ICL Research Profile



Matrix Algebra on GPU and Multicore Architectures (MAGMA) is a collection of next generation linear algebra (LA) libraries for heterogeneous architectures. The MAGMA package supports interfaces for current LA packages and standards, e.g., LAPACK and BLAS, to allow computational scientists to easily port any LA-reliant software components to heterogeneous architectures. MAGMA allows applications to fully exploit the power of current heterogeneous systems of multi/many-core CPUs and multi-GPUs/coprocessors to deliver the fastest possible time to accurate solution within given energy constraints.

MAGMA 1.6 features top performance and high accuracy LAPACK compliant routines for multicore CPUs enhanced with NVIDIA GPUs and includes more than 400 routines, covering one-sided dense matrix factorizations and solvers, two-sided factorizations and eigen/singular-value problem solvers, as well as a subset of highly optimized BLAS for GPUs. In 2014, the MAGMA Sparse and MAGMA Batched packages were added with the MAGMA 1.6 release, providing support for sparse iterative and batched linear algebra on a set of small matrices in parallel, respectively. MAGMA provides multiple precision arithmetic support (S/D/C/Z, including mixed-precision). Most of the algorithms are hybrid, using both multicore CPUs and GPUs, but starting with the 1.6 release, GPU-specific algorithms were added. MAGMA also supports AMD GPUs (clMAGMA 1.3) and Intel Xeon Phi coprocessors (MAGMA MIC 1.3).

Find out more at http://icl.eecs.utk.edu/magma/

In Collaboration With

  1. Inria
  2. King Abdullah University of Science and Technology
  3. University of California, Berkeley
  4. University of Colorado Denver

Sponsored by

  1. Cray
  2. National Science Foundation
  3. The United States Department of Defense
  4. The United States Department of Energy

With Support From

  1. AMD
  2. Intel
  3. MathWorks