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 enables applications to fully exploit the power of current heterogeneous systems of many-core CPUs and multi-GPUs/coprocessors to deliver the fastest possible time to accurate solution within given energy constraints.
MAGMA 2.3 features LAPACK-compliant routines for multi-core CPUs enhanced with NVIDIA GPUs (including the Volta V100). MAGMA now 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. A MagmaDNN package was launched to provide high-performance data analytics, including functionalities for machine learning applications that use MAGMA as their computational backend. The MAGMA Sparse and MAGMA Batched packages were added with the MAGMA 1.6 release and continuously extended and improved with each release.
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