PDL ABSTRACT

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

ASPLOS2018. The 23rd ACM International Conference on Architectural Support for Programming Languages and Operating Systems, March 24th – March 28th, Williamsburg, VA, USA.

Mohammad Sadrosadati1,2, Amirhossein Mirhosseini3, Seyed Borna Ehsani1, Hamid Sarbazi-Azad1,4, Mario Drumond5, Babak Falsafi5, Rachata Ausavarungnirun6, Onur Mutlu2,6

1Sharif University of Technology
2ETH Zürich
3University of Michigan
4IPM
5EPFL
6Carnegie Mellon University

http://www.pdl.cmu.edu/

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 8× larger capacity and improving overall GPU performance by 31% while reducing register file power consumption by 46%.

KEYWORDS: GPUs, Register File Design, Latency Tolerance, Energy Efficiency, Memory Technology, Memory Latency

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Last updated 15 October, 2018