GSI: A GPU Stall Inspector to characterize the sources of memory stalls for tightly coupled GPUs

Johnathan Alsop, Matthew D. Sinclair, Rakesh Komuravelli, Sarita V. Adve

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

Abstract

In recent years the power wall has prevented the continued scaling of single core performance. This has lead to the rise of dark silicon and motivated a move toward parallelism and specialization. As a result, energy-efficient high-throughput GPU cores are increasingly favored for accelerating data-parallel applications. However, the best way to efficiently communicate and synchronize across heterogeneous cores remains an important open research question. Many methods have been proposed to improve the efficiency of heterogeneous memory systems, but current methods for evaluating the performance effects of these innovations are limited in their ability to attribute differences in execution time to sources of latency in the memory system. Performance characterization of tightly coupled CPU-GPU systems is complicated by the high levels of parallelism present in GPU codes. Existing simulation tools provide only coarse-grained metrics which can obscure the underlying memory system interactions that cause performance differences. In this work we introduce GPU Stall Inspector (GSI), a method for identifying and visualizing the causes of GPU stalls with a focus on a tightly coupled CPU-GPU memory subsystem. We demonstrate the utility of our approach by evaluating the sources of stalls in several recent architectural innovations for tightly coupled, heterogeneous CPU-GPU systems.

Original languageEnglish (US)
Title of host publicationISPASS 2016 - International Symposium on Performance Analysis of Systems and Software
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages172-182
Number of pages11
ISBN (Electronic)9781509019526
DOIs
StatePublished - May 31 2016
Event17th International Symposium on Performance Analysis of Systems and Software, ISPASS 2016 - Uppsala, Sweden
Duration: Apr 17 2016Apr 19 2016

Publication series

NameISPASS 2016 - International Symposium on Performance Analysis of Systems and Software

Other

Other17th International Symposium on Performance Analysis of Systems and Software, ISPASS 2016
CountrySweden
CityUppsala
Period4/17/164/19/16

ASJC Scopus subject areas

  • Software
  • Safety, Risk, Reliability and Quality
  • Hardware and Architecture

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    Alsop, J., Sinclair, M. D., Komuravelli, R., & Adve, S. V. (2016). GSI: A GPU Stall Inspector to characterize the sources of memory stalls for tightly coupled GPUs. In ISPASS 2016 - International Symposium on Performance Analysis of Systems and Software (pp. 172-182). [7482092] (ISPASS 2016 - International Symposium on Performance Analysis of Systems and Software). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISPASS.2016.7482092