high performance computing on graphics processing units: hgpu.org

Over the last decades parallel-distributed computing becomes most popular than traditional centralized computing. In distributed computing performance up-gradation is achieved by distributing workloads across the participating nodes. One of the most important factors for improving the performance of this type of system is to reduce average and standard deviation of job response time. Runtime insertion of new tasks of various sizes to different nodes is one of the main reasons of Load unbalancing. Among the several latest concepts of Parallel-Distributed Processing CPU-GPU Utilization is focused here. How the ideal portion of the CPU can be utilized for GPU process and visa-versa. This paper also introduces the heterogeneous computing work flow integration focused on CPU-GPU. The purposed system exploits the coarse-grain warp level parallelism. It is also elaborated here that by using which architectures and frameworks developers are racing in the field of heterogeneous computing.