NAG's SMP Library now optimised for the IBM POWER4
Oxford UK – September 2002. The world's leading provider of mathematical and statistical algorithms, the Numerical Algorithms Group (NAG), announces that it has optimised its SMP (Symmetric Multi-Processor) Library for the IBM POWER4 architecture.
IBM provided early access to a POWER4 system at the Hursley Solution Partnership Centre in the UK. "The support provided by IBM and the performance of the POWER4 system were excellent," commented Stefano Salvini, Senior Technical Consultant at NAG.
Some of the results from extensive work are listed below:
On the IBM eServer pSeries p690 system with 1.1 GHz POWER4 processors:
- Common dense linear algebra factorization routines (eg LU, Cholesky and QR), and their associated solvers, sustain a performance of up to 2.8 GFLOPS on a single processor, and scale in performance up to 21 GFLOPS on 8 processors .
- The QR-algorithm symmetric eigenproblem routine (F08JEF/DSTEQR) achieves 2.4 times the performance on 1 processor, and 16.6 times the performance on 8 processors, of the algorithmically equivalent LAPACK routine DSTEQR .
- The QR-algorithm Singular Value Decomposition (SVD) routine (F08MEF/DBDSQR) achieves over 6 times the performance on 1 processor of the algorithmically equivalent LAPACK routine .
- While the standard LAPACK code does not scale in performance on multiple processors, the SMP routine scales by, for example, a factor of 3.7 on 4 processors .
Brian Ford, Director of NAG said, "We were seeing demand from users of the SMP Library to produce an optimised version of the library on IBM POWER4. We are delighted to see how IBM and NAG work together to respond to our user requirements. NAG is committed to ensuring that users of our numerical libraries can utilise the products on IBM and other vendor platforms."
 Performance may depend upon problem size. Values quoted are typical for problems of order N=2000 or higher.
 Example quoted is for a problem of order N=4000.
 Problem of order N=1000
 Problem of order N=2000
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