The Potential of the Cell Processor for Scientific Computing
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 forth coming 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 (IntelItanium2), 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 Cells 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.
- Research Organization:
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- USDOE Director, Office of Science
- DOE Contract Number:
- DE-AC02-05CH11231
- OSTI ID:
- 883789
- Report Number(s):
- LBNL-59071; R&D Project: KL0503; BnR: YN0100000; TRN: US200615%%246
- Resource Relation:
- Conference: International Parallel&DistributedProcessing Symposium - 2006, Rhodes Island, Greece, April 25 - 29,2006
- Country of Publication:
- United States
- Language:
- English
Similar Records
Leading Computational Methods on Scalar and Vector HEC Platforms
Scientific Application Performance on Leading Scalar and VectorSupercomputing Platforms