high performance computing on graphics processing units: hgpu.org

Developing on highly parallel architectures is hard, time consuming, error prone, takes a lot of developers’ focus and effort to producing a production quality application. This is counter productive and results are unknown in advance whether it is worth it to go through such experience. In this work, we will take a complete overview of prototyping the parallelization of an application from sequential to multi core and GPU architectures, with focus on GPUs. This is in an effort to find a more developer friendly means of achieving said goals. In this project report, we are sharing our experience and results for the efforts trying to find a faster way to program the prevalent accelerator-devices by porting a benchmark from the benchmark suite (PARSEC). Both multicore CPUs and GPUs are prevalent architectures in both HPC, Supercomputing, regular mainstream computers and even mobile devices such as phones and tablets. These architectures are used in almost every possible workload. It is important for every one’s computing experience and/or investment to utilize such hardware. The difficulty with massively parallel programming is that they are not easy to get right within the often allowable time frame neither there is good enough support to program them in a more modular approach. During our efforts to port this benchmark, we try to asses and decide which framework(s) can best be the fastest way to harness such processing power if not for production quality applications, then for prototyping such applications. This is important to make sure of correct behavior and results out of such programs as well as assuring the worthiness of parallelizing them and finding subtle issues that are not known but at the time of development earlier in time. Documenting findings and a proposed workflow using the framework(s) of choice to facilitate the adoption of such high performance devices is then shared.