
==== Front
Comput Softw Big Sci
Comput Softw Big Sci
Computing and Software for Big Science
2510-2036
2510-2044
Springer International Publishing Cham

124
10.1007/s41781-024-00124-1
Research
Portable Acceleration of CMS Computing Workflows with Coprocessors as a Service
The CMS Collaborationcms-publication-committee-chair@cern.ch

290
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1 https://ror.org/00ad27c73 grid.48507.3e 0000 0004 0482 7128 Yerevan Physics Institute, Yerevan, Armenia
2 https://ror.org/039shy520 grid.450258.e 0000 0004 0625 7405 Institut für Hochenergiephysik, Vienna, Austria
3 https://ror.org/008x57b05 grid.5284.b 0000 0001 0790 3681 Universiteit Antwerpen, Antwerpen, Belgium
4 https://ror.org/006e5kg04 grid.8767.e 0000 0001 2290 8069 Vrije Universiteit Brussel, Brussel, Belgium
5 https://ror.org/01r9htc13 grid.4989.c 0000 0001 2348 6355 Université Libre de Bruxelles, Bruxelles, Belgium
6 https://ror.org/00cv9y106 grid.5342.0 0000 0001 2069 7798 Ghent University, Ghent, Belgium
7 https://ror.org/02495e989 grid.7942.8 0000 0001 2294 713X Université Catholique de Louvain, Louvain-la-Neuve, Belgium
8 https://ror.org/02wnmk332 grid.418228.5 0000 0004 0643 8134 Centro Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil
9 https://ror.org/0198v2949 grid.412211.5 0000 0004 4687 5267 Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
10 grid.412368.a 0000 0004 0643 8839 Universidade Estadual Paulista, Universidade Federal do ABC, São Paulo, Brazil
11 grid.410344.6 0000 0001 2097 3094 Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy of Sciences, Sofia, Bulgaria
12 https://ror.org/02jv3k292 grid.11355.33 0000 0001 2192 3275 University of Sofia, Sofia, Bulgaria
13 https://ror.org/04xe01d27 grid.412182.c 0000 0001 2179 0636 Instituto De Alta Investigación, Universidad de Tarapacá, Casilla 7 D, Arica, Chile
14 https://ror.org/00wk2mp56 grid.64939.31 0000 0000 9999 1211 Beihang University, Beijing, China
15 https://ror.org/03cve4549 grid.12527.33 0000 0001 0662 3178 Department of Physics, Tsinghua University, Beijing, China
16 https://ror.org/03v8tnc06 grid.418741.f 0000 0004 0632 3097 Institute of High Energy Physics, Beijing, China
17 grid.11135.37 0000 0001 2256 9319 State Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing, China
18 https://ror.org/0064kty71 grid.12981.33 0000 0001 2360 039X Sun Yat-sen University, Guangzhou, China
19 https://ror.org/04c4dkn09 grid.59053.3a 0000 0001 2167 9639 University of Science and Technology of China, Hefei, China
20 https://ror.org/036trcv74 grid.260474.3 0000 0001 0089 5711 Nanjing Normal University, Nanjing, China
21 grid.8547.e 0000 0001 0125 2443 Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) - Fudan University, Shanghai, China
22 https://ror.org/00a2xv884 grid.13402.34 0000 0004 1759 700X Zhejiang University, Hangzhou, Zhejiang, China
23 https://ror.org/02mhbdp94 grid.7247.6 0000 0004 1937 0714 Universidad de Los Andes, Bogota, Colombia
24 https://ror.org/03bp5hc83 grid.412881.6 0000 0000 8882 5269 Universidad de Antioquia, Medellin, Colombia
25 https://ror.org/00m31ft63 grid.38603.3e 0000 0004 0644 1675 University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, Split, Croatia
26 https://ror.org/00m31ft63 grid.38603.3e 0000 0004 0644 1675 Faculty of Science, University of Split, Split, Croatia
27 https://ror.org/02mw21745 grid.4905.8 0000 0004 0635 7705 Institute Rudjer Boskovic, Zagreb, Croatia
28 https://ror.org/02qjrjx09 grid.6603.3 0000 0001 2116 7908 University of Cyprus, Nicosia, Cyprus
29 https://ror.org/024d6js02 grid.4491.8 0000 0004 1937 116X Charles University, Prague, Czech Republic
30 https://ror.org/01gb99w41 grid.440857.a 0000 0004 0485 2489 Escuela Politecnica Nacional, Quito, Ecuador
31 https://ror.org/01r2c3v86 grid.412251.1 0000 0000 9008 4711 Universidad San Francisco de Quito, Quito, Ecuador
32 grid.423564.2 0000 0001 2165 2866 Academy of Scientific Research and Technology of the Arab Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
33 https://ror.org/023gzwx10 grid.411170.2 0000 0004 0412 4537 Center for High Energy Physics (CHEP-FU), Fayoum University, El-Fayoum, Egypt
34 https://ror.org/03eqd4a41 grid.177284.f 0000 0004 0410 6208 National Institute of Chemical Physics and Biophysics, Tallinn, Estonia
35 https://ror.org/040af2s02 grid.7737.4 0000 0004 0410 2071 Department of Physics, University of Helsinki, Helsinki, Finland
36 https://ror.org/01x2x1522 grid.470106.4 0000 0001 1106 2387 Helsinki Institute of Physics, Helsinki, Finland
37 https://ror.org/0208vgz68 grid.12332.31 0000 0001 0533 3048 Lappeenranta-Lahti University of Technology, Lappeenranta, Finland
38 https://ror.org/03xjwb503 grid.460789.4 0000 0004 4910 6535 IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
39 grid.508893.f Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau, France
40 https://ror.org/00pg6eq24 grid.11843.3f 0000 0001 2157 9291 Université de Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, France
41 https://ror.org/02avf8f85 Institut de Physique des 2 Infinis de Lyon (IP2I ), Villeurbanne, France
42 https://ror.org/00aamz256 grid.41405.34 0000 0001 0702 1187 Georgian Technical University, Tbilisi, Georgia
43 https://ror.org/04xfq0f34 grid.1957.a 0000 0001 0728 696X RWTH Aachen University, I. Physikalisches Institut, Aachen, Germany
44 https://ror.org/04xfq0f34 grid.1957.a 0000 0001 0728 696X RWTH Aachen University, III. Physikalisches Institut A, Aachen, Germany
45 https://ror.org/04xfq0f34 grid.1957.a 0000 0001 0728 696X RWTH Aachen University, III. Physikalisches Institut B, Aachen, Germany
46 https://ror.org/01js2sh04 grid.7683.a 0000 0004 0492 0453 Deutsches Elektronen-Synchrotron, Hamburg, Germany
47 https://ror.org/00g30e956 grid.9026.d 0000 0001 2287 2617 University of Hamburg, Hamburg, Germany
48 https://ror.org/04t3en479 grid.7892.4 0000 0001 0075 5874 Karlsruher Institut fuer Technologie, Karlsruhe, Germany
49 grid.6083.d 0000 0004 0635 6999 Institute of Nuclear and Particle Physics (INPP), NCSR Demokritos, Aghia Paraskevi, Greece
50 https://ror.org/04gnjpq42 grid.5216.0 0000 0001 2155 0800 National and Kapodistrian University of Athens, Athens, Greece
51 grid.4241.3 0000 0001 2185 9808 National Technical University of Athens, Athens, Greece
52 https://ror.org/01qg3j183 grid.9594.1 0000 0001 2108 7481 University of Ioánnina, Ioánnina, Greece
53 grid.419766.b 0000 0004 1759 8344 HUN-REN Wigner Research Centre for Physics, Budapest, Hungary
54 https://ror.org/01jsq2704 grid.5591.8 0000 0001 2294 6276 MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös Loránd University, Budapest, Hungary
55 https://ror.org/02xf66n48 grid.7122.6 0000 0001 1088 8582 Faculty of Informatics, University of Debrecen, Debrecen, Hungary
56 grid.418861.2 0000 0001 0674 7808 Institute of Nuclear Research ATOMKI, Debrecen, Hungary
57 Karoly Robert Campus, MATE Institute of Technology, Gyongyos, Hungary
58 https://ror.org/04p2sbk06 grid.261674.0 0000 0001 2174 5640 Panjab University, Chandigarh, India
59 https://ror.org/04gzb2213 grid.8195.5 0000 0001 2109 4999 University of Delhi, Delhi, India
60 https://ror.org/0491yz035 grid.473481.d 0000 0001 0661 8707 Saha Institute of Nuclear Physics, HBNI, Kolkata, India
61 https://ror.org/03v0r5n49 grid.417969.4 0000 0001 2315 1926 Indian Institute of Technology Madras, Madras, India
62 https://ror.org/03ht1xw27 grid.22401.35 0000 0004 0502 9283 Tata Institute of Fundamental Research-A, Mumbai, India
63 https://ror.org/03ht1xw27 grid.22401.35 0000 0004 0502 9283 Tata Institute of Fundamental Research-B, Mumbai, India
64 https://ror.org/02r2k1c68 grid.419643.d 0000 0004 1764 227X National Institute of Science Education and Research, An OCC of Homi Bhabha National Institute, Bhubaneswar, Odisha India
65 https://ror.org/028qa3n13 grid.417959.7 0000 0004 1764 2413 Indian Institute of Science Education and Research (IISER), Pune, India
66 grid.411751.7 0000 0000 9908 3264 Isfahan University of Technology, Isfahan, Iran
67 https://ror.org/04xreqs31 grid.418744.a 0000 0000 8841 7951 Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
68 https://ror.org/05m7pjf47 grid.7886.1 0000 0001 0768 2743 University College Dublin, Dublin, Ireland
69 grid.4466.0 0000 0001 0578 5482 INFN Sezione di Bari, Università di Bari, Politecnico di Bari, Bari, Italy
70 grid.6292.f 0000 0004 1757 1758 INFN Sezione di Bologna, Università di Bologna, Bologna, Italy
71 grid.8158.4 0000 0004 1757 1969 INFN Sezione di Catania, Università di Catania, Catania, Italy
72 https://ror.org/02vv5y108 grid.470204.5 0000 0001 2231 4148 INFN Sezione di Firenze, Università di Firenze, Firenze, Italy
73 https://ror.org/049jf1a25 grid.463190.9 0000 0004 0648 0236 INFN Laboratori Nazionali di Frascati, Frascati, Italy
74 grid.5606.5 0000 0001 2151 3065 INFN Sezione di Genova, Università di Genova, Genova, Italy
75 https://ror.org/03xejxm22 grid.470207.6 0000 0004 8390 4143 INFN Sezione di Milano-Bicocca, Università di Milano-Bicocca, Milano, Italy
76 grid.508348.2 INFN Sezione di Napoli, Università di Napoli ’Federico II’, Napoli, Italy; Università della Basilicata, Potenza, Italy; Scuola Superiore Meridionale (SSM), Napoli, Italy
77 grid.11696.39 0000 0004 1937 0351 INFN Sezione di Padova, Università di Padova, Padova, Italy; Università di Trento, Trento, Italy
78 grid.8982.b 0000 0004 1762 5736 INFN Sezione di Pavia, Università di Pavia, Pavia, Italy
79 grid.9027.c 0000 0004 1757 3630 INFN Sezione di Perugia, Università di Perugia, Perugia, Italy
80 grid.9024.f 0000 0004 1757 4641 INFN Sezione di Pisa, Università di Pisa, Scuola Normale Superiore di Pisa, Pisa, Italy; Università di Siena, Siena, Italy
81 grid.7841.a INFN Sezione di Roma, Sapienza Università di Roma, Roma, Italy
82 https://ror.org/01vj6ck58 grid.470222.1 0000 0004 7471 9712 INFN Sezione di Torino, Università di Torino, Torino, Italy; Università del Piemonte Orientale, Novara, Italy
83 grid.5133.4 0000 0001 1941 4308 INFN Sezione di Trieste, Università di Trieste, Trieste, Italy
84 https://ror.org/040c17130 grid.258803.4 0000 0001 0661 1556 Kyungpook National University, Daegu, Korea
85 grid.411733.3 0000 0004 0532 811X Department of Mathematics and Physics, GWNU, Gangneung, Korea
86 https://ror.org/05kzjxq56 grid.14005.30 0000 0001 0356 9399 Chonnam National University, Institute for Universe and Elementary Particles, Kwangju, Korea
87 https://ror.org/046865y68 grid.49606.3d 0000 0001 1364 9317 Hanyang University, Seoul, Korea
88 https://ror.org/047dqcg40 grid.222754.4 0000 0001 0840 2678 Korea University, Seoul, Korea
89 https://ror.org/01zqcg218 grid.289247.2 0000 0001 2171 7818 Department of Physics, Kyung Hee University, Seoul, Korea
90 https://ror.org/00aft1q37 grid.263333.4 0000 0001 0727 6358 Sejong University, Seoul, Korea
91 https://ror.org/04h9pn542 grid.31501.36 0000 0004 0470 5905 Seoul National University, Seoul, Korea
92 https://ror.org/05en5nh73 grid.267134.5 0000 0000 8597 6969 University of Seoul, Seoul, Korea
93 https://ror.org/01wjejq96 grid.15444.30 0000 0004 0470 5454 Department of Physics, Yonsei University, Seoul, Korea
94 https://ror.org/04q78tk20 grid.264381.a 0000 0001 2181 989X Sungkyunkwan University, Suwon, Korea
95 https://ror.org/02gqgne03 grid.472279.d 0000 0004 0418 1945 College of Engineering and Technology, American University of the Middle East (AUM), Dasman, Kuwait
96 https://ror.org/00twb6c09 grid.6973.b 0000 0004 0567 9729 Riga Technical University, Riga, Latvia
97 https://ror.org/05g3mes96 grid.9845.0 0000 0001 0775 3222 University of Latvia (LU), Riga, Latvia
98 https://ror.org/03nadee84 grid.6441.7 0000 0001 2243 2806 Vilnius University, Vilnius, Lithuania
99 https://ror.org/00rzspn62 grid.10347.31 0000 0001 2308 5949 National Centre for Particle Physics, Universiti Malaya, Kuala Lumpur, Malaysia
100 grid.11893.32 0000 0001 2193 1646 Universidad de Sonora (UNISON), Hermosillo, Mexico
101 grid.512574.0 Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico
102 https://ror.org/05vss7635 grid.441047.2 0000 0001 2156 4794 Universidad Iberoamericana, Mexico City, Mexico
103 https://ror.org/03p2z7827 grid.411659.e 0000 0001 2112 2750 Benemerita Universidad Autonoma de Puebla, Puebla, Mexico
104 https://ror.org/02drrjp49 grid.12316.37 0000 0001 2182 0188 University of Montenegro, Podgorica, Montenegro
105 https://ror.org/03y7q9t39 grid.21006.35 0000 0001 2179 4063 University of Canterbury, Christchurch, New Zealand
106 grid.412621.2 0000 0001 2215 1297 National Centre for Physics, Quaid-I-Azam University, Islamabad, Pakistan
107 grid.9922.0 0000 0000 9174 1488 AGH University of Krakow, Faculty of Computer Science, Electronics and Telecommunications, Krakow, Poland
108 https://ror.org/00nzsxq20 grid.450295.f 0000 0001 0941 0848 National Centre for Nuclear Research, Swierk, Poland
109 https://ror.org/039bjqg32 grid.12847.38 0000 0004 1937 1290 Institute of Experimental Physics, Faculty of Physics, University of Warsaw, Warsaw, Poland
110 grid.1035.7 0000000099214842 Warsaw University of Technology, Warsaw, Poland
111 https://ror.org/01hys1667 grid.420929.4 Laboratório de Instrumentação e Física Experimental de Partículas, Lisboa, Portugal
112 https://ror.org/02qsmb048 grid.7149.b 0000 0001 2166 9385 Faculty of Physics, University of Belgrade, Belgrade, Serbia
113 grid.7149.b 0000 0001 2166 9385 VINCA Institute of Nuclear Sciences, University of Belgrade, Belgrade, Serbia
114 https://ror.org/05xx77y52 grid.420019.e 0000 0001 1959 5823 Centro de Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT), Madrid, Spain
115 https://ror.org/01cby8j38 grid.5515.4 0000 0001 1957 8126 Universidad Autónoma de Madrid, Madrid, Spain
116 https://ror.org/006gksa02 grid.10863.3c 0000 0001 2164 6351 Universidad de Oviedo, Instituto Universitario de Ciencias y Tecnologías Espaciales de Asturias (ICTEA), Oviedo, Spain
117 grid.7821.c 0000 0004 1770 272X Instituto de Física de Cantabria (IFCA), CSIC-Universidad de Cantabria, Santander, Spain
118 https://ror.org/02phn5242 grid.8065.b 0000 0001 2182 8067 University of Colombo, Colombo, Sri Lanka
119 https://ror.org/033jvzr14 grid.412759.c 0000 0001 0103 6011 Department of Physics, University of Ruhuna, Matara, Sri Lanka
120 https://ror.org/01ggx4157 grid.9132.9 0000 0001 2156 142X CERN, European Organization for Nuclear Research, Geneva, Switzerland
121 https://ror.org/03eh3y714 grid.5991.4 0000 0001 1090 7501 Paul Scherrer Institut, Villigen, Switzerland
122 https://ror.org/05a28rw58 grid.5801.c 0000 0001 2156 2780 ETH Zurich, Institute for Particle Physics and Astrophysics (IPA), Zurich, Switzerland
123 https://ror.org/02crff812 grid.7400.3 0000 0004 1937 0650 Universität Zürich, Zurich, Switzerland
124 https://ror.org/00944ve71 grid.37589.30 0000 0004 0532 3167 National Central University, Chung-Li, Taiwan
125 https://ror.org/05bqach95 grid.19188.39 0000 0004 0546 0241 National Taiwan University (NTU), Taipei, Taiwan
126 https://ror.org/028wp3y58 grid.7922.e 0000 0001 0244 7875 High Energy Physics Research Unit, Department of Physics, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
127 https://ror.org/05wxkj555 grid.98622.37 0000 0001 2271 3229 Çukurova University, Physics Department, Science and Art Faculty, Adana, Turkey
128 https://ror.org/014weej12 grid.6935.9 0000 0001 1881 7391 Middle East Technical University, Physics Department, Ankara, Turkey
129 https://ror.org/03z9tma90 grid.11220.30 0000 0001 2253 9056 Bogazici University, Istanbul, Turkey
130 https://ror.org/059636586 grid.10516.33 0000 0001 2174 543X Istanbul Technical University, Istanbul, Turkey
131 https://ror.org/03a5qrr21 grid.9601.e 0000 0001 2166 6619 Istanbul University, Istanbul, Turkey
132 https://ror.org/0547yzj13 grid.38575.3c 0000 0001 2337 3561 Yildiz Technical University, Istanbul, Turkey
133 grid.466758.e Institute for Scintillation Materials of National Academy of Science of Ukraine, Kharkiv, Ukraine
134 https://ror.org/00183pc12 grid.425540.2 0000 0000 9526 3153 National Science Centre, Kharkiv Institute of Physics and Technology, Kharkiv, Ukraine
135 https://ror.org/0524sp257 grid.5337.2 0000 0004 1936 7603 University of Bristol, Bristol, UK
136 https://ror.org/03gq8fr08 grid.76978.37 0000 0001 2296 6998 Rutherford Appleton Laboratory, Didcot, UK
137 https://ror.org/041kmwe10 grid.7445.2 0000 0001 2113 8111 Imperial College, London, UK
138 grid.7728.a 0000 0001 0724 6933 Brunel University, Uxbridge, UK
139 https://ror.org/005781934 grid.252890.4 0000 0001 2111 2894 Baylor University, Waco, TX USA
140 https://ror.org/047yk3s18 grid.39936.36 0000 0001 2174 6686 Catholic University of America, Washington, DC USA
141 https://ror.org/03xrrjk67 grid.411015.0 0000 0001 0727 7545 The University of Alabama, Tuscaloosa, AL USA
142 https://ror.org/05qwgg493 grid.189504.1 0000 0004 1936 7558 Boston University, Boston, MA USA
143 https://ror.org/05gq02987 grid.40263.33 0000 0004 1936 9094 Brown University, Providence, RI USA
144 grid.27860.3b 0000 0004 1936 9684 University of California, Davis, CA USA
145 grid.19006.3e 0000 0000 9632 6718 University of California, Los Angeles, CA USA
146 grid.266097.c 0000 0001 2222 1582 University of California, Riverside, CA USA
147 https://ror.org/05t99sp05 grid.468726.9 0000 0004 0486 2046 University of California, San Diego, La Jolla, CA USA
148 grid.133342.4 0000 0004 1936 9676 University of California, Santa Barbara, Department of Physics, Santa Barbara, CA USA
149 https://ror.org/05dxps055 grid.20861.3d 0000 0001 0706 8890 California Institute of Technology, Pasadena, CA USA
150 https://ror.org/05x2bcf33 grid.147455.6 0000 0001 2097 0344 Carnegie Mellon University, Pittsburgh, PA USA
151 https://ror.org/02ttsq026 grid.266190.a 0000 0000 9621 4564 University of Colorado Boulder, Boulder, CO USA
152 https://ror.org/05bnh6r87 grid.5386.8 0000 0004 1936 877X Cornell University, Ithaca, NY USA
153 https://ror.org/020hgte69 grid.417851.e 0000 0001 0675 0679 Fermi National Accelerator Laboratory, Batavia, IL USA
154 https://ror.org/02y3ad647 grid.15276.37 0000 0004 1936 8091 University of Florida, Gainesville, FL USA
155 https://ror.org/05g3dte14 grid.255986.5 0000 0004 0472 0419 Florida State University, Tallahassee, FL USA
156 https://ror.org/04atsbb87 grid.255966.b 0000 0001 2229 7296 Florida Institute of Technology, Melbourne, FL USA
157 https://ror.org/02mpq6x41 grid.185648.6 0000 0001 2175 0319 University of Illinois Chicago, Chicago, USA
158 https://ror.org/036jqmy94 grid.214572.7 0000 0004 1936 8294 The University of Iowa, Iowa, USA
159 https://ror.org/00za53h95 grid.21107.35 0000 0001 2171 9311 Johns Hopkins University, Baltimore, MD USA
160 https://ror.org/001tmjg57 grid.266515.3 0000 0001 2106 0692 The University of Kansas, Lawrence, KS USA
161 https://ror.org/05p1j8758 grid.36567.31 0000 0001 0737 1259 Kansas State University, Manhattan, KS USA
162 https://ror.org/041nk4h53 grid.250008.f 0000 0001 2160 9702 Lawrence Livermore National Laboratory, Livermore, CA USA
163 https://ror.org/047s2c258 grid.164295.d 0000 0001 0941 7177 University of Maryland, College Park, MD USA
164 https://ror.org/042nb2s44 grid.116068.8 0000 0001 2341 2786 Massachusetts Institute of Technology, Cambridge, MA USA
165 https://ror.org/017zqws13 grid.17635.36 0000 0004 1936 8657 University of Minnesota, Minneapolis, MN USA
166 https://ror.org/02teq1165 grid.251313.7 0000 0001 2169 2489 University of Mississippi, Oxford, MS USA
167 https://ror.org/043mer456 grid.24434.35 0000 0004 1937 0060 University of Nebraska-Lincoln, Lincoln, NE USA
168 grid.273335.3 0000 0004 1936 9887 State University of New York at Buffalo, Buffalo, NY USA
169 https://ror.org/04t5xt781 grid.261112.7 0000 0001 2173 3359 Northeastern University, Boston, MA USA
170 https://ror.org/000e0be47 grid.16753.36 0000 0001 2299 3507 Northwestern University, Evanston, IL USA
171 https://ror.org/00mkhxb43 grid.131063.6 0000 0001 2168 0066 University of Notre Dame, Notre Dame, IN USA
172 https://ror.org/00rs6vg23 grid.261331.4 0000 0001 2285 7943 The Ohio State University, Columbus, OH USA
173 https://ror.org/00hx57361 grid.16750.35 0000 0001 2097 5006 Princeton University, Princeton, NJ USA
174 https://ror.org/00wek6x04 grid.267044.3 0000 0004 0398 9176 University of Puerto Rico, Mayaguez, PR USA
175 https://ror.org/02dqehb95 grid.169077.e 0000 0004 1937 2197 Purdue University, West Lafayette, IN USA
176 https://ror.org/04keq6987 grid.504659.b 0000 0000 8864 7239 Purdue University Northwest, Hammond, IN USA
177 https://ror.org/008zs3103 grid.21940.3e 0000 0004 1936 8278 Rice University, Houston, TX USA
178 https://ror.org/022kthw22 grid.16416.34 0000 0004 1936 9174 University of Rochester, Rochester, NY USA
179 https://ror.org/0420db125 grid.134907.8 0000 0001 2166 1519 The Rockefeller University, New York, NY USA
180 https://ror.org/05vt9qd57 grid.430387.b 0000 0004 1936 8796 Rutgers, The State University of New Jersey, Piscataway, NJ USA
181 https://ror.org/020f3ap87 grid.411461.7 0000 0001 2315 1184 University of Tennessee, Knoxville, TN USA
182 https://ror.org/01f5ytq51 grid.264756.4 0000 0004 4687 2082 Texas A &M University, College Station, TX USA
183 grid.264784.b 0000 0001 2186 7496 Texas Tech University, Lubbock, TX USA
184 https://ror.org/02vm5rt34 grid.152326.1 0000 0001 2264 7217 Vanderbilt University, Nashville, TN USA
185 https://ror.org/0153tk833 grid.27755.32 0000 0000 9136 933X University of Virginia, Charlottesville, VA USA
186 https://ror.org/01070mq45 grid.254444.7 0000 0001 1456 7807 Wayne State University, Detroit, MI USA
187 https://ror.org/01y2jtd41 grid.14003.36 0000 0001 2167 3675 University of Wisconsin, Madison, WI USA
188 grid.9132.9 0000 0001 2156 142X Authors affiliated with an institute or an international laboratory covered by a cooperation agreement with CERN, Geneva, Switzerland
189 https://ror.org/00s8vne50 grid.21072.36 0000 0004 0640 687X Yerevan State University, Yerevan, Armenia
190 https://ror.org/04d836q62 grid.5329.d 0000 0004 1937 0669 TU Wien, Vienna, Austria
191 grid.442567.6 0000 0000 9015 5153 Institute of Basic and Applied Sciences, Faculty of Engineering, Arab Academy for Science, Technology and Maritime Transport, Alexandria, Egypt
192 https://ror.org/00cv9y106 grid.5342.0 0000 0001 2069 7798 Ghent University, Ghent, Belgium
193 https://ror.org/04wffgt70 grid.411087.b 0000 0001 0723 2494 Universidade Estadual de Campinas, Campinas, Brazil
194 https://ror.org/041yk2d64 grid.8532.c 0000 0001 2200 7498 Federal University of Rio Grande do Sul, Porto Alegre, Brazil
195 grid.412352.3 0000 0001 2163 5978 UFMS, Nova Andradina, Brazil
196 https://ror.org/036trcv74 grid.260474.3 0000 0001 0089 5711 Nanjing Normal University, Nanjing, China
197 https://ror.org/036jqmy94 grid.214572.7 0000 0004 1936 8294 Now at The University of Iowa, Iowa, USA
198 https://ror.org/05qbk4x57 grid.410726.6 0000 0004 1797 8419 University of Chinese Academy of Sciences, Beijing, China
199 https://ror.org/02egfyg20 grid.464262.0 0000 0001 0318 1175 China Center of Advanced Science and Technology, Beijing, China
200 https://ror.org/05qbk4x57 grid.410726.6 0000 0004 1797 8419 University of Chinese Academy of Sciences, Beijing, China
201 https://ror.org/01g140v14 grid.495581.4 China Spallation Neutron Source, Guangdong, China
202 grid.462338.8 0000 0004 0605 6769 Now at Henan Normal University, Xinxiang, China
203 https://ror.org/01r9htc13 grid.4989.c 0000 0001 2348 6355 Université Libre de Bruxelles, Bruxelles, Belgium
204 grid.9132.9 0000 0001 2156 142X an institute or an international laboratory covered by a cooperation agreement with CERN, Geneva, Switzerland
205 https://ror.org/00h55v928 grid.412093.d 0000 0000 9853 2750 Helwan University, Cairo, Egypt
206 https://ror.org/04w5f4y88 grid.440881.1 0000 0004 0576 5483 Now at Zewail City of Science and Technology, Zewail, Egypt
207 https://ror.org/0066fxv63 grid.440862.c 0000 0004 0377 5514 British University in Egypt, Cairo, Egypt
208 grid.7269.a 0000 0004 0621 1570 Now at Ain Shams University, Cairo, Egypt
209 https://ror.org/02dqehb95 grid.169077.e 0000 0004 1937 2197 Purdue University, West Lafayette, IN USA
210 https://ror.org/04k8k6n84 grid.9156.b 0000 0004 0473 5039 Université de Haute Alsace, Mulhouse, France
211 https://ror.org/03cve4549 grid.12527.33 0000 0001 0662 3178 Department of Physics, Tsinghua University, Beijing, China
212 https://ror.org/051qn8h41 grid.428923.6 0000 0000 9489 2441 Ilia State University, Tbilisi, Georgia
213 https://ror.org/04j5z3x06 grid.412290.c 0000 0000 8024 0602 The University of the State of Amazonas, Manaus, Brazil
214 grid.412176.7 0000 0001 1498 7262 Erzincan Binali Yildirim University, Erzincan, Turkey
215 https://ror.org/00g30e956 grid.9026.d 0000 0001 2287 2617 University of Hamburg, Hamburg, Germany
216 https://ror.org/04xfq0f34 grid.1957.a 0000 0001 0728 696X RWTH Aachen University, III. Physikalisches Institut A, Aachen, Germany
217 grid.411751.7 0000 0000 9908 3264 Isfahan University of Technology, Isfahan, Iran
218 grid.7787.f 0000 0001 2364 5811 Bergische University Wuppertal (BUW), Wuppertal, Germany
219 https://ror.org/02wxx3e24 grid.8842.6 0000 0001 2188 0404 Brandenburg University of Technology, Cottbus, Germany
220 https://ror.org/02nv7yv05 grid.8385.6 0000 0001 2297 375X Forschungszentrum Jülich, Juelich, Germany
221 https://ror.org/01ggx4157 grid.9132.9 0000 0001 2156 142X CERN, European Organization for Nuclear Research, Geneva, Switzerland
222 https://ror.org/02xf66n48 grid.7122.6 0000 0001 1088 8582 Institute of Physics, University of Debrecen, Debrecen, Hungary
223 grid.418861.2 0000 0001 0674 7808 Institute of Nuclear Research ATOMKI, Debrecen, Hungary
224 grid.7399.4 0000 0004 1937 1397 Now at Universitatea Babes-Bolyai - Facultatea de Fizica, Cluj-Napoca, Romania
225 https://ror.org/01jaj8n65 grid.252487.e 0000 0000 8632 679X Physics Department, Faculty of Science, Assiut University, Assiut, Egypt
226 grid.419766.b 0000 0004 1759 8344 HUN-REN Wigner Research Centre for Physics, Budapest, Hungary
227 https://ror.org/02qbzdk74 grid.412577.2 0000 0001 2176 2352 Punjab Agricultural University, Ludhiana, India
228 https://ror.org/02y28sc20 grid.440987.6 0000 0001 2259 7889 University of Visva-Bharati, Santiniketan, India
229 grid.34980.36 0000 0001 0482 5067 Indian Institute of Science (IISc), Bangalore, India
230 https://ror.org/028vtqb15 grid.462084.c 0000 0001 2216 7125 Birla Institute of Technology, Mesra, Mesra, India
231 https://ror.org/04gx72j20 grid.459611.e 0000 0004 1774 3038 IIT Bhubaneswar, Bhubaneswar, India
232 https://ror.org/01741jv66 grid.418915.0 0000 0004 0504 1311 Institute of Physics, Bhubaneswar, India
233 https://ror.org/04a7rxb17 grid.18048.35 0000 0000 9951 5557 University of Hyderabad, Hyderabad, India
234 https://ror.org/01js2sh04 grid.7683.a 0000 0004 0492 0453 Deutsches Elektronen-Synchrotron, Hamburg, Germany
235 https://ror.org/00af3sa43 grid.411751.7 0000 0000 9908 3264 Department of Physics, Isfahan University of Technology, Isfahan, Iran
236 https://ror.org/024c2fq17 grid.412553.4 0000 0001 0740 9747 Sharif University of Technology, Tehran, Iran
237 https://ror.org/04jf6jw55 grid.510412.3 Department of Physics, University of Science and Technology of Mazandaran, Behshahr, Iran
238 https://ror.org/02an8es95 grid.5196.b 0000 0000 9864 2490 Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Bologna, Italy
239 https://ror.org/02wdzfm91 grid.510931.f Centro Siciliano di Fisica Nucleare e di Struttura Della Materia, Catania, Italy
240 https://ror.org/00j0rk173 grid.440899.8 0000 0004 1780 761X Università degli Studi Guglielmo Marconi, Roma, Italy
241 https://ror.org/04swxte59 grid.508348.2 Scuola Superiore Meridionale, Università di Napoli ’Federico II’, Napoli, Italy
242 https://ror.org/020hgte69 grid.417851.e 0000 0001 0675 0679 Fermi National Accelerator Laboratory, Batavia, IL USA
243 grid.472635.1 0000 0004 6476 9521 Consiglio Nazionale delle Ricerche, Istituto Officina dei Materiali, Perugia, Italy
244 https://ror.org/00twb6c09 grid.6973.b 0000 0004 0567 9729 Riga Technical University, Riga, Latvia
245 https://ror.org/00bw8d226 grid.412113.4 0000 0004 1937 1557 Department of Applied Physics, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Malaysia
246 https://ror.org/059ex5q34 grid.418270.8 0000 0004 0428 7635 Consejo Nacional de Ciencia y Tecnología, Mexico City, Mexico
247 grid.443373.4 0000 0001 0438 3334 Trincomalee Campus, Eastern University, Nilaveli, Sri Lanka
248 Saegis Campus, Nugegoda, Sri Lanka
249 https://ror.org/04gnjpq42 grid.5216.0 0000 0001 2155 0800 National and Kapodistrian University of Athens, Athens, Greece
250 https://ror.org/02s376052 grid.5333.6 0000 0001 2183 9049 Ecole Polytechnique Fédérale Lausanne, Lausanne, Switzerland
251 https://ror.org/02crff812 grid.7400.3 0000 0004 1937 0650 Universität Zürich, Zurich, Switzerland
252 https://ror.org/05kdjqf72 grid.475784.d 0000 0000 9532 5705 Stefan Meyer Institute for Subatomic Physics, Vienna, Austria
253 https://ror.org/049nhh297 grid.450330.1 0000 0001 2276 7382 Laboratoire d’Annecy-le-Vieux de Physique des Particules, IN2P3-CNRS, Annecy-le-Vieux, France
254 Near East University, Research Center of Experimental Health Science, Mersin, Turkey
255 https://ror.org/02s82rs08 grid.505922.9 Konya Technical University, Konya, Turkey
256 https://ror.org/017v96566 0000 0004 6412 5697 Izmir Bakircay University, Izmir, Turkey
257 https://ror.org/02s4gkg68 grid.411126.1 0000 0004 0369 5557 Adiyaman University, Adiyaman, Turkey
258 grid.411743.4 0000 0004 0369 8360 Bozok Universitetesi Rektörlügü, Yozgat, Turkey
259 https://ror.org/02kswqa67 grid.16477.33 0000 0001 0668 8422 Marmara University, Istanbul, Turkey
260 https://ror.org/010t24d82 grid.510982.7 Milli Savunma University, Istanbul, Turkey
261 https://ror.org/04v302n28 grid.16487.3c 0000 0000 9216 0511 Kafkas University, Kars, Turkey
262 https://ror.org/054d5vq03 grid.444283.d 0000 0004 0371 5255 Now at stanbul Okan University, Istanbul, Turkey
263 https://ror.org/04kwvgz42 grid.14442.37 0000 0001 2342 7339 Hacettepe University, Ankara, Turkey
264 https://ror.org/03a5qrr21 grid.9601.e 0000 0001 2166 6619 Istanbul University, Cerrahpasa, Faculty of Engineering, Istanbul, Turkey
265 https://ror.org/0547yzj13 grid.38575.3c 0000 0001 2337 3561 Yildiz Technical University, Istanbul, Turkey
266 https://ror.org/006e5kg04 grid.8767.e 0000 0001 2290 8069 Vrije Universiteit Brussel, Brussel, Belgium
267 https://ror.org/01ryk1543 grid.5491.9 0000 0004 1936 9297 School of Physics and Astronomy, University of Southampton, Southampton, UK
268 https://ror.org/0524sp257 grid.5337.2 0000 0004 1936 7603 University of Bristol, Bristol, UK
269 https://ror.org/01v29qb04 grid.8250.f 0000 0000 8700 0572 IPPP Durham University, Durham, UK
270 https://ror.org/02bfwt286 grid.1002.3 0000 0004 1936 7857 Monash University, Faculty of Science, Clayton, Australia
271 grid.7605.4 0000 0001 2336 6580 Università di Torino, Torino, Italy
272 https://ror.org/05wnc7373 grid.446604.4 0000 0004 0583 4952 Bethel University, St. Paul, MN USA
273 https://ror.org/037vvf096 grid.440455.4 0000 0004 1755 486X Karamanoğlu Mehmetbey University, Karaman, Turkey
274 https://ror.org/05dxps055 grid.20861.3d 0000 0001 0706 8890 California Institute of Technology, Pasadena, CA USA
275 https://ror.org/00znex860 grid.265465.6 0000 0001 2296 3025 United States Naval Academy, Annapolis, MD USA
276 https://ror.org/03hx84x94 grid.448543.a 0000 0004 0369 6517 Bingol University, Bingol, Turkey
277 https://ror.org/00aamz256 grid.41405.34 0000 0001 0702 1187 Georgian Technical University, Tbilisi, Georgia
278 https://ror.org/004ah3r71 grid.449244.b 0000 0004 0408 6032 Sinop University, Sinop, Turkey
279 https://ror.org/047g8vk19 grid.411739.9 0000 0001 2331 2603 Erciyes University, Kayseri, Turkey
280 https://ror.org/00d3pnh21 grid.443874.8 0000 0000 9463 5349 Horia Hulubei National Institute of Physics and Nuclear Engineering (IFIN-HH), Bucharest, Romania
281 grid.9132.9 0000 0001 2156 142X Now at an institute or an international laboratory covered by a cooperation agreement with CERN, Geneva, Switzerland
282 https://ror.org/03vb4dm14 grid.412392.f 0000 0004 0413 3978 Texas A &M University at Qatar, Doha, Qatar
283 https://ror.org/040c17130 grid.258803.4 0000 0001 0661 1556 Kyungpook National University, Daegu, Korea
284 grid.9132.9 0000 0001 2156 142X another institute or international laboratory covered by a cooperation agreement with CERN, Geneva, Switzerland
285 https://ror.org/008x57b05 grid.5284.b 0000 0001 0790 3681 Universiteit Antwerpen, Antwerpen, Belgium
286 https://ror.org/00ad27c73 grid.48507.3e 0000 0004 0482 7128 Yerevan Physics Institute, Yerevan, Armenia
287 https://ror.org/04t5xt781 grid.261112.7 0000 0001 2173 3359 Northeastern University, Boston, MA USA
288 https://ror.org/041kmwe10 grid.7445.2 0000 0001 2113 8111 Imperial College, London, UK
289 grid.443859.7 0000 0004 0477 2171 Institute of Nuclear Physics of the Uzbekistan Academy of Sciences, Tashkent, Uzbekistan
290 grid.9132.9 0000 0001 2156 142X CERN, Geneva, Switzerland
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Computing demands for large scientific experiments, such as the CMS experiment at the CERN LHC, will increase dramatically in the next decades. To complement the future performance increases of software running on central processing units (CPUs), explorations of coprocessor usage in data processing hold great potential and interest. Coprocessors are a class of computer processors that supplement CPUs, often improving the execution of certain functions due to architectural design choices. We explore the approach of Services for Optimized Network Inference on Coprocessors (SONIC) and study the deployment of this as-a-service approach in large-scale data processing. In the studies, we take a data processing workflow of the CMS experiment and run the main workflow on CPUs, while offloading several machine learning (ML) inference tasks onto either remote or local coprocessors, specifically graphics processing units (GPUs). With experiments performed at Google Cloud, the Purdue Tier-2 computing center, and combinations of the two, we demonstrate the acceleration of these ML algorithms individually on coprocessors and the corresponding throughput improvement for the entire workflow. This approach can be easily generalized to different types of coprocessors and deployed on local CPUs without decreasing the throughput performance. We emphasize that the SONIC approach enables high coprocessor usage and enables the portability to run workflows on different types of coprocessors.

Keywords

CMS
Offline and computing
Machine learning
SCScience Committee Hayrapetyan A. http://dx.doi.org/10.13039/501100013699 Austrian Federal Ministry of Education, Science and Research BMBWF (Bundesministerium für Bildung, Wissenschaft, Forschung) [name change from BMWFW] Tumasyan A. http://dx.doi.org/10.13039/501100002428 Austrian Science Fund FWF Adam W. http://dx.doi.org/10.13039/501100002661 Belgian Fonds de la Recherche Scientifique FRS - FNRS Andrejkovic J. W. http://dx.doi.org/10.13039/501100003130 Belgian Fonds voor Wetenschappelijk Onderzoek FWO Bergauer T. http://dx.doi.org/10.13039/501100003593 CNPq Conselho Nacional de Desenvolvimento Cientifico e Tecnelógica Chatterjee S. http://dx.doi.org/10.13039/501100002322 CAPES Coordenação de Aperfeiçoamento de Pessoal de Nível Superior Damanakis K. http://dx.doi.org/10.13039/501100004586 FAPERJ Fundação de Amparo à Pesquisa do Estado de Rio de Janeiro Dragicevic M. http://dx.doi.org/10.13039/501100004263 FAPERGS Fundação de Amparo À Pesquisa do Estado do Rio Grande do Sul Hussain P. S. http://dx.doi.org/10.13039/501100001807 FAPESP Fundação de Amparo à Pesquisa do Estado de São Paulo Jeitler M. http://dx.doi.org/10.13039/501100005992 Bulgarian Ministry of Education and Science MES Krammer N. http://dx.doi.org/10.13039/501100003336 Bulgarian National Science Fund BNSF Li A. http://dx.doi.org/10.13039/100012470 CERN CERN [Conseil Européen pour la Recherche Nucléaire] Liko D. http://dx.doi.org/10.13039/501100002367 Chinese Academy of Sciences CAS Mikulec I. http://dx.doi.org/10.13039/501100002855 Ministry of Science and Technology MOST Schieck J. http://dx.doi.org/10.13039/501100001809 Chinese National Natural Science Foundation of China NSFC Schöfbeck R. http://dx.doi.org/10.13039/100007637 Colombian Funding Agency (MINICIENCIAS) Departamento Administriva de Ciencia, Tecnología e Innovación, now Ministerio de Ciencia Tecnología e Innovación Schwarz D. http://dx.doi.org/10.13039/501100006588 Croatian Ministry of Science, Education and Sport Ministarstvo Znanosti, Obrazovanja i Sporta; MSES Sonawane M. http://dx.doi.org/10.13039/501100004488 Croatian Science Foundation Hrvatska Zaklada za Znanost; CSF Templ S. http://dx.doi.org/10.13039/501100001810 Research and Innovation Foundation RIF Waltenberger W. http://dx.doi.org/10.13039/501100004299 SENESCYT Secretaría de Educación Superior, Ciencia, Tecnología e Innovación Wulz C.-E. http://dx.doi.org/10.13039/501100002301 Estonian Research Council via PRG780, PRG803, RVTT3 and TK202 Eesti Teadusagentuur; ERC IUT Darwish M. R. http://dx.doi.org/10.13039/501100002341 Academy of Finland Suomen Akatemia Janssen T. Finnish Ministry of Education and CultureMEC Mechelen P. Van Helsinki Institute of PhysicsHIP Bols E. S. http://dx.doi.org/10.13039/501100004794 Institut National de Physique Nucléaire et de Physique des Particules IN2P3 D’Hondt J. http://dx.doi.org/10.13039/501100004794 Centre National de la Recherche Scientifique CNRS Dansana S. http://dx.doi.org/10.13039/501100006489 Commissariat à l’Énergie Atomique et aux Énergies Alternatives CEA De Moor A. http://dx.doi.org/10.13039/501100004801 Shota Rustaveli National Science Foundation SRNSF Delcourt M. http://dx.doi.org/10.13039/501100002347 Bundesministerium für Bildung und Forschung BMBF Faham H. El http://dx.doi.org/10.13039/501100001659 Deutsche Forschungsgemeinschaft DFG Lowette S. http://dx.doi.org/10.13039/501100001656 Helmholtz-Gemeinschaft Deutscher Forschungszentren HGF Makarenko I. http://dx.doi.org/10.13039/501100003448 General Secretariat for Research and Innovation GSRI Müller D. http://dx.doi.org/10.13039/501100011019 National Research, Development and Innovation Office NKFIH: Nemzeti Kutatási, Fejlesztési és Innováció Hivatal Sahasransu A. R. http://dx.doi.org/10.13039/501100001502 Department of Atomic Energy DAE Tavernier S. http://dx.doi.org/10.13039/501100001409 Department of Science and Technology DST Tytgat M. ICSC -National Research Centre for High Performance Computing, Big Data and Quantum Computing, funded by the EU NexGeneration programhttp://dx.doi.org/10.13039/501100006115 Institute for Research in Fundamental Studies IPM Putte S. Van http://dx.doi.org/10.13039/501100001602 Science Foundation SFI Vannerom D. http://dx.doi.org/10.13039/501100004007 Istituto Nazionale di Fisica Nucleare INFN Clerbaux B. http://dx.doi.org/10.13039/501100004085 Korean Ministry of Education, Science and Technology previously listed was MSIP, Ministry of Science, ICT, and Future Planning Das A. K. http://dx.doi.org/10.13039/501100003725 National Research Foundation of Korea (NRF) previously listed was WCU (World Class University) De Lentdecker G. http://dx.doi.org/10.13039/501100001864 MES Ministry of Education and Science Favart L. http://dx.doi.org/10.13039/501100008523 Lithuanian Academy of Sciences LAS or Lietuvos Mokslu Akademija Gianneios P. http://dx.doi.org/10.13039/501100003093 Ministry of Education ?Ministry of Higher Education, Malaysia Hohov D. http://dx.doi.org/10.13039/501100004386 University of Malaya http://dx.doi.org/10.13039/501100006348 BUAP Benemérita Universidad Autónoma de Puebla Khalilzadeh A. http://dx.doi.org/10.13039/501100008688 CINVESTAV Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional Khan F. A. http://dx.doi.org/10.13039/501100003141 CONACYT Consejo Nacional de Ciencia y Tecnología Lee K. LNSLaboratorio Nacional de Supercómputo del Sureste Mahdavikhorrami M. http://dx.doi.org/10.13039/100010096 SEP Secretaría de Educación Pública Malara A. http://dx.doi.org/10.13039/501100005324 UASLP Universidad Autónoma de San Luis Potosí Paredes S. MOSMinistry of Science Thomas L. http://dx.doi.org/10.13039/501100003524 Ministry of Business, Innovation and Employment MBIE Bemden M. Vanden http://dx.doi.org/10.13039/501100008689 Pakistan Atomic Energy Commission PAEC Velde C. Vander http://dx.doi.org/10.13039/501100004569 Ministry of Educaton and Science Ministerstwo Edukacji I Nauki; MES [was MSHE, Ministerstwo Nauki i Szkolnictwa Wyzszego] Vanlaer P. http://dx.doi.org/10.13039/501100004442 National Science Centre Narodowe Centrum Nauki; NSC De Coen M. http://dx.doi.org/10.13039/501100001871 Fundação para a Ciência e a Tecnologia, CERN/FIS-PAR/0025/2019 and CERN/FIS-INS/0032/2019 FCT Dobur D. http://dx.doi.org/10.13039/501100003822 JINR, Dubna Joint Institute for Nuclear Research Hong Y. http://dx.doi.org/10.13039/501100003443 Ministry of Education and Science of the Russian Federation MON Knolle J. http://dx.doi.org/10.13039/501100008687 Federal Agency of Atomic Energy of the Russian Federation RosAtom Lambrecht L. http://dx.doi.org/10.13039/501100002674 Russian Academy of Sciences RAS Mestdach G. http://dx.doi.org/10.13039/501100002261 Russian Foundation for Basic Research RFBR Amarilo K. Mota http://dx.doi.org/10.13039/501100013400 National Research Center “Kurchatov Institute" NRC KI Rendón C. http://dx.doi.org/10.13039/501100004564 Ministry of Education, Science and Technological Development of Serbia MESTD Samalan A. http://dx.doi.org/10.13039/501100011033 MCIN/AEI/10.13039/501100011033, ERDF ”a way of making Europe” MCIN/AEI (Ministerio de Ciencia e Innovación/Agencia Estatal de Investigación) Skovpen K. http://dx.doi.org/10.13039/501100008530 Fondo Europeo de Desarrollo Regional, Spain FEDER (ERDF) Bossche N. Van Den https://doi.org/10.13039/100011941 Plan de Ciencia, Tecnología e Innovación del Principado de Asturias PCTI Linden J. van der http://dx.doi.org/10.13039/501100008981 MOSTR Ministry of Science, Technology, and Research Wezenbeek L. ETH BoardEidgenössische Technische Hochschule (ETH) Zürich Benecke A. http://dx.doi.org/10.13039/501100003006 ETH Zurich Eidgenössische Technische Hochschule (ETH) Zürich Bethani A. http://dx.doi.org/10.13039/501100004219 PSI Paul Scherrer Institut Bruno G. http://dx.doi.org/10.13039/501100001711 SNF Swiss National Science Foundation (Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung) Caputo C. UniZHUniversität Zürich Delaere C. Canton ZurichSERState Secretariat for Education and Research (Education, Research, and Innovation: SERI) Giammanco A. http://dx.doi.org/10.13039/501100004663 Ministry of Science and Technology http://dx.doi.org/10.13039/501100016204 Ministry of Higher Education, Science, Research and Innovation MHESI Jain Sa. http://dx.doi.org/10.13039/501100004192 National Science and Technology Development Agency of Thailand NSTDA Lemaitre V. http://dx.doi.org/10.13039/501100004410 Scientific and Technical Research Council of Turkey Türkiye Bilimsel ve Teknolojik Arastirma Kurumu Lidrych J. http://dx.doi.org/10.13039/100010440 Turkish Atomic Energy Authority Türkiye Atom Enerjisi Kurumu Mastrapasqua P. http://dx.doi.org/10.13039/501100004742 National Academy of Sciences of Ukraine http://dx.doi.org/10.13039/501100000271 Science and Technology Facilities Council http://dx.doi.org/10.13039/100000015 US Department of Energy http://dx.doi.org/10.13039/100000001 US National Science Foundation Marie-Curie programmehttp://dx.doi.org/10.13039/501100000781 European Research Council and EPLANET (European Union) ERC doi only Tumasyan A. http://dx.doi.org/10.13039/501100000921 European Research Council/European Cooperation in Science and Technology), Action CA16108 COST Adam W. http://dx.doi.org/10.13039/100010661 Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, 758316, 765710, 824093 (European Union) http://dx.doi.org/10.13039/501100004117 Leventis Foundation http://dx.doi.org/10.13039/100000879 Alfred P. Sloan Foundation http://dx.doi.org/10.13039/100005156 Alexander von Humboldt Foundation Alexander von Humboldt-Stiftung Damanakis K. Science Committee, project no. 22rl-037Belgian Federal Science Policy Officehttp://dx.doi.org/10.13039/501100003134 Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture (FRIA-Belgium) http://dx.doi.org/10.13039/501100003132 Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium) http://dx.doi.org/10.13039/501100002661 Belgian Fonds de la Recherche Scientifique, "Excellence of Science - EOS" - be.h project n. 30820817 FRS - FNRS Li A. http://dx.doi.org/10.13039/501100003130 Belgian Fonds voor Wetenschappelijk Onderzoek, "Excellence of Science - EOS" - be.h project n. 30820817 FWO Liko D. http://dx.doi.org/10.13039/501100009592 Beijing Municipal Science & Technology Commission, No. Z191100007219010 https://dx.doi.org/10.13039/501100012226 Fundamental Research Funds for the Central Universities http://dx.doi.org/10.13039/501100001823 Ministry of Education, Youth and Sports (MEYS) of the Czech Republic Ministerstvo Školství, Mládeže a Telovýchovy SRNSF Schöfbeck R. Schwarz D. http://dx.doi.org/10.13039/501100001659 Deutsche Forschungsgemeinschaft (DFG) under Germany’s Excellence Strategy - EXC 2121 ”Quantum Universe” – 390833306 http://dx.doi.org/10.13039/501100001659 Deutsche Forschungsgemeinschaft (DFG), project number 400140256 - GRK2497 http://dx.doi.org/10.13039/501100013209 Hellenic Foundation for Research and Innovation, Project Number 2288 HFRI Waltenberger W. http://dx.doi.org/10.13039/501100003825 Hungarian Academy of Sciences Magyar Tudományos Akadémia Wulz C.-E. http://dx.doi.org/10.13039/501100011019 New National Excellence Program - ÚNKP, the NKFIH research grants K 124845, K 124850, K 128713, K 128786, K 129058, K 131991, K 133046, K 138136, K 143460, K 143477, 2020-2.2.1-ED-2021-00181, and TKP2021-NKTA-64 ÚNKP: Új Nemzeti Kiválósági Program Darwish M. R. Council of Scientific and Industrial Research, Indiahttp://dx.doi.org/10.13039/501100005375 Latvian Council of Science Latvijas Zinatnes Padome Mechelen P. Van http://dx.doi.org/10.13039/501100004569 Ministy of Education and Science, project no. 2022/WK/14 Ministerstwo Edukacji I Nauki; MES Bols E. S. http://dx.doi.org/10.13039/501100004281 National Science Center, Opus 2021/41/B/ST2/01369 and 2021/43/B/ST2/01552 Narodowe Centrum Nauki D’Hondt J. http://dx.doi.org/10.13039/501100001871 Fundação para a Ciência e a Tecnologia, CEECIND/01334/2018 http://dx.doi.org/10.13039/100008982 National Priorities Research Program by Qatar National Research Fund http://dx.doi.org/10.13039/501100003443 Ministry of Science and Higher Education, project no. 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pmcIntroduction

During the first two runs of the CERN LHC [1], the ATLAS [2] and CMS [3] Collaborations have analyzed trillions of high-energy proton–proton or lead–lead collisions and produced an extensive suite of physics results. Among these are the discovery of the Higgs boson [4–6] in the standard model (SM) and stringent constraints on various beyond the SM physics scenarios, such as supersymmetry [7–14] and exotic heavy-particle or dark matter candidate production [15–21]. In order to measure the SM with higher precision and search for new processes with lower cross-sections, the amount of data that is delivered by the LHC and processed by the experiments is expected to increase dramatically in ongoing and future physics runs [22, 23].

The high data-taking rates and increasing event complexity of the ATLAS and CMS experiments present a significant computational challenge for data processing [24, 25]. A two-level trigger system is employed to run fast algorithms and reduce the data rate from 40 TB/s to about 10 GB/s [26–28]. While this is a significantly smaller rate, it is still very challenging for subsequent processing steps. As discussed in Refs. [29–31], even with optimistic expectations for computing research and development, the projected computing needs for CMS will be only narrowly satisfied.

At present, data processing is mainly carried out using central processing units (CPUs) but their expected performance increase is limited [32]. Nevertheless, data processing can be supported with a variety of modern architectures, such as graphics processing units (GPUs), field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), or Graphcore intelligence processing units (IPUs) [33, 34], which can collectively be referred to as coprocessors. These architectures are becoming increasingly popular because of their large numbers of processing units, inherent parallelization designs, and more energy-efficient and environmentally friendly computing, especially suited for machine learning (ML) algorithm computations.

Within high-energy physics (HEP), deep-learning (DL) algorithms are already widely used for regression and classification tasks, and their popularity is growing rapidly [35–39]. Because of this growth, inference execution for these algorithms consumes increasingly large fractions of the overall processing load. However, the processing load can be shared and accelerated by using heterogeneous computing architectures. Therefore, developing a framework to enable and optimize the deployment and portability of coprocessors is of considerable interest to HEP experiments [40, 41]. As a proof of concept, this paper focuses on accelerating the inference for ML algorithms in one of the CMS data processing stages, explained in “The CMS Data Tiers“ section. These algorithms collectively take about 10% of the total processing time of that stage. For this approach to resolve the future computing challenge, ML algorithms would need to be used for larger portions of CMS data processing. Utilizing ML for tasks such as tracking [42–45], clustering [46–48], particle reconstruction [49–53], and particle identification [54–60] is an active area of research.

One of the approaches for the coprocessor deployment is to equip every CPU machine with coprocessors, referred to as directly connected. In this scenario, every CPU thread within the machine can communicate with the coprocessor. However, since the coprocessor-to-CPU ratio must be determined before deployment, the coprocessor resources are unlikely to be optimally utilized, leading to either unused resources when undersaturating, or decreased performance when oversaturating. In addition, it is difficult to utilize additional or more advanced coprocessor resources after deployment.

An alternative approach is inference as a service (IaaS), where coprocessor resources are separated from CPU machines. As illustrated in Fig. 1, in this scheme CPU-based clients can send the computing request with the necessary information to coprocessor-based servers via network calls. The servers running on coprocessor resources can perform computing tasks upon request. This removes the restriction of a coprocessor only being used by the CPUs directly connected to it, allowing it to accept processing requests from any CPUs (local or remote), as long as network communications are available. Certain types of coprocessors can be allocated for specific tasks, and the coprocessor-to-CPU ratio is flexible. Resource utilization can, therefore, be optimized based on specific tasks, as the number of client-side jobs using a single server can be varied depending on the computational demands of a given task. Furthermore, at the software level, since the support for coprocessors and CPU workflows is separated, it is easier to support different types of coprocessors. This ensures algorithm portability with minimal maintenance burden. The implementation of IaaS in experimental software frameworks can be accomplished using the Services for Optimized Network Inference on Coprocessors (SONIC) approach [61], as described in “ The SONIC Approach“ section.Fig. 1 An example inference as a service setup with multiple coprocessor servers. Clients usually run on CPUs, shown on the left side; servers hosting different models run on coprocessors, shown on the right side

The SONIC approach has previously been demonstrated using FPGAs [61, 62] and GPUs [63–66] with a variety of ML algorithms. These studies demonstrated that offloading ML algorithms with the SONIC approach adds little additional computational overhead from the client–server communications and other operations. Therefore, a corresponding increase in the throughput has been observed when offloading algorithms to these faster coprocessors.

Heterogeneous computing frameworks using GPUs have appeared recently in multiple non-IaaS contexts in HEP as well. One of the first significant real-time applications was in the ALICE high-level trigger (HLT) system [67], where GPUs were used to accelerate track reconstruction. Similarly, a fully GPU-based first-level trigger has been implemented in LHCb [68], which runs on 200 GPUs [69]. In CMS, a GPU-specific version of the pixel tracking domain (non-ML) algorithm called Patatrack was developed, along with several other reconstruction algorithms, which are now employed in the HLT for Run 3 to reduce the processing time per event [70, 71]. Further review of GPU usage in real-time applications for HEP can be found in Ref. [72].

In this paper, we take one data production workflow, called the Mini-AOD production workflow [73], as an example, and study the performance gains of applying the IaaS framework to this workflow. The abbreviation “AOD” comes from a lower-level data format called Analysis Object Data (AOD) discussed in The CMS Detector, Software, and Computing“ section. In the current Mini-AOD production workflow, which is a data refinement and slimming step, about 10% of the computing time is consumed by ML algorithm inference, which can be easily accelerated on GPUs. We first summarize studies of the optimization and acceleration of the inference of each ML algorithm on GPUs. Then we show that the IaaS scheme, which is implemented in the CMS software framework cmssw [74, 75] via the SONIC approach, not only decreases processing time but can also be applied in large-scale production to optimize GPU utilization. Finally, we show that the SONIC approach can be easily ported to different types of coprocessors such as IPUs, and it can also run on local CPUs without decreasing the throughput. The study of power, power savings, and sustainability is also important, but is beyond the scope of this paper.

The paper is organized as follows. “The CMS Detector, Software, and Computing“ section provides a brief overview of the CMS computing architecture and different data tiers for production. “The SONIC Approach“ section discusses the SONIC approach in detail, including the technical implementation in cmssw, the current inference servers, and features of the approach. “ Physics Data Sets, Algorithms, and Benchmark Setup“ section includes the data set used for the studies and the algorithms that can currently use the SONIC approach for inference. “Performance“ and  “Portability“sections provide detailed studies evaluating the computational performance of this approach in the Mini-AOD production workflow. Finally, Summary summarizes the studies and discusses future plans.

The CMS Detector, Software, And Computing

Introduction to the CMS Experiment

The LHC provides countercirculating beams of high-energy protons or heavy ions, such that bunches of particles in these beams can interact with each other in the center of the CMS detector [3] nearly every 25 ns. When particles from the countercirculating beams collide, a large variety of physical processes can occur, which lead to the creation of either fundamental or composite particles. These particles, or their decay products, can then propagate into the CMS detector, which is designed to measure their energy and momentum. In the context of this paper, we will refer to a readout cycle of the detector as an event. The detector itself comprises multiple layers including silicon pixels and strips, crystal electromagnetic calorimeters, sampling hadron calorimeters, and muon spectrometers. Each component of the detector has active elements that create electrical signals when particles interact with the detector. Each discrete signal is called a hit.

Events of interest are selected using a two-tiered trigger system. The first level (L1), composed of custom hardware processors, uses information from the calorimeters and muon detectors to select events at a rate of around 100 kHz within a fixed latency of 4μs [76]. The second level, known as the HLT, consists of a farm of processors running a version of the full event reconstruction software optimized for fast processing, and reduces the event rate to around 1 kHz before data storage [28]. Processing in the trigger system is referred to as online computing, while subsequent processing is known as offline computing.

The cmssw Framework

The cmssw framework is an open-source software framework that is used in triggering, data formatting and processing, simulation, and offline analysis [74, 75]. Components of the framework are used in combination to extract high-level physics information for each event from detector hits. The cmssw framework processes each event with a sequence of algorithms, converting hits from electrical signals to position and energy measurements, linking these measurements into clusters [77] and trajectories [78], combining trajectories and clusters into single-particle representations or jets corresponding to hadronic showers [79]. Additional algorithms in cmssw, e.g., ML-based reconstruction algorithms, can be run to determine quantities such as an overall imbalance of the momentum in the direction perpendicular to the beam or to tag jets as containing or being produced by certain particles.

The cmssw framework uses Intel Threading Building Blocks [80] to enable task-based multithreading. As explained in Ref. [81], this multithreading implementation allows for asynchronous nonblocking calls to external resources, such as a GPU, via ExternalWork. This setup optimizes CPU resource utilization by minimizing downtime; the CPU is allowed to continue executing algorithms that do not require a coprocessor or depend on the results of the coprocessor-dependent algorithm while waiting for the external call to return.

The CMS Data Tiers

The cmssw framework is used both in online and offline contexts within CMS. While the L1 trigger is hardware-based, the HLT is composed of algorithms in cmssw. The framework also contains the algorithms that are used to process the raw data after they have been stored, deriving the higher-level information useful for a wide range of data analyses based on reconstructed objects. The centralized CMS offline data processing flow involves three steps, which are performed both for raw data and simulated data sets. In the first step, an AOD format for every event is derived. This contains high-level information, such as reconstructed particles and jets, but the data size is large. In the second step, a slimmed, higher level Mini-AOD derivation is created [73]. Mini-AOD files are designed to be relatively small and accessible, serving as an intermediate step and the standard foundation for a variety of CMS physics analyses. Finally, in the third step, a further slimmed Nano-AOD format is created that contains only very high-level physics observables [82]. This format is commonly used directly for physics analyses. Data sets are reprocessed regularly to incorporate the latest Monte Carlo event generator tunes [83], calibrations, and algorithm improvements. Table 1 summarizes the average event size of these different data tiers with 2016–2018 (Run 2) data-taking conditions [84].Table 1 Average event size of different CMS data tiers with Run 2 data-taking conditions [73, 82, 85]

Data tier	Event size [kB/event]	
Raw	1000	
AOD	480	
Mini-AOD	35–60	
Nano-AOD	1–2	

In the scope of the studies in this paper, we choose Mini-AOD production as our test case. Mini-AOD files are derived from the AOD data format, reducing the size per event by an order of magnitude. Mini-AOD processing involves a wide variety of algorithms that propagate, filter, and reanalyze the AOD input objects.

The SONIC Approach

This section describes the implementation of the SONIC approach in cmssw and the server technology currently used, which is the NVIDIA Triton Inference Server (Triton) [86]. The benefits of running inference with the SONIC approach along with additional complexity and other implications are also discussed in this section.

Implementation in cmssw

The SONIC approach is implemented in cmssw through the ExternalWork framework component [81] and accesses coprocessor resources on remote servers via gRPC Remote Procedure Calls (gRPCs), which is a cross-platform open-source high-performance remote procedure call framework originally developed by Google [87]. An illustration of this procedure, where client jobs make asynchronous, nonblocking gRPC calls to remote servers, is shown in Fig. 2. Multiple servers can run on multiple coprocessors, with load balancers in between. Asynchronous communication allows client CPUs to process other tasks in parallel while data are transferred between the client and the server and the inference task is processed on the server. An important aspect of this scheme is that the client-side code does not need to be able to run any particular inference packages or frameworks; it simply has to collect the relevant input data for a trained model, communicate that information to the server in the expected format, and handle the output from the server.Fig. 2 The SONIC implementation of IaaS in cmssw. The figure also shows the possibility of an additional load-balancing layer in the SONIC scheme. For example, if multiple coprocessor-enabled machines are used to host servers, a Kubernetes engine can be set up to distribute inference calls across the machines [88]. Image adapted from Ref. [64]

The client-side framework code for the SONIC approach is split into two packages: a core package [89] containing class templates and other common infrastructure for the IaaS approach, and a dedicated package [90] to interact specifically with the Triton server. SONIC modules provide a similar interface to standard cmssw modules. The partitioning into two packages reflects that the SONIC approach can be implemented for multiple server backends. For example, the first implementation [61, 91] used the Microsoft Brainwave service [92], which provides FPGA resources. Another potential backend service is the TensorFlow as a Service framework, which can provide access to TensorFlow-based ML models via the HTTP protocol [93]. This framework was introduced along with the Machine Learning as a Service pipeline for HEP (MLaaS4HEP), which is a streamlined mechanism for training and deploying models from data in ROOT data format files [94]. In practice, given the generality and openness of the protocols used by Triton, which are themselves an extension of the KServe standard [95], we expect that future development of the SONIC approach will continue to use these protocols, even if the backends or coprocessors change. Further discussion of different backends and coprocessors can be found in “Portability“ section.

A central TritonService is provided to keep track of all available servers and which models each of them serves. By default, each client-side module only needs to specify the model it needs, and the TritonService will automatically find a server hosting that model. A client-side module can optionally specify a preferred server, which the TritonService will use if the server is found and confirmed to serve the required model.

Within cmssw, a mechanism has been implemented to account for the possibility that a client job cannot access a specified server for whatever reason. In this case, a “fallback” server is automatically created using either local GPU resources if they are available or the CPU resources allocated to the client job in question. The client then makes gRPC calls to that local fallback server, which introduces negligible latency. Detailed studies related to these fallback servers are discussed later in “The CPU Fallback Server“ section. In general, the server overhead consumes very little of the CPU resources beyond what would be used for conventional inference, such that the per-event processing time is not strongly affected by the SONIC approach relative to running without it. Fallback servers are automatically shut down when the job finishes.

The NVIDIA Triton Inference Server

As shown in Fig. 2, the server-side implementation of the SONIC approach in cmssw currently uses Triton for inference on coprocessors [86, 96]. Triton is an open-source solution whose protocols are public and extensible, as noted above. It supports inference of ML algorithms, called models, in most modern formats, including PyTorch [97], TensorRT (TRT) [98], ONNX Runtime (ONNX) [99], TensorFlow [100], and XGBoost [101]. It also supports custom backends for alternative tasks, such as classical rule-based domain algorithms and inference on different types of coprocessors. Several features of Triton are worth highlighting:Multiple model instances: A single server can host multiple models at the same time or even multiple instances of the same model to allow concurrent inference requests.

Dynamic batching: Usually coprocessors have many processing units and the number of operations from one inference call is not enough to fully utilize the coprocessors. With dynamic batching, if multiple inference calls are made within a window of time, the server can concatenate the inputs of all calls into a single batch, which improves the GPU utilization and therefore increases the overall throughput. In practice, the time window is typically chosen to be about the client-side CPU processing time per event, such that the inference can be processed in time without delay.

Model analyzer: Parameters like the number of model instances on a single server, the length of a batching window, and the optimal batch size are tunable and can be optimized on a case-by-case basis. Triton provides a model analyzer tool to aid in this optimization [102]. It can mimic the clients and send randomized inputs or some pre-saved data to the server. The server performance is measured, and the optimal deployment configuration can be determined by scanning the parameter space.

Ragged batching: Traditionally, inference can be performed on a batch of multiple inputs as long as each input is of the same size. In HEP data, the size of the input for inference can vary from one instance to another, making it harder to batch them together. For example, if an algorithm uses information from every particle in an event as input, it is difficult to batch inference requests from multiple events because events can have a wide range of numbers of particles. Ragged batching allows inference requests with different sizes to be batched together, thereby improving the performance. This feature is relatively new and not yet fully studied in this paper.

Triton servers can use one or multiple GPUs on the same machine with a built-in load balancer. They can also run purely on CPU resources when there are no GPU resources available. For other types of coprocessors, Triton servers can also be used with the help of custom backends. A server requires a trained model file and a configuration file specifying input and output variable names, shapes, types, and model versions, along with the preferred batch size and other details that can be acquired through the inference optimizations. These model and configuration files are currently accessible through the CernVM-File System (CVMFS) [103], and are tracked by the cmssw release management system.

Advantages of the SONIC Approach

Inference as a service, as implemented in the SONIC approach, provides several advantages and benefits, which are summarized here. Many of these features arise from the differences between the IaaS approach and the more traditional approach of HEP software frameworks to use only local computing resources. These features include:Containerization: The SONIC approach factorizes ML frameworks out of the client software stack, i.e., cmssw, reducing the workload to support a wide variety of ML models. With the SONIC approach, one can use any framework supported by Triton, including custom backends, without needing to modify the cmssw software stack to resolve library dependencies and ensure compatibility between all external packages. This allows us to pick the best inference backend for one algorithm, with less concern for the implementation details.

Simplicity: Because of the containerization discussed above, SONIC client-side code is simpler and more general than the corresponding direct inference code. SONIC modules need only implement the conversion of input data into the server’s desired format and the reverse operation for output data.

Flexibility: In the SONIC paradigm, the connections between client CPUs and server coprocessors are not fixed. The servers can be physically located nearby or far from clients. Clients from many machines can access a single server running on either one or multiple coprocessors. Similarly, a single client can access multiple different servers running on multiple different machines.

Efficiency: The SONIC approach enables balanced utilization of coprocessor resources. By optimizing the coprocessor-to-CPU ratio for different tasks, it is easier to fully utilize coprocessor resources without oversaturating them.

Portability: Through the use of the SONIC approach, client-side code does not have to be modified to take advantage of different types of coprocessors. Only a consistent protocol for communicating with the inference server is required, regardless of the underlying hardware: CPU, GPU, FPGA, IPU, or any other architecture.

Accessibility: If GPUs or other coprocessors are not available locally, the only way to accelerate workflows is to access those resources remotely, as a service. The SONIC architecture implements this use case for CMS, allowing the use of remote coprocessors.

Limitations and Production Requirements

However, with these advantages come additional complexity and changes in resource usage. Production jobs using the SONIC approach rely on a separate server running different software, compared to the existing scheme in which jobs only execute cmssw on local hardware. This implies several additional considerations:Server failures: Inference servers, remote or local, may experience software or hardware failures that prevent them from running. These new failure modes are mostly independent from existing known sources of failures, potentially leading to an overall increase in the rate of job failures. However, these failures can be mitigated with server-side technology, such as the load balancer Kubernetes [88], and client-side protocols, like the local fallback server.

Network usage: The use of remote inference servers necessarily implies an increase in network traffic, as input and output data must be communicated over the network. Typically, input data are much larger than output data; the total usage depends on the algorithm. For the Mini-AOD production workflow tests presented here, the network usage is discussed in “Large-Scale Tests“ section. High network usage can be mitigated using compression, with some tradeoff in throughput from the additional operations to compress and decompress the data. In the studies done here, we have not observed significant issues with network usage, and as a result, analysis of the tradeoff between compression and network usage is not included.

Memory usage: The use of remote inference servers reduces the local memory usage of production jobs, compared to the direct inference approach. However, the use of local fallback servers, whether to mitigate remote server failures or take advantage of the containerization and portability of the SONIC approach, implies increased memory usage. Running the server process locally is generally expected to use more memory than the corresponding direct inference libraries. Measurements of memory usage are presented in “The CPU Fallback Server“ section.

These potential drawbacks, especially uncorrelated failures and network usage, are similar to those from other distributed services used in CMS production, such as the conditions database or XRootD [104, 105]. These can potentially impact the processing performance and should be studied more intensively.

Handling this additional complexity requires new components to be deployed in the CMS workflow management system. We provide below several examples of such operational concerns.Server discovery: The SONIC approach allows the use of remote coprocessor resources, which requires the information of servers running on these resources, e.g., IP addresses, port numbers, served models, and number of GPUs, etc. to be available to the client jobs. Such information can be collected in the site configurations and provided by the job submission system or a central service. For the studies presented in this paper, servers are launched manually, and their addresses are written into the cmssw configuration files.

Load balancing: Different production jobs running on different data sets have different coprocessor resource demands. A load balancer, such as Kubernetes, can be set up to dynamically load and unload models on different servers and distribute client inference requests to these servers. The load balancing for large-scale inference requests is tested and discussed in “Large-Scale Tests“ section.

Versioning: CMS production requires the versions of cmssw, servers, ML models, and ML backends to be controlled and recorded for proper provenance tracking. The ML model versions are already tracked by the cmssw release management system; the server and backend versioning should be included in the same system for consistency. For the studies presented in this paper, the prebuilt Triton server provided by NVIDIA was used.

Optimization procedure: While the SONIC approach enables the benefits described in “Advantages of the SONIC Approach“ section, it does not necessarily make them trivially attainable. Some use-case-specific optimizations are required before large-scale deployment, as explained in more detail in “Performance“ section. For example, each model should be analyzed individually to find a preferred batch size. Similarly, while the flexible coprocessor-to-CPU ratio permits more efficient utilization of resources, one must first determine an appropriate coprocessor-to-CPU ratio. This is further complicated by the fact that the ratio can depend on the hardware used, such as CPU or GPU type, and on the physics content of the data set being processed.

Physics Data Sets, Algorithms, and Benchmark Setup

This section describes the physics data set, the ML algorithms in cmssw that can currently use the SONIC approach for inference, and the computing resources used in the studies.

Data Set and Software Versions

In the studies, we chose to process a simulated Run 2 data set of events with one top quark and one anti-top quark (tt¯), as it includes many types of physics objects, including leptons, heavy-flavor jets, and missing transverse momentum. Here, a heavy-flavor jet is one that originates from a charm or bottom quark, and missing transverse momentum, or p→Tmiss, is defined as the negative vector sum of the transverse momenta of all of the reconstructed particles in an event, and its magnitude is denoted as pTmiss [106]. The data set was copied to local disk to factor out the effects of remote input–output (I/O) limitations for the benchmarks. The cmssw version CMSSW_12_0_0_pre5 and Triton server version 21.06 were used for the studies.

Algorithms Supported by the SONIC Approach

Adapting an ML algorithm to work with the SONIC approach requires a small effort to write code to prepare the network inputs and save the network outputs within cmssw. A comparison of the producers used for direct inference and for the SONIC approach can be found in Refs. [107] and [108], respectively. Most of the pre- and post-processing steps are the same, while the operations that send inference requests to the ML backends directly or to the servers are different.

In these studies, we tested three independent and computing-intensive ML-based algorithms in the Mini-AOD workflow. These algorithms were chosen to illustrate the performance of the SONIC approach for models with differing input and output sizes, physics applications, and backends, as detailed below.

The ParticleNet Algorithm

Graph neural networks (GNNs) have been demonstrated to achieve state-of-the-art performance for identifying jets as arising from specific particles, a task known as jet tagging [54–56]. ParticleNet [55] is a GNN-based algorithm for jet tagging and regression that represents jets as “particle clouds.” This algorithm was trained in PyTorch [97] and exported to the ONNX format. With the SONIC approach, it is possible to perform inference with ParticleNet in the following formats: ONNX; PyTorch; and PyTorch with TRT [98], which optimizes model performance on NVIDIA devices.

There are four different trained versions of ParticleNet currently running in the Mini-AOD workflow for different purposes: tagging anti-kT jets [109], clustered with the FastJet package [110], with a radius parameter of 0.4 (AK4 jets), abbreviated PN-AK4 [111],

tagging anti-kT jets with a radius parameter of 0.8 (AK8 jets) [112],

mass-decorrelated tagging for AK8 jets [112], and

mass regression for AK8 jets [113].

The three AK8 ParticleNet algorithms are abbreviated PN-AK8 in the following sections. All ParticleNet variations can be hosted on Triton servers.

The inputs to ParticleNet are the kinematic and flavor properties of the particle constituents of each jet and the secondary vertices associated with the jet, including 20 features for one particle and 11 features for one vertex. Up to 100 particles and 10 vertices are used in the inputs for AK8 jets; if there are more than 100 particles in a jet, the 100 particles with the highest transverse momenta are used, and if there are more than 10 vertices, then the 10 with the highest displacement from the beamline are used. For AK4 jets, the maximum numbers of particles and vertices are 50 and 5, respectively. For the three tagging versions of ParticleNet, the outputs of each inference are category probabilities for a variety of predefined jet categories, such as the presence of a Higgs boson or the presence of a top quark. For the mass regression, the output is a single value: the predicted jet mass.

In Mini-AOD processing, inference is performed separately for each jet in a given event, so the number of ParticleNet inferences depends on the specific physics processes and can vary substantially from event to event. When running the standard cmssw version of ParticleNet, no inference batching is performed, so each inference is truly performed separately. In this context, each inference can have a variable number of inputs with no padding involved. When using the SONIC approach for ParticleNet, it is simplest to batch all jets in an event into a single inference request, such that input particle and secondary vertex information for each jet in an event is sent to the server in a single request. Because ragged batching is not yet fully implemented in the inference server, the different inference requests must have the same number of inputs per batch. Therefore, in subsequent performance studies, we adopt a maximally padded approach, where zeros are added to the vectors of particles and vertices for AK8 (AK4) jets such that they have consistent lengths of 100 (50) and 10 (5), respectively.

An illustration of the jet content of the Run 2 simulated tt¯ data set is given in Fig. 3. The distributions of the number of jets per event and particles per jet are provided for both AK4 and AK8 jets. As shown in the figure, the number of jets can vary dramatically from event to event, indicating the importance of dynamic batching.Fig. 3 The jet information in the Run 2 simulated tt¯ data set used in subsequent studies. Distributions of the number of jets per event (left) and the number of particles per jet (right) are shown for AK4 jets (upper) and AK8 jets (lower). For the distributions of the number of particles, the rightmost bin is an overflow bin

In the configuration where all AK4 jets are padded to 50 particles and 5 vertices and all AK8 jets are padded to 100 particles and 10 vertices, a single four-threaded job processing a Run 2 tt¯ event will generate about 140 kB/s of server input for AK4 jets and about 10 kB/s of server input for each of the AK8 jet versions of ParticleNet. At a processing rate of about 4 events per second, with about 16 AK4 jets per event, this corresponds to about 2 kB of information per jet, which is consistent with the number of float inputs per jet.

The DeepMET Algorithm

DeepMET [114] is a TensorFlow-based deep neural network model that estimates the p→Tmiss in an event. The vector p→Tmiss is associated with either the production of neutrinos or potential beyond the SM particles that could propagate through the detector interacting only weakly. The inputs to DeepMET are 11 features from each particle [79] in an event, with zero-padding up to 4 500 particle candidates for a given event. This zero padding is used for both the standard version of DeepMET and its SONIC implementation. In both cases, only one inference is made per event. DeepMET outputs two values for each event: the p→Tmiss components in the transverse plane. Because every event necessarily has the same number of inputs, dynamic batching is automatically available via the SONIC approach, aiding inference efficiency in cases where many client jobs make concurrent requests within a certain time window (each client thread will make a request about once per second). A single four-threaded job processing a Run 2 tt¯ event will generate about 1.3 MB/s of server input for DeepMET.

The DeepTau Algorithm

The DeepTau algorithm [115] is a TensorFlow-based deep neural network model to identify hadronically decaying τ leptons from the jet collections. The inputs to the network include low-level particle features of electrons/photons, muons, and hadrons, and high-level features such as the τ lepton candidate kinematic information. The algorithm splits nearby cones into grid cells, and loops over the cells to collect the particle features. The low-level particle features are then processed by a convolutional neural network (CNN) to extract the particle-level information. The high-level features are processed by a fully connected neural network (FCNN) to extract the τ lepton candidate-level information. The outputs of the CNN and FCNN are combined and processed by a final FCNN to produce a four-dimensional vector that represents the probability that the candidate originates from a genuine τ lepton, a muon, an electron, or a quark or gluon jet. In the implementation of direct inference in cmssw, the network is split into three sub-models. The first two networks process the lower-level input information separately, and the third network combines the information from the first two models with the high-level information and outputs the discriminator values. In the SONIC implementation, we use a combined model with zero-padded inputs, so that dynamic batching can be used and GPUs can be utilized more efficiently. A single four-threaded job processing a Run 2 tt¯ event will generate about 4.7 MB/s of server input for DeepTau, making it the most demanding algorithm explored in this study in terms of server input.

Average Processing Time

The processing time is defined as the real-world time spent between starting and finishing processing one event. The processing time breakdown for the three algorithms highlighted above in the Run 2 tt¯ events is presented in Table 2, measured with 1-thread jobs using one single CPU core. The per-event per-thread average processing time is about 1 s; PN-AK4 consumes about 4.3% of this time and PN-AK8 about 1.1%, while DeepMET and DeepTau take about 1.3 and 2.1%, respectively. Thus, for this collection of events, the algorithms supported by the SONIC approach account for about 9% of the total processing time. This fraction is dependent on the type of events. For example, if there are fewer jets per event, ParticleNet will consume a smaller fraction of the total processing time. While the fraction of the total workflow accelerated with the SONIC approach in this paper is less than 10%, an increasing number of ML algorithms are being integrated into CMS data processing. Because of this, it will be possible to accelerate a larger fraction of the total processing time with the SONIC approach in the future.Table 2 The average time of the Mini-AOD processing (without the SONIC approach) with one thread on one single CPU core.

Algorithm	Time [ms]	Fraction [%]	Input [MB]	
PN-AK4	42	4.3	0.04	
PN-AK8	11	1.2	0.01	
DeepMET	13	1.3	0.33	
DeepTau	21	2.1	1.18	
PN-AK4+PN-AK8+DeepMET+DeepTau	88	8.9	1.55	
Full workflow	990	100.0	—	
The average processing times of the algorithms supported by the SONIC approach are listed in the column labeled “Time.” The column labeled “Fraction” refers to the fraction of the full workflow’s processing time that the algorithm in question consumes. Together, the algorithms currently supported by the SONIC approach consume about 9% of the total processing time. This table also contains the expected server input for each model type created per event in Run 2 tt¯ events in the column labeled “Input”

Computing Resources

Fermilab, via the LHC Physics Center, provides CPU-only batch resources and a set of interactive machines with NVIDIA Tesla T4 GPUs [116]. Through Fermilab, we were also able to steer the allocation of cloud resources (see below) using the HEPCloud [117] framework. These resources are physically located in Illinois.

The Google Cloud Platform (GCP) provides virtual machines (VMs) that are either CPU-only or enabled with NVIDIA Tesla T4 GPUs. By default, the CPUs are a mix of Skylake, Broadwell, Haswell, Sandy Bridge, and Ivy Bridge architectures [118]. In GCP, we created customized machines with specified numbers of CPU threads or different ratios of CPU threads to numbers of GPUs. Similarly, a customized, dynamic SLURM [119] cluster was created that could instantiate and deplete four-thread VMs on demand for medium-scale tests that ran jobs across O(1000) CPUs. The cluster’s four-thread configuration was chosen so that four-threaded cmssw jobs would saturate the node’s resources, improving the reproducibility of timing tests. CPU-only VMs could also be instantiated through HEPCloud. In GCP, we also maintained GPU-enabled VMs running Triton servers that both the SLURM and HEPCloud client nodes could access. These resources are physically located in Iowa.

At the Purdue CMS Tier-2 computing cluster, tests were performed with reserved CPU-only and GPU-enabled machines. The CPU-only machines are 20-core Intel E5-2660 v3 machines, and the GPU-enabled machines each have two AMD EPYC-7702 CPUs [120] with an NVIDIA Tesla T4 GPU. Reserving these nodes allowed for controlled resource utilization, leading to more reproducible timing tests. These resources are physically located in Indiana.

The diversity of resource locations used in these studies demonstrates one of this approach’s key features, namely that it enables the use of nonlocal resources. We were able to start a server in one location and have client jobs running at another site. One such study is presented in “Cross-site Tests“ section.

Within a GCP project, VMs do not have ingress bandwidth limits other than machine limits. These are above 10 GB/s, which, based on the scans in “Per-Algorithm Inference Optimization“ section and Table 2, is far above the amount of information that can be sent from client VMs to a server-hosting machine without saturating GPU resources. For example, in GCP, if a single GPU is used to host all model types, then in a typical running scenario, it can service about 33 simultaneous four-threaded client jobs, or 132 client cores. A single client core generates about 1.55 MB/s of traffic, so a total of 200 MB/s of ingress is expected per GPU at the saturation point, which is reached when the GPU is running at the maximum throughput and cannot handle any additional incoming requests. Similarly, the ingress bandwidth from the server into the client VMs will be very small, as inference results are no more than O(10) float values per algorithm. The GCP does impose VM egress bandwidth limits, which is typically 0.25 GB/s per CPU for a VM to an internal IP address. However, given that a single client-side core will generate about 1.55 MB/s of network traffic, this is also well within the allowed limit. There are slightly more stringent restrictions for GCP bandwidth to external IP addresses. However, when we performed such tests as in “Cross-site Tests“ section, typically only one external machine was involved, with levels of network traffic well within that allowed by the restrictions.

Performance

In this section, we discuss the performance of running the Mini-AOD production workflow with the SONIC approach. We compare to direct inference, which refers to the standard approach where the inference is performed using the ML backends integrated into cmssw on CPUs.

As mentioned in “ The NVIDIA Triton Inference Server“ section, we first optimize the per-algorithm workflows to find the optimal configurations for the full production workflow. Next we check the impact of deploying servers on different sites. Finally, we mimic the real production jobs by running scale-up tests and evaluating the performance.

Per-Algorithm Inference Optimization

To maximize the resource efficiency and throughput benefits of the SONIC approach, we first perform single-model characterization studies independent of cmssw. For example, to maximize GPU usage without oversaturation, we need to find the optimal ratio of client-side CPUs to server-side GPUs, batch size for inference in the Triton server, and model configuration that will provide the highest throughput.

The latter two optimizations can be performed with the Triton Model Analyzer tool [102]. This tool feeds inputs in the correct tensor format (either randomized numbers or real data) to a loaded model hosted on a server, allowing for robust characterization of processing time per inference or exploration of the impact of batch size. As an example, we measure the processing time and throughput of the ParticleNet algorithm for AK4 jet tagging on one NVIDIA Tesla T4 GPU, with different inference backends supported in Triton: ONNX, ONNX with TRT, and PyTorch [97] (labeled PT in the figures). The results are shown in Fig. 4.Fig. 4 Average processing time (left) and throughput (right) of the PN-AK4 algorithm served by a Triton server running on one NVIDIA Tesla T4 GPU, presented as a function of the batch size. Values are shown for different inference backends: ONNX (orange), ONNX with TRT (green), and PyTorch (red). Performance values for these backends when running on a CPU-based Triton server are given in dashed lines, with the same color-to-backend correspondence

For smaller batch sizes, the TRT version of the ParticleNet algorithm leads to the highest throughput in total inferences per second, while at higher batch sizes, the PyTorch version gives higher throughput. In the version of ParticleNet supported by SONIC in cmssw, all the jets in a single event are batched together, and there are on average 16 AK4 jets per event in our tt¯ data set. To achieve higher batch sizes in a production scenario, multiple cmssw clients would need to make an inference request to the same server within a relatively narrow time window. Triton allows us to specify a preferred batch size, such that if many inference requests are queued within the time window, the server will perform inference with batches of approximately the specified size. For example, the peak throughput seems to plateau around a batch size of 100 for the PyTorch version of ParticleNet.Fig. 5 Average processing time (left) and throughput (right) of one of the AK8 ParticleNet algorithms served by a Triton server running on one NVIDIA Tesla T4 GPU, presented as a function of the batch size. Values are shown for different inference backends: ONNX (orange), ONNX with TRT (green), and PyTorch (red). Performance values for these backends when running on a CPU-based Triton server are given in dashed lines, with the same color-to-backend correspondence

Similar studies can be performed on other models as well. Figures 5, 6, and 7 show the processing time and throughput scans of PN-AK8 jet tagging, DeepMET, and DeepTau models, respectively. Some backend tests are skipped due to the model availability.Fig. 6 Average processing time (left) and throughput (right) of the DeepMET algorithm served by a Triton server running on one NVIDIA Tesla T4 GPU, presented as a function of the batch size. Similar performance when running on a CPU-based Triton server is given in dashed lines

Fig. 7 Average processing time (left) and throughput (right) of the DeepTau algorithm served by a Triton server running on one NVIDIA Tesla T4 GPU, presented as a function of the batch size. Values are shown for different inference backends: TensorFlow (TF) (orange), and TensorFlow with TRT (blue). Performance values for these backends when running on a CPU-based Triton server are given in dashed lines, with the same color-to-backend correspondence

The model analyzer can also determine approximately how many inferences per second a single GPU can perform before saturation. For example, for the PyTorch version of ParticleNet for AK4 jets, a single Tesla T4 GPU can perform about 5500 inferences per second without saturating. This corresponds to about 350 events per second, given the typical number of jets per event. Based on this, we can estimate how many CPU clients one GPU can support in parallel. A typical production configuration runs four-threaded Mini-AOD jobs, each of which processes about 3.9 events per second. Therefore, a single GPU should be able to handle about 90 four-threaded jobs in parallel running asynchronously, assuming it is being used exclusively for a single server hosting the PyTorch PN-AK4 model.

This expected saturation point can be tested directly by running the Mini-AOD workflow in cmssw and scanning the throughput as a function of the number of cmssw CPU clients pinging one GPU server. Figure 8 shows such tests for the PN-AK4, PN-AK8 jet tagging, DeepMET, and DeepTau models, which were performed in GCP using a custom SLURM cluster. For each model, a single server running on one NVIDIA T4 GPU was started on one cloud VM, and client-side jobs were executed in VMs that had 4 CPU threads. The tests for each model class were performed separately, and as one model class was being tested, the direct-inference versions of the other models were used.

The accelerated versions of the workflow can be compared with the dashed black line, which represents the average throughput of the workflow when the direct-inference versions of all the models were used. In this case, jobs were also started in the VMs with 4 CPU threads, but there was no communication with an external server. As there were no shared resources between jobs, there is no expected dependence on the number of synchronized jobs. The average processing time for this setup was determined very accurately by simply running a large number of jobs, so no associated error is shown in Fig. 8.

When offloading PN-AK4 inference to the GPU, we expect an improvement in the overall throughput of about 4% compared with direct inference, corresponding to the fraction of time taken by the total PN-AK4 processing shown in Table 2. Such an improvement is observed before saturation, where the throughput is stable as a function of the number of simultaneous CPU clients. The throughput decreases as the GPU starts to saturate because individual client-side jobs have to wait longer for inference requests to complete and return. The throughput becomes lower than CPU-only inference slightly above 160 four-threaded parallel jobs, i.e.,, equivalent to 640 single-threaded jobs, comparable with the expected saturation point from the model analyzer.Fig. 8 The GPU saturation scan performed in GCP, where the per-event throughput is shown as a function of the number of parallel CPU clients for the PyTorch version of PN-AK4 (black), DeepMET (blue), DeepTau optimized with TRT (red), and all PyTorch versions of PN-AK8 on a single GPU (green). Each of the parallel jobs was run in a four-threaded configuration. The CPU tasks ran in four-threaded GCP VMs, and the Triton servers were hosted on separate single GPU VMs also in GCP. The line for direct-inference jobs represents the baseline configuration measured by running all algorithms without the use of the SONIC approach or any GPUs. Each solid line represents running one of the specified models on GPU via the SONIC approach

Similar analyses were performed for the other models. When all three variants of ParticleNet for AK8 jets are hosted on a single GPU, that GPU can serve about 190 simultaneous four-threaded client jobs. For DeepTau and DeepMET, a single GPU hosting only one of the algorithms could serve about 64 and 520 client jobs, respectively. The differences between these saturation points are primarily due to different model sizes and numbers of objects per event. From these saturation values, it is possible to determine the number of GPUs needed to serve a production job that uses many client-side CPUs, and thus to determine the ratio of the number of GPUs hosting different models. For ParticleNet and DeepTau, the saturation points will depend on the number of jets and tau leptons in the processed events, such that the ratio of GPUs hosting different models and the required ratio of GPUs to CPUs is dependent on the type of events. If dynamic batching is enabled, the number of GPUs required for a model scales approximately linearly with the number of objects per event that require an ML inference. For example, if there are half as many jets per event, it would require about half as many GPUs to serve ParticleNet, while the number of GPUs required for DeepMET should not change, as that model makes one inference per event.

A single GPU can host multiple models such as DeepTau and DeepMET. In practice, it was found that loading only a single model on each GPU (split-model) led to about a 3–5% increase in the overall performance relative to loading every model on each GPU (all-on-one). If a 10% improvement in throughput is observed in the split-model configuration, one would expect better than a 9.5% improvement in the all-on-one configuration. Because of this slight improvement, the split-model configuration was used in subsequent large-scale tests. However, in scenarios where different physics processes are combined into a single data set, using the all-on-one configuration may be the most straightforward deployment option. Preparatory model profiling should still be performed to ensure that enough GPUs and model instances will be available, but the difference in performance between model partitioning schemes is small.

Cross-site Tests

A potential bottleneck in the SONIC paradigm is the increased inference latency caused by the physical distance between client and server and other network traffic. While a previous study observed that the average processing time difference between remote and on-premises servers is negligible [63], we tested this observation explicitly with the Mini-AOD workflow, with results presented in Fig. 9. In this test, client-side jobs were executed at Purdue’s Tier-2 computing cluster in Indiana. All the models are loaded into one server running on a single GPU for simplicity. The blue points and lines show the throughput improvement in the Mini-AOD workflow when the client jobs communicate with Triton servers hosting all the models at the same time, running on a single GPU also physically located at Purdue. The improvement is shown as a function of the number of simultaneous four-threaded client-side jobs running at once. The single GPU server begins to saturate when about 10 client-side jobs are sending requests at once and the Mini-AOD workflow running with the SONIC approach becomes slower than the CPU-only workflow if more than about 17 four-threaded client-side jobs are running at once. The direct-inference line in Fig. 9 was made in the same way discussed in “Per-Algorithm Inference Optimization for Fig. 8, though the CPU-only jobs were run at Purdue rather than GCP in Iowa.

These cross-site tests were performed before model configuration optimization as discussed in the preceding section. The exact model configuration is less important for this test, as long as the near and far servers have the same type of GPUs and host the same models. Here, the non-TRT version of DeepTau was used, which saturated close to 40 synchronized four-threaded jobs. When all the models are loaded on a single GPU server, the approximate saturation point can be found with reciprocal addition. With the saturation points of 90 (AK4 jet ParticleNet), 190 (AK8 jet ParticleNet, 3 models), 40 (DeepTau), and 520 (DeepMET, 2 models), the expected saturation point is around 18 synchronized four-threaded jobs, when running all the models together on one GPU. As noted in the previous section, this estimate is often about 5% high. The saturation point seen in Fig. 9 is lower than expected from the above analysis due to the different configurations of the CPU machines in each scenario. Twenty-threaded, hyperthreading-disabled Intel Xeon Processor E5-2660 v3 units were used at Purdue, which are more powerful processors than those used in the GCP-based tests. The faster the client-side resources process events, the lower the saturation point is for a given type of GPU.

The blue points and line in Fig. 9 show the throughput improvement when the client-side jobs are once again run at Purdue, while the Triton server hosting all the models on a single GPU is run on GCP resources, which were physically located in Iowa for this test. Both the observed throughput increase and observed GPU saturation point are about the same for both server locations, so we conclude that the client-to-server distance has little impact on performance up to a few hundred kilometers. In the future, it will be important to monitor the impact of distance, especially beyond a few hundred kilometers.Fig. 9 Production tests across different sites. The CPU tasks always run at Purdue, while the servers with GPU inference tasks for all the models run at Purdue (blue) and at GCP in Iowa (red). The throughput values are higher than those shown in Fig. 8 because the CPUs at Purdue are more powerful than those comprising the GCP VMs

Large-Scale Tests

Finally, we perform large-scale tests to emulate realistic Mini-AOD production scenarios. These tests were performed exclusively using GCP resources. Here, 24 NVIDIA Tesla T4 GPUs were used to host the PyTorch version of ParticleNet for AK4 jets, 20 GPUs were used to host the PyTorch version of all three ParticleNet models for AK8 jets, 48 GPUs were used to host DeepTau, and 10 GPUs were used to host DeepMET. The ratios of the number of GPUs hosting each model do not exactly match those expected from “Per-Algorithm Inference Optimization“ section; this was done for multiple reasons. First, a larger number of GPUs than strictly necessary were used in the tests to ensure that the performance would meet the expectations described in “Per-Algorithm Inference Optimization“section, and thereby avoid the cost of repeating the test multiple times. For instance, based on a single-GPU saturation point of 150 four-threaded jobs, one would expect that only about 17 GPUs would be needed to serve the AK4 jet ParticleNet model. It is worth noting that while the demonstration illustrated here uses a CPU-to-GPU ratio of 9820:102 (about 1 GPU per 96 CPU cores), achieving a higher ratio is likely possible as we could have safely decreased the number of GPUs used. Second, it was more practical to instantiate VMs with factors of 4 GPUs. This had the added benefit of maximizing the allowed I/O bandwidth for VMs, which in GCP is restricted for VMs with fewer CPU cores. A maximal CPU-to-GPU ratio for VMs in GCP is achieved for machines with 4 GPUs. While I/O bandwidth was not expected to create problems for this large test, a conservative approach was taken to mitigate the risk of needing repeated trials. Thus, the GPU allocation approach was to find the minimum number of GPUs expected based on single saturation scans (17 for the AK4 jet ParticleNet model, 14 for the AK8 jet ParticleNet models, 42 for DeepTau, and 5 for DeepMET), find the next largest number divisible by 4, then add one extra server corresponding to 4 additional GPUs. For DeepMET, two 4-GPU VMs were used along with one 2-GPU machine, as DeepMET is a relatively lightweight algorithm, and the approach used for the other algorithms would have more than doubled the number of GPUs allocated for the algorithm.

A separate Kubernetes load-balancer was used for each model type to distribute inference requests evenly among server-hosting, GPU-enabled VMs. Thus, client VMs used separate IP addresses for each model type, and each inference for a particular model type was passed through a single load-balancing machine, allowing for network monitoring for each model separately.

Client-side jobs also ran on CPU-only GCP resources, using HEPCloud to dynamically allocate preemptible resources and assign jobs to the client-side VMs. Each client job was run in a four-threaded configuration, with input data files stored locally, and each VM created in this HEPCloud setup had 32 cores and 160 GB of memory, meaning up to 8 simultaneous jobs could run in a single VM.

The largest test had 2500 simultaneous client-side jobs, which amounts to 10 000 CPU cores. Because these jobs were run on preemptible resources, Google reserves the right to reallocate any VM to higher priority requests from other GCP users. Of the 2500 jobs, 2455 jobs completed successfully without preemption, so in total 9820 client-side CPUs were used.

The results of this large-scale test are shown in Fig. 10. The jobs running with the SONIC approach achieved an average throughput of 4.0 events/s, while CPU-only benchmarking jobs had a throughput of 3.5 events/s. This 13% increase in throughput matches the expectation of completely removing the ParticleNet, DeepTau, and DeepMET inference from the total Mini-AOD per-event processing time within the uncertainty, which is typically around 3% from small-scale tests. The throughput values for this test are slightly different from those shown in Figs. 8 and 9. As noted previously, this is due to the difference in the CPUs used in the tests.

As mentioned before, server-side VMs were optimized to allow maximal input and output bandwidth. No bottlenecks due to bandwidth were observed in this scale-out test. We noted that the maximum data input rate received by one of the Kubernetes load balancers was 11.5 GB/s, which was for DeepTau. The next-highest data input rate was 3.3 GB/s for DeepMET, and less than 500 MB/s of input was needed for all ParticleNet models combined. These values are consistent with expectations from Table 2, as there were roughly 10 000 CPU cores running simultaneously, each processing about 1 event per second. The output rate was significantly smaller for each model, as most return only one or a few floating-point values as the inference result. In the future, more algorithms will be able to be run on coprocessors. These could have larger I/O sizes than the algorithms considered here, so continuing to monitor the network usage will be important.Fig. 10 Scale-out test results on Google Cloud. The average throughput of the workflow with the SONIC approach is 4.0 events/s (solid blue), while the average throughput of the direct-inference workflow is 3.5 events/s (dashed red)

Portability

While the inference servers discussed so far have exclusively utilized GPU resources, servers are easily portable and can run on other processing platforms. Previous uses of the SONIC approach with FPGAs are reported in detail in Refs. [61, 62], where the portability was demonstrated by changing the coprocessor technology and server backend without modifying the client-side software. In the new studies reported here, we have additionally run the Mini-AOD workflow with servers using both CPUs and Graphcore IPUs [33, 34].

The CPU Fallback Server

When running with remote servers, one potentially common and important failure mode is communication errors between clients and servers. To support automatic local CPU inference as a backup option when communication failures occur with remote servers, the SONIC implementation includes a service that can launch a Triton server using local CPU resources for any SONIC approach-compatible models, referred to as a fallback server. Fallback servers can also be used for inference when third-party ML frameworks are not supported for direct inference in cmssw.

Ideally, the use of fallback servers should have minimal impact on per-event throughput relative to running direct-inference jobs without the SONIC approach. This is contingent on two factors. First, the latency introduced by sending data to or from local servers must be negligible. Second, servers should introduce minimal overhead to maximize the CPU resources used to perform inference. The first concern can be resolved using the shared-memory option, which skips the gRPC communication and directly passes the data in certain memory chunks between the server and client. The gRPC overhead in most cases is found to be negligible. Regarding the second concern, local servers are running on the same CPUs as the other modules in the workflow, so scheduling efforts must be made to avoid CPU thread contention between the two. This implies that the synchronous server mode is preferred for local CPU fallback servers. Additionally, inference tasks should not create extra threads to avoid contention. In our experiments, we found that using more inference threads in the server than the number of threads allocated for the cmssw job will slow processing down dramatically.

Having thus explored potential configurations, for the local CPU inference, we run our servers in synchronous mode, with the number of model instances set to the number of threads per job, and the number of inference threads always set to one. This configuration mimics direct inference and avoids thread over-subscription as much as possible. We compare the throughput between direct inference and the SONIC approach with local CPU fallback servers using this configuration. Tests were performed using resources at the Purdue Tier-2 cluster with the CPU-only nodes. There are nCPU=20 Intel E5-2660 CPU cores on one node, and hyperthreading is disabled to ensure more stable results. For all tests, we always saturate the CPU nodes by requiring the product of the number of jobs (nj) and number of threads per job (nT) to be equal to the number of CPU cores: njnT=nCPU=20. For example, to test a configuration where a single job occupies four threads (nT=4), we would run five synchronized jobs (nj=5), while a two-threaded configuration would require 10 synchronized jobs. We scan the throughput as a function of the number of threads, as shown in Fig. 11. The throughput of running with local CPU fallback servers is similar to direct inference. The higher throughput in some cases is a result of optimizations in more recent versions of ONNX Runtime installed on the server, which can be controlled in real production jobs.Fig. 11 Throughput (upper) and throughput ratio (lower) between the SONIC approach and direct inference in the local CPU tests at the Purdue Tier-2 cluster. To ensure the CPUs are always saturated, the number of threads per job multiplied by the number of jobs is set to 20

Memory usage was also monitored with the top command during the studies and provided in Table 3. As the number of threads per job increases, the number of model instances increases as well, leading to higher memory usage in the SONIC approach compared with direct inference. On the other hand, if the number of model instances is fixed to one, the server memory usage is always around 300 MB, so the total memory usage is similar to direct inference, but the throughput decreases by 5–10%, depending on the tasks. Further simultaneous optimization of the throughput and memory usage will be explored in the future.Table 3 Memory usage with direct inference and the SONIC approach in the local CPU tests at the Purdue Tier-2 cluster. The last column is calculated as the sum of client and server memory usage divided by the direct inference memory usage. To ensure the CPUs are always saturated, the number of threads nT per job multiplied by the number of jobs is set to 20

nT	cmssw with	cmssw with SONIC	SONIC app	SONIC/direct	
per job	direct inference [MB]	app. (client) [MB]	server [MB]	
1	1850	1700	200	103%	
2	2000	1800	400	110%	
5	2200	1950	800	125%	
10	2500	2200	1200	136%	
20	2900	2500	2000	155%	

Studies with Graphcore IPUs

As discussed in Sects. 3.2 and 3.3, NVIDIA Triton inference servers support custom backends to run with different coprocessors and different (e.g.,, ML) backends. Since the SONIC approach’s client code only depends on the Triton protocols, algorithms implemented in this way can easily be ported to different types of coprocessors. One of the Mini-AOD production tests was run together with the Graphcore IPU team, where a custom backend was prepared by the developer team to support running ML inference with IPUs. The supported ML frameworks on IPUs when the tests were performed included TensorFlow, ONNX, and PyTorch. PyTorch-Geometric support was in development at that time and is now available. With TensorFlow custom backend available for the server, we can easily run DeepMET and DeepTau inference with the SONIC approach in cmssw on IPUs.

First, a per-algorithm throughput scan was carried out. Comparing running inference on MK2 GC200 IPUs with NVIDIA Tesla V100s, a chip-to-chip factor of 3 throughput improvement was found, with larger gains expected for more computing-intensive models. Running the entire Mini-AOD workflow was also tested. We adapted the workflow configuration to point the cmssw clients running at the Purdue Tier-2 cluster to IPU servers on the Graphcloud cloud. Without any other modifications on the client side, the workflow ran successfully, performing inference of the DeepMET and DeepTau models on the cloud and the other parts of the workflow on the client local CPUs. Outputs were checked and found to be consistent with direct inference, within 10-6 differences for floats due to precision limits.

Summary

Within the next decade, the data-taking rate at the LHC will increase dramatically, straining the expected computing resources of the LHC experiments. At the same time, more algorithms that run on these resources will be converted into either machine learning or domain algorithms that are easily accelerated with the use of coprocessors, such as graphics processing units (GPUs). By pursuing heterogeneous architectures, it is possible to alleviate potential shortcomings of available central processing unit (CPU) resources.

Inference as a service (IaaS) is a promising scheme to integrate coprocessors into CMS computing workflows. In IaaS, client code simply assembles the input data for an algorithm, sends that input to an inference server running either locally or remotely, and retrieves output from the server. The implementation of IaaS discussed throughout this paper is called the Services for Optimized Network Inference on Coprocessors (SONIC) approach, which employs NVIDIA Triton Inference Servers to host models on coprocessors, as demonstrated here in studies on GPUs, CPUs, and Graphcore Intelligence Processing Units (IPUs).

In this paper, the SONIC approach in the CMS software framework (cmssw) is demonstrated in a sample Mini-AOD workflow, where algorithms for jet tagging, tau lepton identification, and missing transverse momentum regression are ported to run on inference servers. These algorithms account for nearly 10% of the total processing time per event in a simulated data set of top quark-antiquark events. After model profiling, which is used to optimize server performance and determine the needed number of GPUs for a given number of client jobs, the expected 10% decrease in per-event processing time was achieved in a large-scale test of Mini-AOD production with the SONIC approach that used about 10 000 CPU cores and 100 GPUs. The network bandwidth is large enough to support high input–output model inference for the workflow tested, and it will be monitored as the fraction of algorithms using remote GPUs increases.

In addition to meeting performance expectations, we demonstrated that the throughput results are not highly sensitive to the physical client-to-server distance, at least up to distances of hundreds of kilometers. Running inference through Triton servers on local CPU resources does not affect the throughput compared with the standard approach of running inference directly on CPUs in the job thread. We also performed a test using GraphCore IPUs to demonstrate the flexibility of the SONIC approach.

The SONIC approach for IaaS represents a flexible method to accelerate algorithms, which is increasingly valuable for LHC experiments. Using a realistic workflow, we highlighted many of the benefits of the SONIC approach, including the use of remote resources, workflow acceleration, and portability to different processor technologies. To make it a viable and robust paradigm for CMS computing in the future, additional studies are ongoing or planned for monitoring and mitigating potential issues such as excessive network and memory usage or server failures.

Acknowledgements

We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at CERN and at other CMS institutes for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid and other centers for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC, the CMS detector, and the supporting computing infrastructure provided by the following funding agencies: SC (Armenia), BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES and BNSF (Bulgaria); CERN; CAS, MoST, and NSFC (China); MINCIENCIAS (Colombia); MSES and CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); ERC PRG, RVTT3 and TK202 (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); SRNSF (Georgia); BMBF, DFG, and HGF (Germany); GSRI (Greece); NKFIH (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); MES (Latvia); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MES and NSC (Poland); FCT (Portugal); MESTD (Serbia); MCIN/AEI and PCTI (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); MHESI and NSTDA (Thailand); TUBITAK and TENMAK (Turkey); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (USA).

Individuals have received support from the Marie-Curie programme and the European Research Council and Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, 758316, 765710, 824093, and COST Action CA16108 (European Union); the Leventis Foundation; the Alfred P. Sloan Foundation; the Alexander von Humboldt Foundation; the Science Committee, project no. 22rl-037 (Armenia); the Belgian Federal Science Policy Office; the Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium) under the “Excellence of Science—EOS” – be.h project n. 30820817; the Beijing Municipal Science & Technology Commission, No. Z191100007219010 and Fundamental Research Funds for the Central Universities (China); the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Shota Rustaveli National Science Foundation, grant FR-22-985 (Georgia); the Deutsche Forschungsgemeinschaft (DFG), under Germany’s Excellence Strategy – EXC 2121 “Quantum Universe”—390833306, and under project number 400140256—GRK2497; the Hellenic Foundation for Research and Innovation (HFRI), Project Number 2288 (Greece); the Hungarian Academy of Sciences, the New National Excellence Program - ÚNKP, the NKFIH research grants K 124845, K 124850, K 128713, K 128786, K 129058, K 131991, K 133046, K 138136, K 143460, K 143477, 2020-2.2.1-ED-2021-00181, and TKP2021-NKTA-64 (Hungary); the Council of Science and Industrial Research, India; ICSC—National Research Centre for High Performance Computing, Big Data and Quantum Computing, funded by the EU NexGeneration program (Italy); the Latvian Council of Science; the Ministry of Education and Science, project no. 2022/WK/14, and the National Science Center, contracts Opus 2021/41/B/ST2/01369 and 2021/43/B/ST2/01552 (Poland); the Fundação para a Ciência e a Tecnologia, grant CEECIND/01334/2018 (Portugal); the National Priorities Research Program by Qatar National Research Fund; MCIN/AEI/10.13039/501100011033, ERDF “a way of making Europe”, and the Programa Estatal de Fomento de la Investigación Científica y Técnica de Excelencia María de Maeztu, grant MDM-2017-0765 and Programa Severo Ochoa del Principado de Asturias (Spain); the Chulalongkorn Academic into Its 2nd Century Project Advancement Project, and the National Science, Research and Innovation Fund via the Program Management Unit for Human Resources & Institutional Development, Research and Innovation, grant B37G660013 (Thailand); the Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation, contract C-1845; and the Weston Havens Foundation (USA).

Author Contributions

N/A: the collaboration submits as a whole.

Funding

Open access funding provided by CERN (European Organization for Nuclear Research)

Data Availability

No datasets were generated or analyzed during the current study.

Declarations

Conflict of interest

The authors declare no conflict of interest.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

M. Narain, S. Wimpenny, A. Vorobyev:Deceased.
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