Matrix Algebra on GPU: An interview with MAGMA lead developers

[ Thanks to Edwood
Ocasio for this link. ]

“Jack: MAGMA (Matrix Algebra on GPU and Multicore Architectures)
is a collection of next generation linear algebra (LA) libraries
designed and implemented by the team that developed LAPACK and
ScaLAPACK.

“MAGMA is for heterogeneous GPU-based architectures. It supports
interfaces to current LA packages and standards, e.g., LAPACK and
BLAS, to allow computational scientists to effortlessly port any
LA-relying software components.

“The main benefits of using MAGMA are that MAGMA can enable
applications to fully exploit the power of current heterogeneous
systems of multi/manycore CPUs and multiGPUs, and deliver the
fastest possible time to an accurate solution within given energy
constraints.

“By combining the strengths of different architectures, MAGMA
overcomes bottlenecks associated with just multicore or GPUs, to
significantly outperform corresponding packages for any of these
homogeneous components taken separately. MAGMA’s one-sided
factorizations (and linear solvers) on a single Fermi GPU (and a
basic CPU host) can outperform state-of-the-art CPU libraries on
high-end multi-socket, multicore nodes (e.g., using up to 48 modern
cores). The benefits for the two-sided factorizations (bases for
eigenproblem and SVD solvers) are even greater, as the performance
can exceed 10X the performance of systems with 48 modern CPU cores.
Architecture-specific performances and comparisons can be found
through the MAGMA site.”

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