A script-based autotuning compiler system to generate high-performance CUDA code

Abstract

This article presents a novel compiler framework for CUDA code generation. The compiler structure is designed to support autotuning , which employs empirical techniques to evaluate a set of alternative mappings of computation kernels and select the mapping that obtains the best performance. This article introduces a Transformation Strategy Generator , a meta-optimizer that generates a set of transformation recipes , which are descriptions of the mapping of the sequential code to parallel CUDA code. These recipes comprise a search space of possible implementations. This system achieves performance comparable and sometimes better than manually tuned libraries and exceeds the performance of a state-of-the-art GPU compiler.