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.
New for 2016, MAGMA 2.2 features LAPACK-compliant routines for multicore CPUs enhanced with NVIDIA GPUs, and includes more than 400 routines, covering one-sided dense matrix factorizations and solvers, and two-sided factorizations and eigen/singular-value problem solvers, as well as a subset of highly optimized BLAS for GPUs. Tuning and support are added for the newest NVIDIA Pascal P100 GPU. The MAGMA Sparse and MAGMA Batched packages were added with the MAGMA 1.6 release and continuously extended and improved since then. 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.
Find out more at http://icl.eecs.utk.edu/magma/
In Collaboration With
- King Abdullah University of Science and Technology
- University of California Berkeley
- University of Colorado Denver
With Support From