Value Prediction and Speculative Execution on GPU |
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Authors: | Shaoshan Liu Christine Eisenbeis Jean-Luc Gaudiot |
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Affiliation: | 1.Microsoft,Redmond,USA;2.Alchemy team, INRIA Saclay - ?le-de-France & Univ Paris-Sud 11 (LRI, UMR CNRS 8623),Orsay,France;3.University of California,Irvine,USA |
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Abstract: | GPUs and CPUs have fundamentally different architectures. It is conventional wisdom that GPUs can accelerate only those applications
that exhibit very high parallelism, especially vector parallelism such as image processing. In this paper, we explore the
possibility of using GPUs for value prediction and speculative execution: we implement software value prediction techniques
to accelerate programs with limited parallelism, and software speculation techniques to accelerate programs that contain runtime
parallelism, which are hard to parallelize statically. Our experiment results show that due to the relatively high overhead,
mapping software value prediction techniques on existing GPUs may not bring any immediate performance gain. On the other hand,
although software speculation techniques introduce some overhead as well, mapping these techniques to existing GPUs can already
bring some performance gain over CPU. Based on these observations, we explore the hardware implementation of speculative execution
operations on GPU architectures to reduce the software performance overheads. The results indicate that the hardware extensions
result in almost tenfold reduction of the control divergent sequential operations with only moderate hardware (5–8%) and power
consumption (1–5%) overheads. |
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