A comparative study of black‐box optimization heuristics for online tuning of high performance computing I/O accelerators

Citations Over TimeTop 18% of 2021 papers

Abstract

Summary High performance computing (HPC) applications' behaviors rely on highly configurable software environments and hardware devices. Finding their optimal parametrization is a complex task, as the size of their parametric space and the non‐linear behavior of HPC systems make hand‐tuning, theoretical modeling or exhaustive sampling unsuitable in most cases. In this article, we propose an online auto‐tuner that relies on black‐box optimization to find the optimal parametrization of input/output (I/O) accelerators for a given HPC application in a limited number of iterations, without making any assumption on the behavior of the tuned system. As many heuristics are available in the literature, we need to guarantee the quality of the tuning by selecting the most appropriate one. To do so, we provide a comparative study of the efficiency of three heuristics applied to tuning two I/O accelerators developed by the Atos company: a pure software accelerator (small read optimizer) and a mixed hardware‐software one (smart burst buffer). To select the most efficient heuristic for our use case, we define several new metrics to evaluate the quality of an auto‐tuner, in an online and offline settings. We find that genetic algorithms provide a faster convergence rate and a faster computation time but surrogate models provide a better score in terms of both distance to the optimum and trajectory stability. Overall, the obtained results show that auto‐tuning heuristics improve the execution time of applications used conjointly with both SRO and SBB accelerators.

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