Quantitative Performance Assessment of Proxy Apps and Parents (ECP Proxy App Project Milestone ADCD-504-9)

Technical Report · Wed Apr 01 04:00:00 EDT 2020

DOI:https://doi.org/10.2172/1617284· OSTI ID:1617284

Richards, David F. ^[1]; Aaziz, Omar ^[2]; Cook, Jeanine ^[2]; Kuehn, Jeffrey ^[3]; Moore, Shirley ^[4]; Pruitt, David ^[5]; Vaughan, Courtenay ^[2]

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1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
2. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
3. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
4. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
5. Univ. of Texas at El Paso, TX (United States)

This report presents highlights of these efforts. Section 2 describes work that has been done to compare the performance of proxy applications on AMD MI60 vs. Nvidia V100 GPUs. So far only a small set of ECP proxies are running on AMD GPUs, but we will continue to expand this analysis as additional proxies become available. We find that although the MI60 and V100 have nearly the same measured memory bandwidth, memory bound proxy app kernels perform 20-30% worse on the MI60. Further work is needed to refine these comparisons to determine whether the root cause is due to differences in the hardware, software stack, platform specific optimization, or some combination of the three. Section 3 describes our continuing effort to find methods to accurately assess the similarity of proxies and parents. We have recently seen very encouraging results using a cosine similarity metric. This technique uses the angle between two vectors of hardware performance counters to characterize the similarity (or difference) between two applications or proxies. We show not only that several widely used proxies are highly similar to their parents, but also that they differ from non-related codes. We also show that cosine similarity can be used to identify gaps and redundancies in suites and even to gain insight into the effects of architectural differences between platforms. Our work on assessing the Exascale toolchain is ongoing. Our successes with performance measurement tools are evident from the data provided in this report. However, our assessments across the broader tool chain are still too incomplete to provide a meaningful report at this time. We will continue to assess tools and work with vendors and third party developers as issues are identified.

• Research Organization: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States); Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Univ. of Texas at El Paso, TX (United States); Argonne National Laboratory (ANL), Argonne, IL (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC)
• DOE Contract Number: AC52-07NA27344; NA0003525; AC05-00OR22725; 89233218CNA000001; AC02-06CH11357; AC02-05CH11231
• OSTI ID: 1617284
• Report Number(s): LLNL-TR--809403; 1015805
• Country of Publication: United States
• Language: English

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97 MATHEMATICS AND COMPUTING