CAWS: Criticality-aware warp scheduling for GPGPU workloads

Shin Ying Lee, Carole-Jean Wu

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

46 Scopus citations

Abstract

The ability to perform fast context-switching and massive multi-threading is the forte of modern GPU architectures, which have emerged as an efficient alternative to traditional chip-multiprocessors for parallel workloads. One of the main benefits of such architecture is its latency-hiding capability. However, the efficacy of GPU's latency-hiding varies significantly across GPGPU applications. To investigate this, this paper first proposes a new algorithm that profiles execution behavior of GPGPU applications. We characterize latencies caused by various pipeline hazards, memory accesses, synchronization primitives, and the warp scheduler. Our results show that the current round-robin warp scheduler works well in overlapping various latency stalls with the execution of other available warps for only a few GPGPU applications. For other applications, there is an excessive latency stall that cannot be hidden by the scheduler effectively. With the latency characterization insight, we observe a significant execution time disparity for warps within the same thread block, which causes sub-optimal performance, called the warp criticality problem. To tackle the warp criticality problem, we design a family of criticality-aware warp scheduling (CAWS) policies by scheduling the critical warp(s) more frequently than other warps. Our results on the breadth-first-search, B+tree search, two point angular correlation function, and K-means clustering show that, with oracle knowledge of warp criticality, our best-performing scheduling policy can improve GPGPU applications' performance by 17% on average. With our designed criticality predictor, the various scheduling policies can improve performance by 10-21% on breadth-first-search. To our knowledge, this is the first paper to characterize warp criticality and explore different criticality-aware warp scheduling policies for GPGPU workloads.

Original languageEnglish (US)
Title of host publicationPACT 2014 - Proceedings of the 23rd International Conference on Parallel Architectures and Compilation Techniques
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages175-186
Number of pages12
ISBN (Print)9781450328098
DOIs
StatePublished - Jan 1 2014
Event23rd International Conference on Parallel Architectures and Compilation Techniques, PACT 2014 - Edmonton, AB, Canada
Duration: Aug 24 2014Aug 27 2014

Publication series

NameParallel Architectures and Compilation Techniques - Conference Proceedings, PACT
ISSN (Print)1089-795X

Other

Other23rd International Conference on Parallel Architectures and Compilation Techniques, PACT 2014
CountryCanada
CityEdmonton, AB
Period8/24/148/27/14

Keywords

  • gpgpu
  • gpu performance characterization
  • warp/wavefront scheduling

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture

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  • Cite this

    Lee, S. Y., & Wu, C-J. (2014). CAWS: Criticality-aware warp scheduling for GPGPU workloads. In PACT 2014 - Proceedings of the 23rd International Conference on Parallel Architectures and Compilation Techniques (pp. 175-186). (Parallel Architectures and Compilation Techniques - Conference Proceedings, PACT). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1145/2628071.2628107