PoLAPACK: Parallel factorization routines with algorithmic blocking LU, QR, and Cholesky factorizations are the most widely used methods for solving dense linear systems of equations, and have been extensively studied and implemented on vector and parallel computers. Most of these factorization routines are implemented with block-partitioned algorithms in order to perform matrix-matrix operations, that is, to obtain the highest performance by maximizing reuse of data in the upper levels of memory, such as cache. Since parallel computers have different performance ratios of computation and communication, the optimal computational block sizes are different from one another in order to generate the maximum performance of an algorithm. Therefore, the data matrix should be distributed with the machine specific optimal block size before the computation. Two small or large a block size makes achieving good performance on a machine nearly impossible. In such a case, getting a better performance may require a complete redistribution of the data matrix. We present parallel LU, QR, and Cholesky factorization routines with an `algorithmic blocking’ on two-dimensional block cyclic data distribution. With the algorithmic blocking, it is possible to obtain the near optimal performance irrespective of the physical block size. The routines are implemented on the Intel Paragon and the SGI/Cray T3E and compared with the corresponding ScaLAPACK factorization routines
References in zbMATH (referenced in 1 article , 1 standard article )
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