Applications and Techniques for Fast Machine Learning in Science
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science—the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshop...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-04-01
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| Series: | Frontiers in Big Data |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fdata.2022.787421/full |
| _version_ | 1797659127423434752 |
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| author | Allison McCarn Deiana Nhan Tran Nhan Tran Joshua Agar Michaela Blott Giuseppe Di Guglielmo Javier Duarte Philip Harris Scott Hauck Mia Liu Mark S. Neubauer Jennifer Ngadiuba Seda Ogrenci-Memik Maurizio Pierini Thea Aarrestad Steffen Bähr Jürgen Becker Anne-Sophie Berthold Richard J. Bonventre Tomás E. Müller Bravo Markus Diefenthaler Zhen Dong Nick Fritzsche Amir Gholami Ekaterina Govorkova Dongning Guo Kyle J. Hazelwood Christian Herwig Babar Khan Sehoon Kim Thomas Klijnsma Yaling Liu Kin Ho Lo Tri Nguyen Gianantonio Pezzullo Seyedramin Rasoulinezhad Ryan A. Rivera Kate Scholberg Justin Selig Sougata Sen Dmitri Strukov William Tang Savannah Thais Kai Lukas Unger Ricardo Vilalta Belina von Krosigk Belina von Krosigk Shen Wang Thomas K. Warburton |
| author_facet | Allison McCarn Deiana Nhan Tran Nhan Tran Joshua Agar Michaela Blott Giuseppe Di Guglielmo Javier Duarte Philip Harris Scott Hauck Mia Liu Mark S. Neubauer Jennifer Ngadiuba Seda Ogrenci-Memik Maurizio Pierini Thea Aarrestad Steffen Bähr Jürgen Becker Anne-Sophie Berthold Richard J. Bonventre Tomás E. Müller Bravo Markus Diefenthaler Zhen Dong Nick Fritzsche Amir Gholami Ekaterina Govorkova Dongning Guo Kyle J. Hazelwood Christian Herwig Babar Khan Sehoon Kim Thomas Klijnsma Yaling Liu Kin Ho Lo Tri Nguyen Gianantonio Pezzullo Seyedramin Rasoulinezhad Ryan A. Rivera Kate Scholberg Justin Selig Sougata Sen Dmitri Strukov William Tang Savannah Thais Kai Lukas Unger Ricardo Vilalta Belina von Krosigk Belina von Krosigk Shen Wang Thomas K. Warburton |
| author_sort | Allison McCarn Deiana |
| collection | DOAJ |
| description | In this community review report, we discuss applications and techniques for fast machine learning (ML) in science—the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs. |
| first_indexed | 2024-03-11T18:10:21Z |
| format | Article |
| id | doaj.art-9e1a26011d554b649834394385e92870 |
| institution | Directory Open Access Journal |
| issn | 2624-909X |
| language | English |
| last_indexed | 2024-03-11T18:10:21Z |
| publishDate | 2022-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Big Data |
| spelling | doaj.art-9e1a26011d554b649834394385e928702023-10-16T15:21:41ZengFrontiers Media S.A.Frontiers in Big Data2624-909X2022-04-01510.3389/fdata.2022.787421787421Applications and Techniques for Fast Machine Learning in ScienceAllison McCarn Deiana0Nhan Tran1Nhan Tran2Joshua Agar3Michaela Blott4Giuseppe Di Guglielmo5Javier Duarte6Philip Harris7Scott Hauck8Mia Liu9Mark S. Neubauer10Jennifer Ngadiuba11Seda Ogrenci-Memik12Maurizio Pierini13Thea Aarrestad14Steffen Bähr15Jürgen Becker16Anne-Sophie Berthold17Richard J. Bonventre18Tomás E. Müller Bravo19Markus Diefenthaler20Zhen Dong21Nick Fritzsche22Amir Gholami23Ekaterina Govorkova24Dongning Guo25Kyle J. Hazelwood26Christian Herwig27Babar Khan28Sehoon Kim29Thomas Klijnsma30Yaling Liu31Kin Ho Lo32Tri Nguyen33Gianantonio Pezzullo34Seyedramin Rasoulinezhad35Ryan A. Rivera36Kate Scholberg37Justin Selig38Sougata Sen39Dmitri Strukov40William Tang41Savannah Thais42Kai Lukas Unger43Ricardo Vilalta44Belina von Krosigk45Belina von Krosigk46Shen Wang47Thomas K. Warburton48Department of Physics, Southern Methodist University, Dallas, TX, United StatesFermi National Accelerator Laboratory, Batavia, IL, United StatesDepartment of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United StatesDepartment of Materials Science and Engineering, Lehigh University, Bethlehem, PA, United StatesXilinx Research, Dublin, IrelandDepartment of Computer Science, Columbia University, New York, NY, United StatesDepartment of Physics, University of California, San Diego, San Diego, CA, United StatesMassachusetts Institute of Technology, Cambridge, MA, United StatesDepartment of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States0Department of Physics and Astronomy, Purdue University, West Lafayette, IN, United States1Department of Physics, University of Illinois Urbana-Champaign, Champaign, IL, United StatesFermi National Accelerator Laboratory, Batavia, IL, United StatesDepartment of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States2European Organization for Nuclear Research (CERN), Meyrin, Switzerland2European Organization for Nuclear Research (CERN), Meyrin, Switzerland3Karlsruhe Institute of Technology, Karlsruhe, Germany3Karlsruhe Institute of Technology, Karlsruhe, Germany4Institute of Nuclear and Particle Physics, Technische Universität Dresden, Dresden, Germany5Lawrence Berkeley National Laboratory, Berkeley, CA, United States6Department of Physics and Astronomy, University of Southampton, Southampton, United Kingdom7Thomas Jefferson National Accelerator Facility, Newport News, VA, United States8Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States4Institute of Nuclear and Particle Physics, Technische Universität Dresden, Dresden, Germany8Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States2European Organization for Nuclear Research (CERN), Meyrin, SwitzerlandDepartment of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United StatesFermi National Accelerator Laboratory, Batavia, IL, United StatesFermi National Accelerator Laboratory, Batavia, IL, United States9Department of Computer Science, Technical University Darmstadt, Darmstadt, Germany8Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United StatesFermi National Accelerator Laboratory, Batavia, IL, United States0Department of Bioengineering, Lehigh University, Bethlehem, PA, United States1Department of Physics, University of Florida, Gainesville, FL, United StatesMassachusetts Institute of Technology, Cambridge, MA, United States2Department of Physics, Yale University, New Haven, CT, United States3Department of Engineering and IT, University of Sydney, Camperdown, NSW, AustraliaFermi National Accelerator Laboratory, Batavia, IL, United States4Department of Physics, Duke University, Durham, NC, United States5Cerebras Systems, Sunnyvale, CA, United States6Birla Institute of Technology and Science, Pilani, India7Department of Electrical and Computer Engineering, University of California, Santa Barbara, Santa Barbara, CA, United States8Department of Physics, Princeton University, Princeton, NJ, United States8Department of Physics, Princeton University, Princeton, NJ, United States3Karlsruhe Institute of Technology, Karlsruhe, Germany9Department of Computer Science, University of Houston, Houston, TX, United States3Karlsruhe Institute of Technology, Karlsruhe, Germany0Department of Physics, Universität Hamburg, Hamburg, Germany1Department of Physics, University of Florida, Gainesville, FL, United States1Department of Physics and Astronomy, Iowa State University, Ames, IA, United StatesIn this community review report, we discuss applications and techniques for fast machine learning (ML) in science—the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs.https://www.frontiersin.org/articles/10.3389/fdata.2022.787421/fullmachine learning for sciencebig dataparticle physicscodesigncoprocessorsheterogeneous computing |
| spellingShingle | Allison McCarn Deiana Nhan Tran Nhan Tran Joshua Agar Michaela Blott Giuseppe Di Guglielmo Javier Duarte Philip Harris Scott Hauck Mia Liu Mark S. Neubauer Jennifer Ngadiuba Seda Ogrenci-Memik Maurizio Pierini Thea Aarrestad Steffen Bähr Jürgen Becker Anne-Sophie Berthold Richard J. Bonventre Tomás E. Müller Bravo Markus Diefenthaler Zhen Dong Nick Fritzsche Amir Gholami Ekaterina Govorkova Dongning Guo Kyle J. Hazelwood Christian Herwig Babar Khan Sehoon Kim Thomas Klijnsma Yaling Liu Kin Ho Lo Tri Nguyen Gianantonio Pezzullo Seyedramin Rasoulinezhad Ryan A. Rivera Kate Scholberg Justin Selig Sougata Sen Dmitri Strukov William Tang Savannah Thais Kai Lukas Unger Ricardo Vilalta Belina von Krosigk Belina von Krosigk Shen Wang Thomas K. Warburton Applications and Techniques for Fast Machine Learning in Science Frontiers in Big Data machine learning for science big data particle physics codesign coprocessors heterogeneous computing |
| title | Applications and Techniques for Fast Machine Learning in Science |
| title_full | Applications and Techniques for Fast Machine Learning in Science |
| title_fullStr | Applications and Techniques for Fast Machine Learning in Science |
| title_full_unstemmed | Applications and Techniques for Fast Machine Learning in Science |
| title_short | Applications and Techniques for Fast Machine Learning in Science |
| title_sort | applications and techniques for fast machine learning in science |
| topic | machine learning for science big data particle physics codesign coprocessors heterogeneous computing |
| url | https://www.frontiersin.org/articles/10.3389/fdata.2022.787421/full |
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