high performance computing on graphics processing units: hgpu.org

Significant performance gains can be achieved by using hardware architectures that integrate GPUs with conventional CPUs to form a hybrid and highly parallel computational engine. However, programming these novel architectures is tedious and error prone, reducing their ease of acceptance in an even wider range of computationally intensive applications. In this paper we discuss a refactoring technique, called Extract Kernel that transforms a loop written in C into a parallel function that uses NVIDIA’s CUDA framework to execute on a GPU. The selected approach and the challenges encountered are described, as well as some early results that demonstrate the potential of this refactoring.