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LTRF: Enabling High-Capacity Register Files for GPUs via Hardware/Software Cooperative Register Prefetching

Mohammad Sadrosadati,Amirhossein Mirhosseini,Seyed Borna Ehsani,H. Sarbazi-Azad,M. Drumond,B. Falsafi,Rachata Ausavarungnirun,O. Mutlu

Published 2018 in International Conference on Architectural Support for Programming Languages and Operating Systems

ABSTRACT

Graphics Processing Units (GPUs) employ large register files to accommodate all active threads and accelerate context switching. Unfortunately, register files are a scalability bottleneck for future GPUs due to long access latency, high power consumption, and large silicon area provisioning. Prior work proposes hierarchical register file, to reduce the register file power consumption by caching registers in a smaller register file cache. Unfortunately, this approach does not improve register access latency due to the low hit rate in the register file cache. In this paper, we propose the Latency-Tolerant Register File (LTRF) architecture to achieve low latency in a two-level hierarchical structure while keeping power consumption low. We observe that compile-time interval analysis enables us to divide GPU program execution into intervals with an accurate estimate of a warp's aggregate register working-set within each interval. The key idea of LTRF is to prefetch the estimated register working-set from the main register file to the register file cache under software control, at the beginning of each interval, and overlap the prefetch latency with the execution of other warps. Our experimental results show that LTRF enables high-capacity yet long-latency main GPU register files, paving the way for various optimizations. As an example optimization, we implement the main register file with emerging high-density high-latency memory technologies, enabling 8X larger capacity and improving overall GPU performance by 31% while reducing register file power consumption by 46%.

PUBLICATION RECORD

  • Publication year

    2018

  • Venue

    International Conference on Architectural Support for Programming Languages and Operating Systems

  • Publication date

    2018-03-19

  • Fields of study

    Computer Science, Engineering

  • Identifiers
    DOI 10.1145/3173162.3173211
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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