SparseBench: a sparse iterative benchmark
Computer Science Department
University of Tennessee
Knoxville, TN 379961301, USA
and
Henk van der Vorst
Universiteit Utrecht
Utrecht, the Netherlands
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For comments and questions, mail to sparsebench@cs.utk.edu.
About the benchmark
SparseBench is a benchmark suite of iterative methods on sparse data. Sparse matrices, such as derived from PDEs, form an important problem area in numerical analysis. Unlike in the case of dense matrices, handling them does not entail much reuse of data. Thus, algorithms for sparse matrices will be more bound by memoryspeed than by processorspeed.
This benchmark uses common iterative methods, preconditioners, and storage schemes to evaluate machine performance on typical sparse operations. The benchmark components are:
 Conjugate Gradient and GMRES iterative methods,
 Jacobi and ILU preconditioners,
 diagonal storage and compressed row storage matrices.
Instructions
Download the file benchmark.tgz below.
Unpack it by
gunzip benchmark.tgz tar xf benchmark.tar or tar x f benchmark.tarGo into the benchmark directory
cd SparseBenchand configure for your architecture
configure
Install the software and test your machine by
Test m <machine name>where "machine name" is an arbitrary name for your machine. If you run 'Test' more than once, only higher numbers are kept.
Mail the results back to the benchmark reporting authority by
Report m <machine name>
You are strongly encouraged to read the files README and install.ps below, which are also part of the full tgz file.
Benchmark results
These are preliminary benchmark results, performed mostly on computers owned by the Innovative Computing Labs of the University of Tennessee. All tests report Megaflop rates on code that is compiled straight out of the box.
First we report the highest rate found for any problem. This was typically attained on a fairly small problem size, the implication being that the whole problem fit into cache.


Next we filter problem by
 Iterative method: gmres or cg;
 Storage scheme: regular (diagonal) or crs (compressed row);
 Preconditioner: none or ilu (incomplete LU).






######################################################################### file readme file install.ps file install.pdf for Installation Guide for the Sparse Iterative Benchmark file benchmark.tgz for Benchmark of Conjugate Gradient methods, using sparse data storage , Sparse benchmark, version 0.9.7, released 17 Nov 2000. , Questions/comments to sparsebench@cs.utk.edu by Jack Dongarra, Victor Eijkhout, Henk van der Vorst file bench.ps file bench.pdf for Details and results of the Sparse Iterative Benchmark #########################################################################