high performance computing on graphics processing units: hgpu.org

With the growing interest in multicore processors and Graphics Processing Units (GPUs), heterogeneous computing is an emerging necessity to fully utilize computing resources. In the traditional approach to parallel programming, programmers are required to manage the sequential part of a program while rewriting the parallel part to a more efficient parallel programming model. Our evaluation across several benchmarks suggests that there are cases for various problem sizes and system configurations that are challenging even for an expert parallel programmer to adapt (i.e., predict which architecture will perform better without actually executing the program). This paper presents a new approach to model parallel programs such that programmers can more easily separate the parallel part of a program from its sequential part. The programmer is given the flexibility to rewrite the parallel part using the parallel programming model of his/her choice. We provide support for merging the parallel code with the existing sequential code. This allows the programmer to focus on the parallel nature of the program and also offers the flexibility of switching between parallel libraries based on the platform and the specific problem context. As an experimental assessment of our approach, we identified and studied 58 parallel programs written by expert programmers.