Matrix Algebra for GPU and Multicore Architectures
Univ. of Tennessee, Knoxville
Univ. of California, Berkeley
Univ. of Colorado, Denver
The goal of the MAGMA project is to create a new generation of linear algebra libraries that achieves the fastest possible time to an accurate solution on heterogeneous architectures, starting with current multicore + multi-GPU systems. To address the complex challenges stemming from these systems' heterogeneity, massive parallelism, and the gap between compute speed and CPU-GPU communication speed, MAGMA's research is based on the idea that optimal software solutions will themselves have to hybridize, combining the strengths of different algorithms within a single framework. Building on this idea, the goal is to design linear algebra algorithms and frameworks for hybrid multicore and multi-GPU systems that can enable applications to fully exploit the power that each of the hybrid components offers.
Designed to be similar to LAPACK in functionality, data storage, and interface, the MAGMA library allows scientists to easily port their existing software components from LAPACK to MAGMA, to take advantage of the new hybrid architectures. MAGMA users do not have to know CUDA in order to use the library.
There are two types of LAPACK-style interfaces. The first one, referred to as the CPU interface, takes the input and produces the result in the CPU's memory. The second, referred to as the GPU interface, takes the input and produces the result in the GPU's memory. In both cases, a hybrid CPU/GPU algorithm is used. Also included is MAGMA BLAS, a complementary to CUBLAS routines.