Lawrence Berkeley National Laboratory

The slowing pace of commodity microprocessor performance improvements combined with ever-increasing chip power demands has become of utmost concern to computational scientists. As a result, the high performance computing community is examining alternative architectures that address the limitations of modern cache-based designs. In this work, we examine the potential of the using the forthcoming STI Cell processor as a building block for future high-end computing systems. Our work contains several novel contributions. We are the first to present quantitative Cell performance data on scientific kernels and show direct comparisons against leading superscalar (AMD Opteron), VLIW (Intel Itanium2), and vector (Cray X1) architectures. Since neither Cell hardware nor cycle-accurate simulators are currently publicly available, we develop both analytical models and simulators to predict kernel performance. Our work also explores the complexity of mapping several important scientific algorithms onto the Cell s unique architecture. Additionally, we propose modest microarchitectural modifications that could significantly increase the efficiency of double-precision calculations. Overall results demonstrate the tremendous potential of the Cell architecture for scientific computations in terms of both raw performance and power efficiency.