Effect of Instruction Fetch and Memory Scheduling on GPU Performance

GPUs are massively multithreaded architectures designed to exploit data level parallelism in applications. Instruction fetch and memory system are two key components in the design of a GPU. In this paper we study the effect of fetch policy and memory system on the performance of a GPU kernel. We vary the fetch and memory scheduling policies and analyze the performance of GPU kernels. As part of our analysis we categorize applications as symmetric and asymmetric based on the instruction lengths of warps. Our analysis shows that for symmetric applications, fairness based fetch and DRAM policies can improve performance. However, asymmetric applications require more sophisticated policies.