Accelerating Distributed-Memory Autotuning via Statistical Analysis of Execution Paths
Accelerating Distributed-Memory Autotuning via Statistical Analysis of Execution Paths
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DOI：
10.1109/ipdps49936.2021.00014
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发表时间：
2021-03
期刊：
影响因子：
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通讯作者：
Edward Hutter;Edgar Solomonik
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文献类型：
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作者：
Edward Hutter;Edgar Solomonik
规模上的自动绩效调整的禁止费用在很大程度上限制了对库的自动调整的使用，用于共享记忆和GPU架构。单个内核的性能（基准程序的子例程）。随后的调用避免了执行时间的预测模型。我们将此框架封装为新的分析工具Critter的一部分，该工具可自动化内核执行决策并沿关键执行路径传播统计配置文件。至7.1倍，预测精度为98％。
The prohibitive expense of automatic performance tuning at scale has largely limited the use of autotuning to libraries for shared-memory and GPU architectures. We introduce a framework for approximate autotuning that achieves a desired confidence in each algorithm configuration’s performance by constructing confidence intervals to describe the performance of individual kernels (subroutines of benchmarked programs). Once a kernel’s performance is deemed sufficiently predictable for a set of inputs, subsequent invocations are avoided and replaced with a predictive model of the execution time. We then leverage online execution path analysis to coordinate selective kernel execution and propagate each kernel’s statistical profile. This strategy is effective in the presence of frequently-recurring computation and communication kernels, which is characteristic to algorithms in numerical linear algebra. We encapsulate this framework as part of a new profiling tool, Critter, that automates kernel execution decisions and propagates statistical profiles along critical paths of execution. We evaluate performance prediction accuracy obtained by our selective execution methods using state-of-the-art distributed-memory implementations of Cholesky and QR factorization on Stampede2, and demonstrate speed-ups of up to 7.1x with 98% prediction accuracy.