We examine the problem of evaluating performance of supercomputer architectures on sparse (matrix) computations and lay out the details of a benchmark package for this problem. Whereas there already exists a number of benchmark packages for scientific computations, such as the Livermore Loops, the Linpack benchmark and the Los Alamos benchmark, none of these deals with the specific nature of sparse computations. Sparse matrix techniques are characterized by the relatively small number of operations per data element and the irregularity of the computation. Both facts may significantly increase the overhead time due to memory traffic. For this reason, the performance evaluation of sparse computations should not only take into account the CPU performance but also the degradation of performance caused by high memory traffic. Furthermore, sparse matrix techniques comprise a variety of different types of basic computations. Taking these considerations into account we propose a benchmark package that consists of several independent modules, each of which has a distinct role.
|Original language||English (US)|
|Title of host publication||Proceedings of the 2nd International Conference on Supercomputing, ICS 1988|
|Publisher||Association for Computing Machinery|
|Number of pages||10|
|State||Published - Jun 1 1988|
|Event||2nd International Conference on Supercomputing, ICS 1988 - St. Malo, France|
Duration: Jul 4 1988 → Jul 8 1988
|Name||Proceedings of the International Conference on Supercomputing|
|Other||2nd International Conference on Supercomputing, ICS 1988|
|Period||7/4/88 → 7/8/88|
Bibliographical noteFunding Information:
sparse bias. AMS(MOS) Classification: 66B, 66Hl0, 65F. Acknowledgementa: This work was supported in part by the National Science Foundation under Grants No. US NSF DCR84-10110 and US NSF DCR85-09970, by the US Deprut-mcnt of Energy under Grant No. DOE DEFG02-85EFl25O01, by the US Air Force under Contract AFSOR85-0211, by the Netherlands Organiestion for the Advancement of Pure Research Z.W.O. under Grant No. NF-61/62-517, and by an IBM donation. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Association for Computing Machinery. To copy otherwise, or to republish, requires a fee and/ or specific permission.
- Sparse blase
- Sparse matrix computations