benchmk
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benchmk
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| Name | Name | Last commit date | ||
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Benchmark Timing
This directory contains the source code for drivers that time the
execution of important CCMATH functions. The header comments in each of
the source files describes the input command line parameters required
by the function. The file 'linux_2p.tim' contains timing data I obtained
on a 233 MHz Pentium II.
The quoted times were obtained, after instalation of the ccmath
library, with programs compiled using
cc -O3 "bench".c -lccm -lm ,
where "bench " is one of the following drivers.
timbes.c ----- time the execution of Bessel functions
timeigv.c ---- time the computation of the eigenvalues
and eigenvectors of a real symmetric matrix
timeval.c ---- time the computation of the eigenvalues of
a real symmetric matrix
timfft2.c ---- time a radix-2 Fast Fourier Transform (FFT)
timfftgc.c --- time a general radix complex FFT
timheval.c --- time the computation of the eigenvalues of a
hermitian matrix
timhevec.c --- time the computation of the eigenvalues and
eigenvectors of a hermitian matrix
timhsrt.c ---- time the heap sort of a real array
timintg.c ---- time a numerical integration of complete
elliptic integrals
timlpac.c ---- time the solution of a real linear system (LINPAC)
timmiv.c ----- time the inversion of a real matrix
timmsrt.c ---- time the merge sort of a real array
timmul.c ----- time the multiplication of real matrices
timnrml.c ---- time the generation of pseudorandom normals
timqsrt.c ---- time the 'quicksort' of a real array
timslpac.c --- time the solution of 'small' linear systems
(dimension < 400)
timssrt.c ---- time the "Shell' sort of a real array
The following functions generate the binary matrix data files used
as input to the eigensystem timing routines.
hmatgen.c ---- generate a hermitian matrix with the specified
dimension
smatgen.c ---- generate a real symmetric matrix with the specified
dimension
The eigenvalues employed in these generators have a simple equal
spacing. This portion of the generator code can easily be changed
to generate matrices with more complex patterns of eigenvalues.