Hardware Specialization: Estimating Monte Carlo Cross-Section Lookup Kernel Performance and Area

Kazutomo Yoshii, John Tramm, Bryce Allen, Tomohiro Ueno, Kentaro Sano, Andrew Siegel, Pete Beckman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Hardware specialization is one of the promising directions in the post-Moore era. It is imperative to understand how hardware specialization paradigms can benefit HPC. An essential question revolves around estimating the theoretical performance of an optimally specialized architecture without requiring extensive hardware development expertise and efforts. Focusing on the Monte Carlo cross-section lookup kernel, known for its notably low resource utilization, we develop a workflow to simulate a specialized architecture's timing and estimate resource usage to answer these questions, leveraging open-source hardware tools. We implement building blocks of the kernel pipeline in the Chisel construction language and generate Verilog codes for resource estimation. Our late-breaking results show that the kernel latency is 46 cycles per lookup while the optimized CPU code takes 680 cycles, and a potential 15k pipeline copies within a 698 mm2 die, reflective of the Intel Xeon Platinum 8180 dimensions.

Original languageEnglish (US)
Title of host publicationProceedings of 2023 SC Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023
PublisherAssociation for Computing Machinery
Pages1274-1278
Number of pages5
ISBN (Electronic)9798400707858
DOIs
StatePublished - Nov 12 2023
Event2023 International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023 - Denver, United States
Duration: Nov 12 2023Nov 17 2023

Publication series

NameACM International Conference Proceeding Series

Conference

Conference2023 International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023
Country/TerritoryUnited States
CityDenver
Period11/12/2311/17/23

Funding

This work is based on work supported by the U.S. Department of Energy, Office of Science, under contract DE-AC02-06CH11357. This paper results from an international collaboration supported by an international collaboration on extreme computing by the U.S. Department of Energy (DOE) and Japan’s Ministry of Education, Culture, Sports, Science and Technology (MEXT). We gratefully acknowledge the computing resources provided and operated by the Joint Laboratory for System Evaluation (JLSE) at Argonne National Laboratory. Some of the results presented in this paper were obtained using the Chameleon testbed supported by the National Science Foundation. This research was supported by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration.

Keywords

  • Chisel
  • Cross Section Lookup Benchmark
  • Hardware Specialization
  • Monte Carlo Simulation
  • Performance Analysis

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

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