© 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.
|Original language||English (US)|
|Number of pages||16|
|Journal||International Journal of Parallel, Emergent and Distributed Systems|
|State||Published - Sep 16 2014|
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledgements: The authors would like to acknowledge the support provided by King Abdulaziz City for Science and Technology (KACST) through the Science & Technology Unit at King Fahd University of Petroleum & Minerals (KFUPM) for funding this work through project No. 12-INF3008-04 as part of the National Science, Technology and Innovation Plan. Thanks to the Department of Information and Computer Science (ICS), King Fahd University of Petroleum and Minerals (KFUPM), and King Abdullah University of Science and Technology (KAUST) for giving access to their computing facilities.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.