Modernizing and Evaluating the Autotuning Framework of SkePU 3

Autotuning is a method which enables a program to automatically choose the most suitable parameters that optimizes it for a certain goal e.g. speed, cost, etc. In this work autotuning is implemented in the context of the SkePU framework, in order to choose the best backend (CUDA, CPU, OpenCL, Hybrid) that would optimize a skeleton execution in terms of performance. SkePU is a framework that provides different algorithmic skeletons with implementations for the different backends (OpenCL, CUDA, OpenMP, CPU). Skeletons are parameterised with a user-provided per-element function which will run in parallel. This thesis shows how the autotuning of SkePU’s automatic backend selection for skeleton calls is implemented with respect to all the different parameters that a SkePU skeleton could have. The autotuning in this thesis is built upon the sampling technique, which is implemented by applying different combinations of sizes for the vector and matrix parameters to eventually generate an execution plan, which will be used as a lookup table when running the skeleton on all different backends. The execution plan will estimate the best performing backend for the sample. This work finally evaluates the implementation by comparing the results of running the autotuning on the different SkePU programs, to running the same programs without the autotuning