%0 Journal Article
%J International Journal of High Performance Computing Applications
%D 2014
%T A Novel Hybrid CPU-GPU Generalized Eigensolver for Electronic Structure Calculations Based on Fine Grained Memory Aware Tasks
%A Azzam Haidar
%A Raffaele Solcà
%A Mark Gates
%A Stanimire Tomov
%A Thomas C. Schulthess
%A Jack Dongarra
%K Eigensolver
%K electronic structure calculations
%K generalized eigensolver
%K gpu
%K high performance
%K hybrid
%K Multicore
%K two-stage
%X The adoption of hybrid CPU–GPU nodes in traditional supercomputing platforms such as the Cray-XK6 opens acceleration opportunities for electronic structure calculations in materials science and chemistry applications, where medium-sized generalized eigenvalue problems must be solved many times. These eigenvalue problems are too small to effectively solve on distributed systems, but can benefit from the massive computing power concentrated on a single-node, hybrid CPU–GPU system. However, hybrid systems call for the development of new algorithms that efficiently exploit heterogeneity and massive parallelism of not just GPUs, but of multicore/manycore CPUs as well. Addressing these demands, we developed a generalized eigensolver featuring novel algorithms of increased computational intensity (compared with the standard algorithms), decomposition of the computation into fine-grained memory aware tasks, and their hybrid execution. The resulting eigensolvers are state-of-the-art in high-performance computing, significantly outperforming existing libraries. We describe the algorithm and analyze its performance impact on applications of interest when different fractions of eigenvectors are needed by the host electronic structure code.
%B International Journal of High Performance Computing Applications
%V 28
%P 196-209
%8 2014-05
%G eng
%N 2
%& 196
%R 10.1177/1094342013502097
%0 Conference Paper
%B 1998 ACM/IEEE conference on Supercomputing (SC '98)
%D 1998
%T Automatically Tuned Linear Algebra Software
%A Clint Whaley
%A Jack Dongarra
%K BLAS
%K code generation
%K high performance
%K linear algebra
%K optimization
%K Tuning
%X This paper describes an approach for the automatic generation and optimization of numerical software for processors with deep memory hierarchies and pipelined functional units. The production of such software for machines ranging from desktop workstations to embedded processors can be a tedious and time consuming process. The work described here can help in automating much of this process. We will concentrate our efforts on the widely used linear algebra kernels called the Basic Linear Algebra Subroutines (BLAS). In particular, the work presented here is for general matrix multiply, DGEMM. However much of the technology and approach developed here can be applied to the other Level 3 BLAS and the general strategy can have an impact on basic linear algebra operations in general and may be extended to other important kernel operations.
%B 1998 ACM/IEEE conference on Supercomputing (SC '98)
%I IEEE Computer Society
%C Orlando, FL
%8 1998-11
%@ 0-89791-984-X
%G eng