In the last decade, the development in computer architectures has strongly influenced and motivated the evolution of algorithms for large-scale scientific computing. The unifying theme of the parallel algorithm group in CERFACS is the exploitation of vector and parallel computers in the solution of large-scale problems arising in computational science and engineering. The choice of a portable approach often leads to a loss in the average performance per computer with respect to a machine dependent implementation of the code. However, we show that, in full linear algebra as well as in sparse linear algebra, efficiency and portability can be combined. To illustrate our approach, we discuss results obtained on a wide range of shared memory multiprocessors including the Alliant FX/80, the IBM 3090E/3VF, the IBM 3090J/6VF, the CRAY-2, and the CRAY Y-MP.
Gaussian eliminationSparse linear equationsMultifrontal methodVectorizationParallelizationElimination tree