An adaptive performance modeling tool for GPU architectures

Sara S. Baghsorkhi, Matthieu Delahaye, Sanjay J. Patel, William D. Gropp, Wen Mei W. Hwu

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

Abstract

This paper presents an analytical model to predict the performance of general-purpose applications on a GPU architecture. The model is designed to provide performance information to an auto-tuning compiler and assist it in narrowing down the search to the more promising implementations. It can also be incorporated into a tool to help programmers better assess the performance bottlenecks in their code. We analyze each GPU kernel and identify how the kernel exercises major GPU microarchitecture features. To identify the performance bottlenecks accurately, we introduce an abstract interpretation of a GPU kernel, work flow graph, based on which we estimate the execution time of a GPU kernel. We validated our performance model on the NVIDIA GPUs using CUDA (Compute Unified Device Architecture). For this purpose, we used data parallel benchmarks that stress different GPU microarchitecture events such as uncoalesced memory accesses, scratch-pad memory bank conflicts, and control flow divergence, which must be accurately modeled but represent challenges to the analytical performance models. The proposed model captures full system complexity and shows high accuracy in predicting the performance trends of different optimized kernel implementations. We also describe our approach to extracting the performance model automatically from a kernel code.

Original languageEnglish (US)
Title of host publicationPPoPP'10 - Proceedings of the 2010 ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
Pages105-114
Number of pages10
DOIs
StatePublished - Mar 15 2010
Event2010 ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP'10 - Bangalore, India
Duration: Jan 9 2010Jan 14 2010

Publication series

NameProceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP

Other

Other2010 ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP'10
CountryIndia
CityBangalore
Period1/9/101/14/10

Keywords

  • Analytical model
  • GPU
  • Parallel programming
  • Performance estimation

ASJC Scopus subject areas

  • Software

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  • Cite this

    Baghsorkhi, S. S., Delahaye, M., Patel, S. J., Gropp, W. D., & Hwu, W. M. W. (2010). An adaptive performance modeling tool for GPU architectures. In PPoPP'10 - Proceedings of the 2010 ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (pp. 105-114). (Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP). https://doi.org/10.1145/1693453.1693470