high performance computing on graphics processing units: hgpu.org

We are currently faced with the situation where applications have increasing computational demands and there is a wide selection of parallel processor systems. In this paper we focus on exploiting fine-grain parallelism for a demanding bioinformatics application – MrBayes – and its phylogenetic likelihood functions (PLF) using different architectures. Our experiments compare side-by-side the scalability and performance achieved using general-purpose multi-core processors, the cell/BE, and graphics processor units (GPU). The results indicate that all processors scale well for larger computation and data sets. Also, GPU and Cell/BE processors achieve the best improvement for the parallel code section. Nevertheless, data transfers and the execution of the serial portion of the code are the reasons for their poor overall performance. The general-purpose multi-core processors prove to be simpler to program and provide the best balance between an efficient parallel and serial execution, resulting in the largest speedup.