R. Vuduc, J. W. Demmel, K. A. Yelick, S. Kamil, R. Nishtala, and B. Lee. Performance optimizations and bounds for sparse matrix-vector multiply. In Proceedings of Supercomputing, Baltimore, MD, USA, November 2002. Home/Search Document Details and Download
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Self Adaptivity in Grid Computing - Vadhiyar, Dongarra (Correct)
....the computing environment is to build self adaptive software that studies the characteristics of the computing environments and chooses the software parameters to achieve high efficiency on the computing environment. Recently, a number of self adaptive software have been designed and implemented [13, 27, 24, 26, 7, 3]. Some of the software apply adaptivity to the computational processors [13, 27] some are tuned for communication networks [24] some are This work is supported in part by the National Science Foundation contract GRANT #EIA 9975020, SC #R36505 29200099 and GRANT #EIA 9975015 intended for ....
....contract GRANT #EIA 9975020, SC #R36505 29200099 and GRANT #EIA 9975015 intended for workstation clusters [7] and some have been developed for computational Grids [3] The adaptive software also differ in terms of the time when adaptivity is performed. Some perform adaptivity at installation time [27,24, 26] while some perform adaptivity at the run time [7, 3] There are very few self adaptive software that dynamically adapts to changes in the load characteristics of the resources on computational Grids. Computa tional Grids [11] involve large system dynamics that the ability to migrate ex ecuting ....
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R. Vuduc, J. W. Demmel, K. A. Yelick, S. Kamil, R. Nishtala, and B. Lee. Performance Optimizations and Bounds for Sparse Matrix-Vector Multiply. In Proceedings of Supercomputing, Baltimore, MD, USA, November 2002.
Performance Modeling and Analysis of Cache Blocking.. - Nishtala, Vuduc.. (2004) Self-citation (Vuduc Demmel Yelick Nishtala) (Correct)
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R. Vuduc, J. W. Demmel, K. A. Yelick, S. Kamil, R. Nishtala, and B. Lee. Performance optimizations and bounds for sparse matrix-vector multiply. In Proceedings of Supercomputing, Baltimore, MD, USA, November 2002.
Automatic Performance Tuning and Analysis of Sparse .. - Vuduc, Kamil, Hsu, .. (2002) Self-citation (Vuduc Demmel Yelick Kamil Nishtala) (Correct)
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R. Vuduc, J. W. Demmel, K. A. Yelick, S. Kamil, R. Nishtala, and B. Lee. Performance optimizations and bounds for sparse matrix-vector multiply. In Proceedings of Supercomputing, Baltimore, MD, 2002. (To appear.).
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R. Vuduc, J. W. Demmel, K. A. Yelick, S. Kamil, R. Nishtala, and B. Lee. Performance optimizations and bounds for sparse matrix-vector multiply. In Proceedings of Supercomputing, Baltimore, MD, USA, November 2002.
Performance Modeling and Analysis of Cache Blocking.. - Nishtala, Vuduc.. (2004) Self-citation (Vuduc Demmel Yelick Nishtala) (Correct)
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R. Vuduc, J. W. Demmel, K. A. Yelick, S. Kamil, R. Nishtala, and B. Lee. Performance optimizations and bounds for sparse matrix-vector multiply. In Proceedings of Supercomputing, Baltimore, MD, USA, November 2002.
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R. Vuduc, J. W. Demmel, K. A. Yelick, S. Kamil, R. Nishtala, and B. Lee. Performance optimizations and bounds for sparse matrix-vector multiply. In Proceedings of Supercomputing, Baltimore, MD, USA, November 2002.
Automatic Performance Tuning and Analysis of Sparse .. - Vuduc, Kamil, Hsu, .. (2002) Self-citation (Vuduc Demmel Yelick Kamil Nishtala) (Correct)
....sparse data structures. Consequently, it is not unusual to see kernels such as SpTS run at under 10 of peak uniprocessor floating point performance. Our approach to automatic tuning of SpTS builds on prior experience with building tuning systems for sparse matrix vector multiply (SpMV) [21, 22, 40], and dense matrix kernels [8, 41] In particular, we adopt the two step methodology of previous approaches: 1) we identify and generate a set of reasonable candidate implementations, and (2) search this set for the fastest implementation by some combination of performance modeling and actually ....
....furthermore, we observe speedups of up to 1.8x when both register blocking and switch to dense optimizations are applied. We also present preliminary results confirming that our heuristics choose reasonable values for the tuning parameters. These results support our prior findings with SpMV [40], suggesting two new directions for performance enhancements: 1) the use of higher level matrix structures (e.g. matrix reordering and multiple register block sizes) and (2) optimizing kernels with more opportunities for data reuse (e.g. multiplication and solve with multiple vectors, ....
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R. Vuduc, J. W. Demmel, K. A. Yelick, S. Kamil, R. Nishtala, and B. Lee. Performance optimizations and bounds for sparse matrix-vector multiply. In Proceedings of Supercomputing, Baltimore, MD, 2002. (To appear.).
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R. Vuduc, J. W. Demmel, K. A. Yelick, S. Kamil, R. Nishtala, and B. Lee. Performance optimizations and bounds for sparse matrix-vector multiply. In Proceedings of the 2002.
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Richard Vuduc, James W. Demmel, Katherine A. Yelick, Shoaib Kamil, Rajesh Nishtala, and Benjamin Lee. Performance optimizations and bounds for sparse matrix-vector multiply. In Proceedings of the 2002.
Optimizing Sparse Matrix-Vector Product Computations.. - Mellor-Crummey, Garvin (2003) (Correct)
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R. Vuduc, J. Demmel, K. Yelick, S. Kamil, R. Nishtala, and B. Lee. Performance optimizations and bounds for sparse matrix-vector multiply. In Proceedings of SC'02: High Performance Networking and Computing, Nov. 2002.
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