We first expose in this memoir efficient matrix multiplication techniques. We set up new schedules that allow us to minimize the extra memory requirements during a Winograd-style matrix multiplication, while keeping the complexity competitive. In order to get them, we develop external tools (pebble game), tight complexity computations and new hybrid algorithms. Then we use parallel technologies (multicore CPU and GPU) in order to accelerate efficiently the sparse matrix--dense vector multiplication (SpMV), crucial to /blackbox/ algorithms and we set up new hybrid formats to store them. Finally, we establish generic design methods focusing on efficiency, especially via building block conceptions or self-optimization. We also propose tools for improving and standardizing code quality in order to make it more sustainable and more robust. This is in particular applied to the LinBox computer algebra library.
