Multicore platforms for scientific computing: cell BE and NVIDIA tesla. Fernández, J., Acacio, M., E., Bernabé, G., Abellán, J., L., & Franco, J. Proceedings of the 2008 International Conference on Scientific Computing, CSC 2008, 2008.
abstract   bibtex   
There are two multicore platforms that are currently concentrating an enormous attention due to their tremendous potential in terms of sustained performance: the Cell Broadband Engine (Cell BE from now on) and the NVIDIA Tesla computing solutions. The former is a recent heterogeneous chip-multiprocessor (CMP) architecture jointly developed by IBM, Sony and Toshiba to offer very high performance, especially on game and multimedia applications. In fact, it is the heart of the PlayStation 3. The latter are general-purpose GPUs (GPGPU) used as data-parallel computing devices based on the Computed Unified Device Architecture (CUDA) common to the latest NVIDIA GPUs. The common denominator is a multicore platform which provides an enormous potential performance benefit driven by a non-traditional programming model. In this paper we try to provide some insight into the peculiarities of both, as regards their cost, performance, programmability and limitations, in order to target scientific computing.
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 title = {Multicore platforms for scientific computing: cell BE and NVIDIA tesla},
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 year = {2008},
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 keywords = {CUDA,Cell BE,Multicore,NVIDIA Tesla,Parallel programming},
 pages = {167-173},
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 abstract = {There are two multicore platforms that are currently concentrating an enormous attention due to their tremendous potential in terms of sustained performance: the Cell Broadband Engine (Cell BE from now on) and the NVIDIA Tesla computing solutions. The former is a recent heterogeneous chip-multiprocessor (CMP) architecture jointly developed by IBM, Sony and Toshiba to offer very high performance, especially on game and multimedia applications. In fact, it is the heart of the PlayStation 3. The latter are general-purpose GPUs (GPGPU) used as data-parallel computing devices based on the Computed Unified Device Architecture (CUDA) common to the latest NVIDIA GPUs. The common denominator is a multicore platform which provides an enormous potential performance benefit driven by a non-traditional programming model. In this paper we try to provide some insight into the peculiarities of both, as regards their cost, performance, programmability and limitations, in order to target scientific computing.},
 bibtype = {article},
 author = {Fernández, J. and Acacio, M. E. and Bernabé, G. and Abellán, J. L. and Franco, J.},
 journal = {Proceedings of the 2008 International Conference on Scientific Computing, CSC 2008}
}
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