Scientific Computing Kernels on the Cell Processor
Scientific Computing Kernels on the Cell Processor Samuel Williams, John Shalf, Leonid Oliker Shoaib Kamil, Parry Husbands, Katherine Yelick Computational Research Division Lawrence Berkeley National Laboratory Berkeley, CA 94720 {swwilliams,jshalf,loliker,sakamil,pjrhusbands,kayelick}@lbl.gov ABSTRACT architectures that provide high performance on scientific ap- The slowing pace of commodity microprocessor performance plications, yet have a healthy market outside the scientific improvements combined with ever-increasing chip power de- community. In this work, we examine the potential of the mands has become of utmost concern to computational sci- recently-released STI Cell processor as a building block for entists. As a result, the high performance computing com- future high-end computing systems, by investigating perfor- munity is examining alternative architectures that address mance across several key scientific computing kernels: dense the limitations of modern cache-based designs. In this work, matrix multiply, sparse matrix vector multiply, stencil com- we examine the potential of using the recently-released STI putations on regular grids, as well as 1D and 2D FFTs. Cell processor as a building block for future high-end com- Cell combines the considerable floating point resources re- puting systems. Our work contains several novel contribu- quired for demanding numerical algorithms with a power- tions. First, we introduce a performance model for Cell and efficient software-controlled memory hierarchy. Despite its apply it to several key scientific computing kernels: dense radical departure from previous mainstream/commodity pro- matrix multiply, sparse matrix vector multiply, stencil com- cessor designs, Cell is particularly compelling because it putations, and 1D/2D FFTs. The difficulty of programming will be produced at such high volumes that it will be cost- Cell, which requires assembly level intrinsics for the best competitive with commodity CPUs.
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