Random Number Generator Controlling Commands
The commands described in this section are for a control of
the random number generators in CHARMM.
* Menu:
* Syntax:: Syntax of the random command
* Function:: Purpose of each of the flags and parameters
Syntax of RANDom commands
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Current Syntax:
RANDom specifications:
RANDom specifications:
RANDom { [CLCG] { [TIME] } } [UNIForm] [ASIN] [SCALe real] [OFFSet real] [TEST]
{ { ISEEd 4X(int } } [GAUSsian real ] [ACOS]
{ }
{ SYSTem { [TIME] } }
{ { ISEEd seed-specs} }
{ }
{ USER { [TIME] } }
{ { ISEEd seed-specs} }
{ }
{ OLDRandom [ISEEd int] }
seed-specs::= repeat integer ?NSEED number of times. For the default
CLCG RNG 4 integer numbers need to be specified. For the
standard fortran randum_number() routine one need to
specify ?NSEED integer numbers. This number is compiler
dependent!
Integer random number generator IRANdom specifications:
IRANdom [SERIes int] [SETUp] [BEGInt int] [ENDInt int]
[SEED int]
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1) RANDom command.
The expression ?RAND will have a random number substituted for it
during command line evaluation. The default is to provide a number
from a uniform distribution, between 0.0 and 1.0; the RANDom command
allows modification of the distribution type and specification of
other factors. The only required keyword is the distribution type,
which must be second; for a GAUSsian distribution, a value for sigma
is required; the default mean is 0.0.
There is a variety of random number generators (RNG) available in
CHARMM. They are:
OLDRandom - legacy RNG. It is not appropriate to use for production
simulations anymore, but it can be used for simple testing
and for comparison with older results and test cases. "Use
The Source Luke" to find out the CHARMM command line
parameters to switch back to either old random or old
CLCG. It is very convenient to put these flags into
test.com script and compare the test results with older
CHARMM versions. The alternative would be to put random
command in test/datadir.def file and in some 40 or so test
cases input scripts which don't stream datadir.def.
CLCG - new RNG that supports 100 series and uses 4 seeds. It is a
modern RNG and is the default choice.
SYSTem - whatever is provided by random_number() routine in
fortran. It supports only one series but uses different
numbers of seeds, depending on compilers and integer(8)
vs integer(4) compilation. Use ?NRAND value to query
USER - a user_random() function is provided in
charmm/usersb.src. This routine can be replaced by users
to test their own RNG.
Additional keywords:
UNIForm uniform distribution - default
GAUSSian sigma Gaussian distribution. Value of sigma must be specified
SCALe scale multiply the number by scale
OFFSet offset add offset to the number
ACOS treat the number as a cosine and return the angle (deg)
ASIN treat the number as a sine and return the angle (deg)
ISEEd iseed specify a new random seed(s) (integer(s)). Use
?NSEED parameter to query how many seeds are
needed for the current random number generator.
NOTE: No big numbers for CLCG (iseed < 2 giga).
No limit for SYSTem random generator.
TIME seeds are assigned from the current system time.
The default.
TEST this command will test the random number generator for
its poriodicity.
PARAllel stores the seeds from all the processors in
the restart file, so parallel run can be
restored if needed. Without the parallel
keyword every processors has its own random
number series initialized from system time.
Note that OLDRandom sub-command sets OLDRNG, which runs "old" random
number generator instead of "new" CLCG method. CLCG unsets OLDRNG, and
runs the CLCG random number generator.
About the seeds:
The use of fixed seeds is discouraged for production runs so
by default the system clock provides an initial seeds for the
RNGs. One can specify seeds on the RANDom command or in some other
commands, eg DYNA. The seed number stored in the trajectory file is
for legacy RNG (OLDRandom) and its use is deprecated. However the full
functionality of seed storage is implemented for the restart file. It
is implemented so that the restart files from older versions of CHARMM
can still be used, only the seed is ignored when running from the old
restart file. The new restart file saves all the necessary seeds.
Examples:
RANDOM GAUSS 0.2 SCALE 10.0 ! gaussian mean of 0.0 with a sigma of 2.
RANDOM UNIFORM SCALE 360. ! uniform 0. to 360
RANDOM UNIFORM ACOS SCALE .5 ! uniform angles with cosines from 0. to .5
RAND GAUS 5. OFFS 60. ! gaussian mean of 60. with a sigma of 5.
RAND UNIF ISEED 7734 ! uniform new random seed
Subsequent use of ?RAND will substitute a number from the appropriate
distribution.
2) IRANdom command.
This command is designed to generate series of random integers taken
from uniform distributions between user-specified limits. Each series
or distribution must first be set up with the IRANdom SETUp command,
in which the lower and upper limits of the distribution, the series
number, and an integer seed are specified. E.g.
IRAND SERIES 1 SETUp BEGI 1 ENDI 18 SEED 2346
IRAND SERIES 2 SETUp BEGI 1 ENDI 402 SEED 4028987
The random integers for each series are then generated with the commands
IRAND SERIES 1
IRAND SERIES 2
etc.
The ?iran expression accesses the last random integer generated.
The purpose of the multiple series feature is at least two-fold. First, it
allows users to generate random numbers easily from many different
distributions during the same charmm run (e.g. for use in different parts
of the same calculation). Second, it may help the user avoid correlations
between random numbers generated for different parts of a calculation.
An internal counter, corresponding initially to the seed, is incremented
by several units with each instance of the IRANdom command; by separating
the seeds of the various distributions sufficiently, the user can thus avoid
cross-series correlations. The use of multiple seeds for a given series
should be unnecessary and is discouraged. The IRANdom function has an
overall period of no less than 10^12 for distribution widths of 10^10 or less.
IRANdom can also be used to effectively generate random real numbers, through
a division of the generated integers by a constant, with the use of the
CALC command.
CHARMM Documentation / Rick_Venable@nih.gov