Performance hotspot based CUDA acceleration

With the introduction of many-core GPUs, there is widespread interest in using GPUs to accelerate non-graphics applications such as bioinformatics, energy, finance and several research areas. Even though the GPUs provide highly parallel processing capability, performance improvement is not always achievable due to multiple reasons. One of them will be the application mapping to the hardware acceleration module. In this paper, we investigate performance hotspot functions of an application and used it for application mapping for CUDA acceleration. Based on our experiments with three non-graphic applications, hotspot function based CUDA acceleration shows 15%-40% performance improvement on GPGPU with minimal efforts.