He has been invited to present several keynote lectures at major international conferences such as ACM'sFederated Computing Research Conference,[17]IEEE Cluster,[18] HPC Asia, Supercomputing Asia,[19] or the International Symposium on Distributed Computing.[20] Hoefler is known to give widely accessible presentations at major interdisciplinary events such as the Heidelberg Laureate Forum, the ADIA Lab Symposium, or the Global Young Scientists Summit, featuring Nobel and Turing laureate speakers.
He continued his work on the Message Passing Interface standard as a key member of the MPI Forum[24] responsible for the chapters on Collective Communication and Process Topologies as well as co-authoring the chapter on One-Sided Communications.[25]
Hoefler has been an elected member of the ACM SIGHPC executive committee since its founding in 2011.[32]
He was elected IEEE Fellow for “contributions to large-scale parallel processing systems and supercomputers”,[13]ACM Fellow for “foundational contributions to High-Performance Computing and the application of HPC techniques to machine learning”,[14] and he received the IEEESidney Fernbach Award in 2022 for “application-aware design of HPC algorithms, systems and architectures, and transformative impact on scientific computing and industry”.[4]
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 and the chapters “Collective Communication”, “One-Sided Communications”, and “Process Topologies” in MPI-3 . For the MPI-3 standardization, he chaired the Collective Communications and Topology working groups.[38]
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.[39] Nonblocking collective operations such as allreduce, allgather, or broadcast form the basis of modern AI training systems.[40]
On the application side, Hoefler focuses on improving the performance of climate simulations as a digital twin[46][47][48] and machine learning for climate simulations.[49] He has been a convener of the Berlin Summit in Earth Virtualization Engines[50] to develop strategies to enable global access to high-resolution climate simulations.[51][52]
Scientific reproducibility
Hoefler has been vocal about improving reproducibility of performance measurements in high-performance computing[53] and later machine learning. The latter is featured in IEEE Computer Journal as a cover feature on Research Reproducibility.[54] 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.[55] His group received the SIGHPC Certificate of Appreciation for reproducible methods at the ACM/IEEE Supercomputing Conference (SC22) ACM student cluster competition.[56] His paper on HammingMesh received the ACM/IEEE Supercomputing Conference (SC22) Best Reproducibility Advancement Award.[57][56] He also presented the opening keynote at the first ACM Conference on Reproducibility and Replicability.[58]
↑US 11886938,Goel, Deepak; Heddes, Mattheus C.& Hoefler, Torstenet al.,"Message communication between integrated computing devices",published 11 March 2021, assigned to Microsoft Technology Licensing LLC
↑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. S2CID251294669.
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