Spcl-friends March 2025

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SPCL_Bcast: John Mellor-Crummey, Measurement and Analysis of Application Performance on GPU-accelerated Systems at Exascale, Thursday, 13th March, 6PM CET
by Marcin Chrapek 10 Mar '25

10 Mar '25

John Mellor-Crummey The Scalable Parallel Computing Lab's *SPCL_Bcast* seminar continues with *John Mellor-Crummey**of **Rice University* presenting on *Measurement and Analysis of Application Performance on GPU-accelerated Systems at Exascale*. Everyone is welcome to attend (over Zoom)! *When:* Thursday, 13th March, 6PM CET *Where:* Zoom Join <https://spcl.inf.ethz.ch/Bcast/join> *Abstract:* As part of the US DOE's Exascale Computing Project, Rice University began extending its HPCToolkit performance tools to support instruction-level measurement and analysis of applications executing on GPU-accelerated exascale supercomputers. Hardware support for instruction-level performance measurement in AMD, Intel, and NVIDIA GPUs was developed at the urging of the HPCToolkit project team. HPCToolkit employs PC sampling or binary instrumentation to perform instruction-level measurements of GPU computations. When measuring a GPU-accelerated application, HPCToolkit employs a novel wait-free data structure to communicate performance measurements between tool threads and application threads. To help attribute performance information in detail, HPCToolkit performs parallel analysis of large CPU and GPU binaries involved in the execution of an exascale application to rapidly recover mappings between machine instructions and source code. To analyze terabytes of performance measurements gathered during executions at exascale, HPCToolkit employs distributed-memory parallelism, multithreading, sparse data structures, and out-of-core streaming analysis algorithms. To support interactive exploration of profiles up to terabytes in size, HPCToolkit's hpcviewer GUI uses out-of-core methods to visualize performance data. These strategies have enabled HPCToolkit to efficiently measure, analyze and explore terabytes of performance data for executions using as many as 64K MPI ranks and 64K GPU tiles on ORNL's Frontier supercomputer. This talk will describe key aspects of HPCToolkit, successes analyzing applications, and some challenges ahead. *Biography:* John Mellor-Crummey is a Professor of Computer Science at Rice University in Houston, TX, USA. His principal research focus at present is tools for measurement and analysis of application performance. His past work includes scalable synchronization algorithms for shared-memory multiprocessors, compilers and runtime systems for parallel computing, techniques for execution replay of parallel programs, tools for dynamic data race detection, and techniques for network performance analysis and optimization. Mellor-Crummey co-led development of the OMPT tools interface for OpenMP 5. He is a co-recipient of the 2006 Dijkstra Prize in Distributed Computing, co-recipient of a 2024 Honor Award from the US Secretary of Energy, and a Fellow of the ACM. More details & future talks <https://spcl.inf.ethz.ch/Bcast/> Scalable Parallel Computing Lab (SPCL) Department of Computer Science, ETH Zurich Website <https://spcl.inf.ethz.ch> X(Twitter) <https://twitter.com/spcl_eth> YouTube <https://www.youtube.com/@spcl> GitHub <https://github.com/spcl>

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