Performance Modeling, Performance Tuning and Quantization for GPU Programs

Abstract

GPUs have played an important role in solving many scientific problems that range across different domains. Writing GPU programs might be easy, but writing them efficiently is much more difficult. To achieve the best performance, it is necessary that the compiler and runtime have advanced techniques to compile and run the program efficiently. These techniques should be transparent to the programmers and help them avoid the burden of having to know many details of the underlying architecture. Among the most important aspects that help improve the performance of a GPU program, we focus on the problem of performance modeling, performance tuning and quantization. Performance modeling estimates the execution time of the program and can be useful in analyzing the program characteristics or partitioning the workload in a heterogenous system. Performance tuning finds the optimal solution from an optimization space in a reasonable time. Quantization reduces the precision needed to execute the program without losing significant output accuracy. The proposed techniques can be integrated into GPU compilers and runtimes to help them be more efficient.