GPGPU Performance Estimation with Core and Memory Frequency Scaling

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

12 Citations (Scopus)

Abstract

Graphics processing units (GPUs) support dynamic voltage and frequency scaling to balance computational performance and energy consumption. However, simple and accurate performance estimation for a given GPU kernel under different frequency settings is still lacking for real hardware, which is important to decide the best frequency configuration for energy saving. We reveal a fine-grained analytical model to estimate the execution time of GPU kernels with both core and memory frequency scaling. Over a 2× range of both core and memory frequencies among 20 GPU kernels, our model achieves accurate results (4.83 % error on average) with real hardware. Compared to the cycle-level simulators, our model only needs simple micro-benchmarks to extract a set of hardware parameters and kernel performance counters to produce such high accuracy without kernel source analysis.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE 24th International Conference on Parallel and Distributed Systems, ICPADS 2018
PublisherIEEE Computer Society
Pages417-424
Number of pages8
ISBN (Electronic)9781538673089
DOIs
Publication statusPublished - 19 Feb 2019
Event24th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2018 - Singapore, Singapore
Duration: 11 Dec 201813 Dec 2018

Publication series

NameProceedings of the International Conference on Parallel and Distributed Systems - ICPADS
Volume2018-December
ISSN (Print)1521-9097

Conference

Conference24th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2018
Country/TerritorySingapore
CitySingapore
Period11/12/1813/12/18

Scopus Subject Areas

  • Hardware and Architecture

User-Defined Keywords

  • Dynamic Voltage and Frequency Scaling
  • GPU Performance Modeling
  • Graphics Processing Units

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