A Conjugate Gradient Method for Systolic Architectures
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Significant numerical computations now commonly require vast                    processing power.  At the core of many of these problems is the                 solution of large, sparse systems of equations.  A host of                      specialized high-speed processors have been developed to attack                 large problems.  Systolic architectures have proven to be                       excellent candidates for a range of tasks, and they promise to                  continue as powerful computing tools for some time to come.  But,               without appropriate algorithm development, these specialized                    processors will never reach their full potential.  The most                     obvious of the missing systolic algorithms are the sparse matrix                methods, including the conjugate gradient techniques (the                       "technology" that is arguably the most important advance in large               scale scientific computing during the past decade and the one to                come).                                                                                                                                                          Phase I will develop a conjugate gradient method for systolic                   architectures.  Such a method will greatly aid the use of these                 architectures:  the machine cycles will be used more efficiently                as will the time of the scientists and engineers using them.                                                                                                    During Phase II the theoretical concepts will be extended to                    include nonsymmetric conjugate gradient methods and                             preconditioners.
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{'Code': '5371', 'Text': 'SBIR Phase I'}
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