ABCLib_DRSSED: A parallel eigensolver with an auto-tuning facility

Authors:
Takahiro Katagiri;Kenji Kise;Hiroki Honda;Toshitsugu Yuba
Affiliations:
Graduate School of Information Systems, The University of Electro-Communications, 1-5-1 Choufu-gaoka, Choufu-shi, Tokyo 182-8585, Japan and Japan Science and Technology Agency, PRESTO, 1-5-1 Chouf ...;Graduate School of Information Systems, The University of Electro-Communications, 1-5-1 Choufu-gaoka, Choufu-shi, Tokyo 182-8585, Japan and Japan Science and Technology Agency, PRESTO, 1-5-1 Chouf ...;Graduate School of Information Systems, The University of Electro-Communications, 1-5-1 Choufu-gaoka, Choufu-shi, Tokyo 182-8585, Japan;Graduate School of Information Systems, The University of Electro-Communications, 1-5-1 Choufu-gaoka, Choufu-shi, Tokyo 182-8585, Japan
Venue:
Parallel Computing
Year:
2006

Citing 10
Cited 3

Optimizing matrix multiply using PHiPAC: a portable, high-performance, ANSI C coding methodology

ICS '97 Proceedings of the 11th international conference on Supercomputing
Practical experience in the numerical dangers of heterogeneous computing

ACM Transactions on Mathematical Software (TOMS)
Applied numerical linear algebra

Applied numerical linear algebra
ScaLAPACK user's guide

ScaLAPACK user's guide
A fast Fourier transform compiler

Proceedings of the ACM SIGPLAN 1999 conference on Programming language design and implementation
The Autopilot performance-directed adaptive control system

Future Generation Computer Systems - I. High Performance Numerical Methods and Applications. II. Performance Data Mining: Automated Diagnosis, Adaption, and Optimization
Dense linear algebra kernels on heterogeneous platforms: redistribution issues

Parallel Computing - Parallel matrix algorithms and applications
Knowledge Discovery in Auto-tuning Parallel Numerical Library

Progress in Discovery Science, Final Report of the Japanese Discovery Science Project
Effect of auto-tuning with user's knowledge for numerical software

Proceedings of the 1st conference on Computing frontiers
Architecture of an automatically tuned linear algebra library

Parallel Computing

Using experimental data to improve the performance modelling of parallel linear algebra routines

PPAM'07 Proceedings of the 7th international conference on Parallel processing and applied mathematics
A massively parallel dense symmetric eigensolver with communication splitting multicasting algorithm

VECPAR'10 Proceedings of the 9th international conference on High performance computing for computational science
Empirical Installation of Linear Algebra Shared-Memory Subroutines for Auto-Tuning

International Journal of Parallel Programming

Quantified Score

Hi-index	0.00

Visualization

Abstract

Conventional auto-tuning numerical software has the drawbacks of (1) fixed sampling points for the performance estimation; (2) inadequate adaptation to heterogeneous environments. To solve these drawbacks, we developed ABCLib_DRSSED, which is a parallel eigensolver with an auto-tuning facility. ABCLib_DRSSED has (1) functions based on the sampling points which are constructed with an end-user interface; (2) a load-balancer for the data to be distributed; (3) a new auto-tuning optimization timing called Before Execute-time Optimization (BEO). In our performance evaluation of the BEO, we obtained speedup factors from 10% to 90%, and 340% in the case of a failed estimation. In the evaluation of the load-balancer, the performance was 220% improved.