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|Title||Massively Parallel Automated Software Tuning|
|Publication Type||Conference Paper|
|Year of Publication||2019|
|Authors||Kurzak, J., Y. Tsai, M. Gates, A. Abdelfattah, and J. Dongarra|
|Conference Name||48th International Conference on Parallel Processing (ICPP 2019)|
|Conference Location||Kyoto, Japan|
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.