Evaluation of Cache-based Superscalar and Cacheless Vector Architectures for Scientific Computations
The growing gap between sustained and peak performance for s cientific applications is a well-known problem in high end computing. The recent development of par allel vector systems offers the potential to bridge this gap for many computational science codes and d eliver a substantial increase in computing capabilities. This paper examines the intranode perfor mance of the NEC SX-6 vector processor and the cache-based IBM Power3/4 superscalar architecture s ac oss a number of scientific computing areas. First, we present the performance of a microbenchmar k suite that examines low-level machine characteristics. Next, we study the behavior of the NAS Para llel Benchmarks. Finally, we evaluate the performance of several scientific computing codes. Results demonstrate that the SX-6 achieves high performance on a large fraction of our applications and ofte n significantly outperforms the cache-based architectures. However, certain applications are not easi ly menable to vectorization and would require extensive algorithm and implementation reengineering to u tilize the SX-6 effectively.