Torsten Hoefler is a Professor of Computer Science at ETH Zurich[8] and the Chief Architect for Machine Learning at the Swiss National Supercomputing Centre.[9] Previously, he led the Advanced Application and User Support team at the Blue Waters Directorate of the National Center for Supercomputing Applications,[10] and held an adjunct professor position at the Computer Science Department at the University of Illinois at Urbana Champaign.[11] His expertise lies in large-scale parallel computing and high-performance computing systems. He focuses on applications in large-scale artificial intelligence as well as climate sciences.

Torsten Hoefler
Alma materIndiana University
TU Chemnitz
Awards
Scientific career
FieldsHigh-Performance Computing
Computer Science
InstitutionsETH Zurich
Swiss National Supercomputing Centre
Microsoft
Cray
University of Illinois at Urbana-Champaign
Indiana University
Doctoral advisorAndrew Lumsdaine

Hoefler is an IEEE Fellow,[12] ACM Fellow,[13] and a member of the European Academy of Sciences Academia Europaea.[14] He is also a Fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS). His Erdos number is two.[15]

He has been invited to present several keynote lectures at major international conferences such as ACM's Federated Computing Research Conference,[16] IEEE Cluster,[17] HPC Asia, Supercomputing Asia,[18] or the International Symposium on Distributed Computing.[19]

Career

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Hoefler received his Diplom in Computer Science from TU Chemnitz where he received the best student award in 2005.[20] He worked on high-performance computing systems from the very beginning of his career. He continued his studies at Indiana University, the home of Open MPI, under the guidance of Prof. Andrew Lumsdaine. He received his PhD in Computer Science in 2008 from Indiana University and was subsequently honored with the university's Young Alumni Award[21] as well as Distinguished Alumni Award[22]

He continued his work on the Message Passing Interface standard as a key member of the MPI Forum[23] responsible for the chapters on Collective Communication and Process Topologies as well as co-authoring the chapter on One-Sided Communications.[24]

In 2010, he joined the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign (UIUC). As lead for application performance analysis and support, he supported the design and deployment of the Blue Waters Supercomputer.[10] He also held a position as adjunct professor at UIUC's Computer Science department. He accepted a position as assistant professor at ETH Zurich in 2011,[25] where he received tenure in 2017,[26] and is full professor from 2020.[27]

Hoefler has held various visiting researcher positions at French Alternative Energies and Atomic Energy Commission in France, CINECA in Italy, as well as Argonne National Laboratory, Sandia National Laboratory, and Microsoft in the United States.[11] As a consultant, he supported Cray Inc. in the area of high-performance networking and Microsoft Corporation in the areas of quantum computing and large-scale artificial intelligence systems. He spent his sabbatical in 2019 at Microsoft helping to establish various AI supercomputing efforts including the Maia 100 system.[28][29][30]

Hoefler has been an elected member of the ACM SIGHPC executive committee since its founding in 2011.[31]

He was elected IEEE Fellow for “contributions to large-scale parallel processing systems and supercomputers”,[12] ACM Fellow for “foundational contributions to High-Performance Computing and the application of HPC techniques to machine learning”,[13] and he received the IEEE Sidney Fernbach Award in 2022 for “application-aware design of HPC algorithms, systems and architectures, and transformative impact on scientific computing and industry”.[3]

Hoefler received the inaugural Jack Dongarra award at ISC High Performance Conference in 2023.[32][33][34] He was appointed as a senior fellow of the Abu Dhabi Investment Authority Labs in 2023.[35][36]

Research impact

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Hoefler is known for his contributions to the Message Passing Interface (MPI) standard. He served as author for the chapters “Collective Communication” and “Process Topologies” in MPI-2.2 [1] and the chapters “Collective Communication”, “One-Sided Communications”, and “Process Topologies” in MPI-3 [2]. For the MPI-3 standardization, he chaired the Collective Communications and Topology working groups.[37]

He developed principles for the implementation of nonblocking collective operations and remote memory access that are widely used in MPI implementations such as OpenMPI, MPICH, and derivatives.[38] Nonblocking collective operations such as allreduce, allgather, or broadcast form the basis of modern AI training systems.[39]

After co-authoring a pioneering paper on parallel deep learning[40] and during his sabbatical at Microsoft, he coined the term “3D parallelism” in modern artificial intelligence training that organizes data parallelism, pipeline parallelism, and operator/tensor parallelism into one consistent view.[41]

In his work on high-speed interconnects, he co-developed several award-winning network topologies[41][42][43] and contributed routing algorithms that are used in the OpenSM routing manager on InfiniBand computer clusters.[44]

On the application side, Hoefler focuses on improving the performance of climate simulations as a digital twin[45][46][47] and machine learning for climate simulations.[48] He has been a convener of the Berlin Summit in Earth Virtualization Engines[49] to develop strategies to enable global access to high-resolution climate simulations.[50][51]

Scientific reproducibility

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Hoefler has been vocal about improving reproducibility of performance measurements in high-performance computing[52] and later machine learning. The latter is featured in IEEE Computer Journal as a cover feature on Research Reproducibility.[53] As Technical Papers chair of ACM/IEEE Supercomputing Conference (SC18), he introduced a new revision-based review process to the conference to improve the quality of the publications.[54] His group received the SIGHPC Certificate of Appreciation for reproducible methods at the ACM/IEEE Supercomputing Conference (SC22) ACM student cluster competition.[55] His paper on HammingMesh received the ACM/IEEE Supercomputing Conference (SC22) Best Reproducibility Advancement Award.[56][55] He also presented the opening keynote at the first ACM Conference on Reproducibility and Replicability.[57]

Awards and honors

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Hoefler and his team received six best (student) paper awards at the ACM/IEEE Supercomputing Conference between 2010 and 2023,[58][59][60][61] the top conference in High-Performance Computing. Additional important awards are listed below.

2024

2023

2022

2021

  • HPCWire "People to Watch"[63]

2020

2019

2015

2014

2013

2012

  • SIAM SIAG/SC Junior Scientist Prize[73]

References

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  1. ^ a b "Awards all around: AI in mathematics, microscopy and climate research". www.acm.org. Retrieved 30 September 2024.
  2. ^ "Global Computing Association Names 57 Fellows for Outstanding Contributions That Propel Technology Today". www.acm.org. Retrieved 17 February 2023.
  3. ^ a b c "Torsten Hoefler Receives IEEE CS Sidney Fernbach Award 2022". 3 October 2022. Retrieved 17 February 2023.
  4. ^ "2022 NEWLY ELEVATED FELLOWS" (PDF). Institute of Electrical and Electronics Engineers (IEEE). Archived from the original (PDF) on 24 November 2021.
  5. ^ a b "Adrian Perrig and Torsten Hoefler named IEEE Fellows". inf.ethz.ch. 7 January 2022. Retrieved 17 February 2023.
  6. ^ "ACM Names Recipients of 2019 Gordon Bell Prize". www.acm.org. Retrieved 17 February 2023.
  7. ^ a b "Turning life into a profession". ethz.ch. 16 November 2015. Retrieved 17 February 2023.
  8. ^ "Prof. Dr. Torsten Hoefler". Departement Informatik. inf.ethz.ch. Zürich, Schweiz: ETH Zürich. Retrieved 22 June 2023.
  9. ^ "ETH Professor Torsten Hoefler Joins CSCS as Chief Architect for Machine Learning".
  10. ^ a b "Blue Waters staff, partners bring home awards from SC10". NCSA. 24 November 2010. Retrieved 17 February 2023.
  11. ^ a b "Torsten Hoefler's CV" (PDF).
  12. ^ a b "2022 Newly Elevated Fellows" (PDF). Institute of Electrical and Electronics Engineers (IEEE). Archived from the original (PDF) on 24 November 2021.
  13. ^ a b c "Global Computing Association Names 57 Fellows for Outstanding Contributions That Propel Technology Today". www.acm.org. Retrieved 22 June 2023.
  14. ^ "Academy of Europe: Hoefler Torsten". www.ae-info.org. Retrieved 22 June 2023.
  15. ^ "Erdos2, Version 2020, August 7, 2020". sites.google.com. Retrieved 11 February 2024.
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  28. ^ US 11076210, Hoefler, Torsten; Heddes, Mattheus C. & Belk, Jonathan R., "Distributed processing architecture", published 2021-07-27, assigned to Microsoft Technology Licensing LLC 
  29. ^ US 11886938, Goel, Deepak; Heddes, Mattheus C. & Hoefler, Torsten et al., "Message communication between integrated computing devices", published 2021-03-11, assigned to Microsoft Technology Licensing LLC 
  30. ^ "With a systems approach to chips, Microsoft aims to tailor everything 'from silicon to service' to meet AI demand". Retrieved 11 February 2024.
  31. ^ "Meeting Your Needs - Executive Committee". www.sighpc.org. Retrieved 22 June 2023.
  32. ^ a b "Torsten Hoefler Earns First Jack Dongarra Early Career Award". HPCwire. Retrieved 22 June 2023.
  33. ^ a b "Torsten Hoefler Earns First Jack Dongarra Early Career Award - Welcome to ISC High Performance 2023". www.isc-hpc.com. Retrieved 22 June 2023.
  34. ^ a b "Torsten Hoefler Named First Winner of Jack Dongarra Early Career Award". 17 April 2023. Retrieved 22 June 2023.
  35. ^ "ADIA Lab Appoints Senior Fellows". Retrieved 15 February 2024.
  36. ^ "Fellows". Retrieved 15 February 2024.
  37. ^ "MPI 3.0 Collective Communications and Topology Working Group". Retrieved 8 November 2023.
  38. ^ "Implementation and Performance Analysis of Non-Blocking Collective Operations for MPI". Retrieved 8 November 2023.
  39. ^ "Improving NCCL performance for cloud ML applications". Retrieved 8 November 2023.
  40. ^ Ben-Nun, Tal; Hoefler, Torsten (30 August 2019). "Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis". ACM Comput. Surv. 52 (4): 65:1–65:43. doi:10.1145/3320060. S2CID 220247313. Retrieved 8 November 2023.
  41. ^ a b "HammingMesh: a network topology for large-scale deep learning". 18 November 2022. pp. 1–18. Retrieved 8 November 2023.
  42. ^ Besta, Maciej; Hoefler, Torsten (2014). "Slim Fly: A Cost Effective Low-Diameter Network Topology". SC14: International Conference for High Performance Computing, Networking, Storage and Analysis. pp. 348–359. arXiv:1912.08968. doi:10.1109/SC.2014.34. ISBN 978-1-4799-5500-8. S2CID 2149630. Retrieved 8 November 2023.
  43. ^ Arimilli, Baba; Arimilli, Ravi; Chung, Vicente; Clark, Scott; Denzel, Wolfgan; Drerup, Ben; Hoefler, Torsten; Joyner, Jody; Lewis, Jerry; Li, Jian; Ni, Nan; Rajamony, Ram (2010). "The PERCS High-Performance Interconnect". 2010 18th IEEE Symposium on High Performance Interconnects. pp. 75–82. doi:10.1109/HOTI.2010.16. ISBN 978-1-4244-8547-5. S2CID 16627945. Retrieved 8 November 2023.
  44. ^ Hoefler, Torsten; Schneider, Timo; Lumsdaine, Andrew (2009). "Optimized Routing for Large-Scale InfiniBand Networks". 2009 17th IEEE Symposium on High Performance Interconnects. pp. 103–111. doi:10.1109/HOTI.2009.9. S2CID 12742852. Retrieved 8 November 2023.
  45. ^ "Convection-resolving climate modeling on future supercomputing platforms (crCLIM)". Retrieved 8 November 2023.
  46. ^ "Scientists begin building highly accurate digital twin of our planet". 23 February 2021. Retrieved 8 November 2023.
  47. ^ Bauer, Peter; Dueben, Peter D.; Hoefler, Torsten; Quintino, Tiago; Schulthess, Thomas C.; Wedi, Nils P. (2021). "The digital revolution of Earth-system science". Nature Computational Science. 1 (2): 104–113. doi:10.1038/s43588-021-00023-0. Retrieved 8 November 2023.
  48. ^ Bauer, Peter; Dueben, Peter; Chantry, Matthew; Doblas-Reyes, Francisco; Hoefler, Torsten; McGovern, Amy; Stevens, Bjorn (2023). "Deep learning and a changing economy in weather and climate prediction". Nature Reviews Earth & Environment. 4 (8): 507–509. Bibcode:2023NRvEE...4..507B. doi:10.1038/s43017-023-00468-z. Retrieved 8 November 2023.
  49. ^ "Participants". Retrieved 8 November 2023.
  50. ^ Stevens, Bjorn; et al. (2024). "Earth Virtualization Engines (EVE)". Earth System Science Data. 16 (4): 2113–2122. Bibcode:2024ESSD...16.2113S. doi:10.5194/essd-16-2113-2024. hdl:20.500.11850/671027. Retrieved 8 November 2023.
  51. ^ "Earth Virtualization Engines: A Technical Perspective". Retrieved 8 November 2023.
  52. ^ "Scientific benchmarking of parallel computing systems: twelve ways to tell the masses when reporting performance results". doi:10.1145/2807591.2807644. S2CID 165618. Retrieved 8 November 2023.
  53. ^ Hoefler, Torsten (2022). "Benchmarking Data Science: 12 Ways to Lie With Statistics and Performance on Parallel Computers". Computer. 55 (8): 49–56. doi:10.1109/MC.2022.3152681. S2CID 251294669. Retrieved 8 November 2023.
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