Opportunistic computing in GPU architectures

Ashutosh Pattnaik, Xulong Tang, Onur Kayiran, Adwait Jog, Asit Mishra, Mahmut T. Kandemir, Anand Sivasubramaniam, Chita R. Das

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

10 Scopus citations

Abstract

Data transfer overhead between computing cores and memory hierarchy has been a persistent issue for von Neumann architectures and the problem has only become more challenging with the emergence of manycore systems. A conceptually powerful approach to mitigate this overhead is to bring the computation closer to data, known as Near Data Computing (NDC). Recently, NDC has been investigated in different flavors for CPU-based multicores, while the GPU domain has received little attention. In this paper, we present a novel NDC solution for GPU architectures with the objective of minimizing on-chip data transfer between the computing cores and Last-Level Cache (LLC). To achieve this, we first identify frequently occurring Load-Compute-Store instruction chains in GPU applications. These chains, when offloaded to a compute unit closer to where the data resides, can significantly reduce data movement. We develop two offloading techniques, called LLC-Compute and Omni-Compute. The first technique, LLC-Compute, augments the LLCs with computational hardware for handling the computation offloaded to them. The second technique (Omni-Compute) employs simple bookkeeping hardware to enable GPU cores to compute instructions offloaded by other GPU cores. Our experimental evaluations on nine GPGPU workloads indicate that the LLC-Compute technique provides, on an average, 19% performance improvement (IPC), 11% performance/watt improvement, and 29% reduction in on-chip data movement compared to the baseline GPU design. The Omni-Compute design boosts these benefits to 31%, 16% and 44%, respectively.

Original languageEnglish (US)
Title of host publicationISCA 2019 - Proceedings of the 2019 46th International Symposium on Computer Architecture
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages210-223
Number of pages14
ISBN (Electronic)9781450366694
DOIs
StatePublished - Jun 22 2019
Event46th International Symposium on Computer Architecture, ISCA 2019 - Phoenix, United States
Duration: Jun 22 2019Jun 26 2019

Publication series

NameProceedings - International Symposium on Computer Architecture
ISSN (Print)1063-6897

Conference

Conference46th International Symposium on Computer Architecture, ISCA 2019
CountryUnited States
CityPhoenix
Period6/22/196/26/19

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture

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    Pattnaik, A., Tang, X., Kayiran, O., Jog, A., Mishra, A., Kandemir, M. T., Sivasubramaniam, A., & Das, C. R. (2019). Opportunistic computing in GPU architectures. In ISCA 2019 - Proceedings of the 2019 46th International Symposium on Computer Architecture (pp. 210-223). (Proceedings - International Symposium on Computer Architecture). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1145/3307650.3322212