Experiences of the GPU Thread Configuration and Shared Memory

Nowadays, GPU processors are widely used for general-purpose parallel computation applications. In the GPU programming, thread and block configuration is one of the most important decisions to be made, which increases parallelism and hides instruction latency. However, in many cases, it is often difficult to have sufficient parallelism to hide all the latencies, where the high latencies are often caused by the global memory accesses. In order to reduce the number of  those accesses, the shared memory is instead used which is  much faster than the global memory being located on a chip. The performance of the proposed thread configuration is evaluated on the GPU 960 processor. The experimental result shows that the best configuration improves the performance by 7.3 times compared to the worst configuration in the experiment. The experiences are also discussed for the shared memory performance when compared to that of the global memory.