Scientific Computing Kernels on the Cell Processor

Journal Article · Wed Apr 04 04:00:00 EDT 2007 · International Journal of Parallel Programming

DOI:https://doi.org/10.1007/s10766-007-0034-5· OSTI ID:927340

Williams, Samuel W; Shalf, John; Oliker, Leonid; Kamil, Shoaib; Husbands, Parry; Yelick, Katherine

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 using the recently-released STI Cell processor as a building block for future high-end computing systems. Our work contains several novel contributions. First, we introduce a performance model for Cell and apply it to several key scientific computing kernels: dense matrix multiply, sparse matrix vector multiply, stencil computations, and 1D/2D FFTs. The difficulty of programming Cell, which requires assembly level intrinsics for the best performance, makes this model useful as an initial step in algorithm design and evaluation. Next, we validate the accuracy of our model by comparing results against published hardware results, as well as our own implementations on a 3.2GHz Cell blade. Additionally, we compare Cell performance to benchmarks run on leading superscalar (AMD Opteron), VLIW (Intel Itanium2), and vector (Cray X1E) architectures. Our work also explores several different mappings of the kernels and demonstrates a simple and effective programming model for Cell's unique architecture. Finally, 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: Ernest Orlando Lawrence Berkeley NationalLaboratory, Berkeley, CA (US)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC02-05CH11231
• OSTI ID: 927340
• Report Number(s): LBNL--63186; BnR: YN0100000
• Journal Information: International Journal of Parallel Programming, Journal Name: International Journal of Parallel Programming Journal Issue: 3 Vol. 35; ISSN IJPPE5; ISSN 0885-7458
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS
ACCURACY
ALGORITHMS
ARCHITECTURE
BENCHMARKS
DESIGN
EFFICIENCY
EVALUATION
KERNELS
MICROPROCESSORS
MODIFICATIONS
PERFORMANCE
POWER DEMAND
PROGRAMMING
VECTORS