MAGMA is a collection of next generation linear algebra (LA) GPU accelerated 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 it can
enable applications to fully exploit the power of current heterogeneous
systems of multi/manycore CPUs and multi-GPUs, and deliver the fastest
possible time to an accurate solution within given energy constraints.

Key Features

By combining the strengths of multicore and GPU architectures, MAGMA  significantly outperforms 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 eigen- and singular-value solvers) are even greater, as the performance can exceed 10X the performance of a system with 48 modern CPU cores. Architecture-specific performances and comparisons can be found through the MAGMA site.

Availability

MAGMA 1.0 is available as open software under the modified BSD license:

• http://icl.cs.utk.edu/magma/software

For more information about MAGMA and other CUDA Libraries:

• A paper of MAGMA by examples written by Andrzej Chrzeszczyk and Jakub Chrzeszczyk
• MAGMA home page at ICL, University of Tennesee
• CULA Tools by EM Photonics
• See other GPU Accelerated Libraries