Massively Parallel Automated Software Tuning

TitleMassively Parallel Automated Software Tuning
Publication TypeConference Paper
Year of Publication2019
AuthorsKurzak, J., Y. Tsai, M. Gates, A. Abdelfattah, and J. Dongarra
Conference Name48th International Conference on Parallel Processing (ICPP 2019)
Date Published2019-08
PublisherACM Press
Conference LocationKyoto, Japan
Abstract

This article presents an implementation of a distributed autotuning engine developed as part of the Bench-testing OpenN Software Autotuning Infrastructure project. The system is geared towards performance optimization of computational kernels for graphics processing units, and allows for the deployment of vast autotuning sweeps to massively parallel machines. The software implements dynamic work scheduling to distributed-memory resources and takes advantage of multithreading for parallel compilation and dispatches kernel launches to multiple accelerators. This paper lays out the main design principles of the system and discusses the basic mechanics of the initial implementation. Preliminary performance results are presented, encountered challenges are discussed, and the future directions are outlined.

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