high performance computing on graphics processing units: hgpu.org

As computing power of GPU increases dramatically, the GPU is widely used for general-purpose parallel applications as well as graphics applications. Especially, programmers using the GPU can easily create multiple threads with the help of APIs provided by GPU vendors. In GPU architecture, threads are grouped into a warp to run on the SIMD pipeline, leading to high performance. However, computational resources of GPU are not fully utilized in executing general-purpose applications due to control-flow instructions, resulting in performance degradation. To improve the GPU performance, several warp formations for handling branch divergence due to control-flow instructions have been proposed. In this work, we analyze the GPU performance according to warp formations with real GPU hardware configuration. Our simulation results show that the warp formation providing high hardware utilization does not guarantee high performance if hardware resources are not fully supported. Therefore, hardware configuration should be considered together with hardware utilization to improve the GPU performance by using warp formation.