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Adaptive Winograd's matrix multiplications
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Journal of Computational Physics
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PARA'06 Proceedings of the 8th international conference on Applied parallel computing: state of the art in scientific computing
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Exact solutions to linear systems of equations using output sensitive lifting
ACM Communications in Computer Algebra
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High performance BLAS formulation of the adaptive Fast Multipole Method
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High-performance dynamic quantum clustering on graphics processors
Journal of Computational Physics
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ACM Transactions on Architecture and Code Optimization (TACO) - Special Issue on High-Performance Embedded Architectures and Compilers
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Scaling LAPACK panel operations using parallel cache assignment
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The Basic Linear Algebra Subprograms (BLAS) define one of the most heavily used performance-critical APIs in scientific computing today. It has long been understood that the most important of these routines, the dense Level 3 BLAS, may be written efficiently given a highly optimized general matrix multiply routine. In this paper, however, we show that an even larger set of operations can be efficiently maintained using a much simpler matrix multiply kernel. Indeed, this is how our own project, ATLAS (which provides one of the most widely used BLAS implementations in use today), supports a large variety of performance-critical routines. Copyright © 2004 John Wiley & Sons, Ltd.