A Hybridization Methodology for High-Performance Linear Algebra Software for GPUs
Emmanuel Agullo, Cédric Augonnet, Jack Dongarra, Hatem Ltaief, Raymond Namyst, Samuel Thibault and Stanimire Tomov
In this chapter, we present a hybridization methodology for the development of high-performance linear algebra software for GPUs. The methodology has been successfully used in MAGMA — a new generation of linear algebra libraries, similar in functionality to LAPACK, but extended for hybrid, GPU-based systems. Algorithms of interest are split into computational tasks. The tasks’ execution is scheduled over the computational components of a hybrid system of multicore CPUs with GPU accelerators using StarPU — a runtime system for accelerator-based ...
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