Fine-grain parallelism using multi-core, Cell/BE, and GPU Systems

Currently, we are facing a situation where applications exhibit increasing computational demands and where a large variety of parallel processor systems are available. In this paper we focus on exploiting fine-grain parallelism for three applications with distinct characteristics: a Bioinformatics application (MrBayes), a Molecular Dynamics application (NAMD), and a database application (TPC-H). We assess, side-by-side, the performance of the three applications on general-purpose multi-core processors, the Cell Broadband Engine (Cell/BE), and Graphics Processing Units (GPU). Our results indicate that application performance depends on the characteristics of the parallel architectures and on the computational requirements of the core functions of the respective applications. For MrBayes the best overall performance is achieved on general-purpose multi-core processors, for NAMD on the Cell/BE, and for TPC-H on GPUs.

► Multi-core architectures with distinct characteristics fit different application types.
► Applications with large memory-bandwidth-to-computation ratio benefit from caches.
► Applications with large computation-to-memory-bandwidth ratio benefit from streaming
► Multi-core should have heterogeneous cores and/or adaptive/reconfigurable ability.