GPU Memory Optimization Through Program Restructuring Methods
Graphic processing units attract more and more scientific computing due to their high performance/cost ratio.Compared to the graphical applications,there are complex data dependences and irregular data access patterns in scientific computing programs.Therefore,we propose GPU-oriented program restructuring methods.By computation restructuring,we enhance the parallelism and the compute intensity of the programs,improving the ALU resources utilization on GPU.By data restructuring,we eliminate the irregular data access patterns,using vector data type to improve the available memory bandwidth of the programs.Experimental results show that the proposed optimization methods can decrease the execution time of the scientific computing programs on GPU and achieve 1.17～8.91 times speedup.