SparseBench: a sparse iterative benchmark
Jack Dongarra, Victor Eijkhout
Computer Science Department
University of Tennessee
Knoxville, TN 379961301, USA
and
Henk van der Vorst
Universiteit Utrecht
Utrecht, the Netherlands
Click here to see the number of accesses to this library.
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.tar
Go into the benchmark directory
cd SparseBench
and 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.
Highest performance ranking 

EV6 [a]  759 
Power3 [a]  606 
EV6 [b]  438 
Power3 [b]  331 
EV56  262 
PPC G4  198 
R12000 [a]  155 
UltraSparcII [a]  154 
Athlon  154 
R12000 [b]  108 
Origin  106 
UltraSparcII [b]  102 
PentiumIII  96 
LX164  81 
UltraSparcII [c]  47 

List of machines used 

Processor  Machine  Owned by  Compiler options 
Athlon  Athlon 600MHz  R. Clint Whaley  O 
EV56  Dec Alpha, 433 MHz  ICL, University of Tennessee 
EV6 [a]   Geophysik, Freie Universitaet Berlin 
EV6 [b]  DEC Alpha, 500 MHZ  ICL, University of Tennessee 
LX164  ALPHA, 533MHz  ICL, University of Tennessee 
Origin  SGI Origin, single processor  NCSA  O 
PPC G4  Macintosh at 450MHz  ICL, University of Tennessee 
PentiumIII  Dell, dual 550MHz  ICL, University of Tennessee 
Power3 [a]  IBM quad 375MHz power3  ICL, University of Tennessee 
Power3 [b]  IBM Power3, dual 200MHz  ICL, University of Tennessee 
R12000 [a]  SGI Octane, 270 MHz  ICL, University of Tennessee  O 
R12000 [b]  SGI Indigo  ICL, University of Tennessee  O 
UltraSparcII [a]  Sun Enterprise 450 model 1300, single 296MHz  ICL, University of Tennessee  O 
UltraSparcII [b]  Sun Ultra5  ICL, University of Tennessee  O 
UltraSparcII [c]  Sun Enterprise, 248MHz  ICL, University of Tennessee 

Next we filter problem by
 Iterative method: gmres or cg;
 Storage scheme: regular (diagonal) or crs (compressed row);
 Preconditioner: none or ilu (incomplete LU).
and we report the "asymptotic performance" which will be the expected Mflop
rate for large problems that overflow the cache.
Asymptotic performance is determined by making a y=a+b/x fit
through the observations, where x is the data set size in Mbytes.

Asymptotic performance on "gmres" problems 

EV6 [a]  216 
Power3 [a]  209 
EV6 [b]  168 
Power3 [b]  130 
R12000 [a]  78 
Origin  71 
EV56  60 
Athlon  44 
LX164  40 
PentiumIII  39 
PPC G4  38 
UltraSparcII [a]  37 
R12000 [b]  30 
UltraSparcII [c]  23 
UltraSparcII [b]  23 


Asymptotic performance on "cg" problems 

EV6 [a]  285 
Power3 [a]  254 
EV6 [b]  198 
Power3 [b]  110 
Origin  70 
UltraSparcII [a]  57 
R12000 [a]  52 
PPC G4  45 
LX164  45 
Athlon  43 
EV56  40 
PentiumIII  37 
UltraSparcII [c]  26 
UltraSparcII [b]  21 
R12000 [b]  19 


Asymptotic performance on "reg" problems 

EV6 [a]  285 
Power3 [a]  254 
EV6 [b]  198 
Power3 [b]  110 
R12000 [a]  78 
Origin  71 
UltraSparcII [a]  57 
EV56  55 
PPC G4  45 
LX164  45 
Athlon  43 
PentiumIII  37 
R12000 [b]  28 
UltraSparcII [c]  26 
UltraSparcII [b]  21 


Asymptotic performance on "crs" problems 

Power3 [a]  209 
EV6 [a]  209 
EV6 [b]  166 
Power3 [b]  130 
Origin  68 
R12000 [a]  63 
EV56  60 
Athlon  44 
LX164  40 
PentiumIII  39 
UltraSparcII [a]  35 
R12000 [b]  30 
UltraSparcII [c]  23 
UltraSparcII [b]  23 
PPC G4  23 


Asymptotic performance on "none" problems 

Power3 [a]  215 
EV6 [a]  205 
EV6 [b]  158 
Power3 [b]  88 
R12000 [a]  64 
Origin  63 
UltraSparcII [a]  40 
EV56  40 
PPC G4  38 
LX164  36 
Athlon  33 
PentiumIII  27 
UltraSparcII [c]  26 
R12000 [b]  24 
UltraSparcII [b]  21 


Asymptotic performance on "ilu" problems 

EV6 [a]  163 
EV6 [b]  132 
Power3 [a]  120 
Power3 [b]  90 
R12000 [a]  62 
Origin  57 
EV56  39 
UltraSparcII [a]  34 
Athlon  34 
LX164  33 
PPC G4  31 
PentiumIII  27 
R12000 [b]  20 
UltraSparcII [c]  16 
UltraSparcII [b]  15 

#########################################################################
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
#########################################################################