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EECS Publication

Heterogeneous Acceleration for Linear Algebra in Mulit-Coprocessor Environments

Azzam Haidar and Piotr Luszczek and Stanimire Tomov and Jack Dongarra

We present an efficient and scalable programming model for the development of linear algebra in heterogeneous multi-coprocessor environments. The model incorporates some of the current best design and implementation practices for the heterogeneous acceleration of dense linear algebra (DLA). Examples are given as the basis for solving linear systems' algorithms { the LU, QR, and Cholesky factorizations. To generate the extreme level of parallelism needed for the efficient use of coprocessors, algorithms of interest are redesigned and then split into well-chosen computational tasks. The tasks execution is scheduled over the computational components of a hybrid system of multi-core CPUs and coprocessors using a light-weight runtime system. The use of light-weight runtime systems keeps scheduling overhead low, while enabling the expression of parallelism through otherwise sequential code. This simplifies the development eorts and allows the exploration of the unique strengths of the various hardware components.

Published  2014-02-14 05:00:00  as  ut-eecs-14-724 (ID:582)


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