The Implementation of a High Performance GPGPU Compiler
In this paper we present our experience in developing an optimizing compiler for general purpose computation on graphics processing units (GPGPU) based on the Cetus compiler framework. The input to our compiler is a nave GPU kernel procedure, which is functionally correct but without any consideration for performance optimization. Our compiler applies a set of optimization techniques to the naive kernel and generates the optimized GPU kernel. Our compiler supports optimizations for GPU kernels using either global memory or texture memory. The implementation of our compiler is facilitated with a source-to-source compiler infrastructure, Cetus. The code transformation in the Cetus compiler framework is called a pass. We classify all the passes used in our work into two categories: functional passes and optimization passes. The functional passes translate input kernels into desired intermediate representation, which clearly represents memory access patterns and thread configurations. A series of optimization passes improve the performance of the kernels by adapting them to the target GPGPU architecture. Our experiments show that the optimized code achieves very high performance, either superior or very close to highly fine-tuned libraries.
gpu compiler optimization vectorization opencl