Exploration of automatic optimisation for CUDA programming

Abstract

© 2014 Taylor & Francis. Writing optimised compute unified device architecture (CUDA) program for graphic processing units (GPUs) is complex even for experts. We present a design methodology for a restructuring tool that converts C-loops into optimised CUDA kernels based on a three-step algorithm which are loop tiling, coalesced memory access and resource optimisation. A method for finding possible loop tiling solutions with coalesced memory access is developed and a simplified algorithm for restructuring C-loops into an efficient CUDA kernel is presented. In the evaluation, we implement matrix multiply (MM), matrix transpose (M-transpose), matrix scaling (M-scaling) and matrix vector multiply (MV) using the proposed algorithm. We present the analysis of the execution time and GPU throughput for the above applications, which favourably compare to other proposals. Evaluation is carried out while scaling the problem size and running under a variety of kernel configurations. The obtained speedup is about 28-35% for M-transpose compared to NVIDIA Software Development Kit, 33% speedup for MV compared to general purpose computation on graphics processing unit compiler, and more than 80% speedup for MM and M-scaling compared to CUDA-lite.