Python version of the Mersenne Twister pseudo-random number generator for Graphic Processor (MTGP)

## Project description

PyMTGP64

This Python module is an interface to MTGP, the Mersenne Twister for Graphic Processors (MTGP version 1.1.2)

by Mutsuo Saito and Makoto Matsumoto (Hiroshima University).

For more details see http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/MTGP/ and the reference below.

The module provides random generators for uniform, Normal and Poisson distributions.

Only 64-bit floating numbers are generated.

The random generator for the Poisson distribution exists in two forms. In the first one,

the distribution is computed for a single value of the characteristic mean 'lambda'.

In the second form, each generated random number can have is own mean value 'lambda'.

This form is particularly suited for generating Photon noise for a large image.

The module implements and provides the following methods:

* init(seed) : Initialize the module

* uniform(n) : Generates n uniformly distributed random numbers in the real interval ]0,1[

* normal(n) : Generates two random series of size n Normally distributed

* poisson(lambda,n) : Generates a Poisson distribution of mean lambda and size n

* poisson_multlamb(lambda) : As poisson() with multiple values of lambda (one for each generated random number).

* free() : Free the state of the pseudo-random number generator

* seed(value) : Initialize the seed value

* block_seeds(seeds) : Initialize the seed associated with each block

* block_num() : Return the number of blocks

* device() : Return the device index

For a complete example see pymtgp64_test.py

Reference: Mutsuo Saito, Makoto Matsumoto, Variants of Mersenne Twister Suitable for Graphic Processors,

Transactions on Mathematical Software, 39 (2013), pp. 12:1--12:20, DOI:10.1145/2427023.24270249

INSTALLATION

The compilation requires CUDA Toolkit (version 5.0 or later).

Edit appropriatley the Makefile. You must in particular define the path

where CUDA libraries and header files are installed (CUDAPATH).

You may also want to tune the constants that are defined in mtgp64-const.h

To compile the module type :

make all

and to install it type:

make install

or

python setup.py install --home=$HOME/

Depending on your system, this will install the module in $HOME/lib/python or in $HOME/lib64/python

You can also install it as root for all the users, in that case type:

sudo python setup.py install

You can finally do a complete test by typping:

make test

This will run the script named pymtgp64_test.py

LICENCES

Copyright (c) 2013 by R. Samadi (LESIA - Observatoire de Paris)

This is a free software: you can redistribute it and/or modify

it under the terms of the GNU General Public License as published by

the Free Software Foundation, either version 3 of the License, or

(at your option) any later version.

This software is distributed in the hope that it will be useful,

but WITHOUT ANY WARRANTY; without even the implied warranty of

MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the

GNU General Public License for more details.

You should have received a copy of the GNU General Public License

along with this code. If not, see <http://www.gnu.org/licenses/>.

Copyright (c) 2009, 2010 Mutsuo Saito, Makoto Matsumoto and Hiroshima

University.

Copyright (c) 2011, 2012 Mutsuo Saito, Makoto Matsumoto, Hiroshima

University and University of Tokyo.

All rights reserved.

Redistribution and use in source and binary forms, with or without

modification, are permitted provided that the following conditions are

met:

* Redistributions of source code must retain the above copyright

notice, this list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above

copyright notice, this list of conditions and the following

disclaimer in the documentation and/or other materials provided

with the distribution.

* Neither the name of the Hiroshima University, The Uinversity

of Tokyo nor the names of its contributors may be used to

endorse or promote products derived from this software without

specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS

"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT

LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR

A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT

OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,

SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT

LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,

DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY

THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT

(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE

OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

This Python module is an interface to MTGP, the Mersenne Twister for Graphic Processors (MTGP version 1.1.2)

by Mutsuo Saito and Makoto Matsumoto (Hiroshima University).

For more details see http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/MTGP/ and the reference below.

The module provides random generators for uniform, Normal and Poisson distributions.

Only 64-bit floating numbers are generated.

The random generator for the Poisson distribution exists in two forms. In the first one,

the distribution is computed for a single value of the characteristic mean 'lambda'.

In the second form, each generated random number can have is own mean value 'lambda'.

This form is particularly suited for generating Photon noise for a large image.

The module implements and provides the following methods:

* init(seed) : Initialize the module

* uniform(n) : Generates n uniformly distributed random numbers in the real interval ]0,1[

* normal(n) : Generates two random series of size n Normally distributed

* poisson(lambda,n) : Generates a Poisson distribution of mean lambda and size n

* poisson_multlamb(lambda) : As poisson() with multiple values of lambda (one for each generated random number).

* free() : Free the state of the pseudo-random number generator

* seed(value) : Initialize the seed value

* block_seeds(seeds) : Initialize the seed associated with each block

* block_num() : Return the number of blocks

* device() : Return the device index

For a complete example see pymtgp64_test.py

Reference: Mutsuo Saito, Makoto Matsumoto, Variants of Mersenne Twister Suitable for Graphic Processors,

Transactions on Mathematical Software, 39 (2013), pp. 12:1--12:20, DOI:10.1145/2427023.24270249

INSTALLATION

The compilation requires CUDA Toolkit (version 5.0 or later).

Edit appropriatley the Makefile. You must in particular define the path

where CUDA libraries and header files are installed (CUDAPATH).

You may also want to tune the constants that are defined in mtgp64-const.h

To compile the module type :

make all

and to install it type:

make install

or

python setup.py install --home=$HOME/

Depending on your system, this will install the module in $HOME/lib/python or in $HOME/lib64/python

You can also install it as root for all the users, in that case type:

sudo python setup.py install

You can finally do a complete test by typping:

make test

This will run the script named pymtgp64_test.py

LICENCES

Copyright (c) 2013 by R. Samadi (LESIA - Observatoire de Paris)

This is a free software: you can redistribute it and/or modify

it under the terms of the GNU General Public License as published by

the Free Software Foundation, either version 3 of the License, or

(at your option) any later version.

This software is distributed in the hope that it will be useful,

but WITHOUT ANY WARRANTY; without even the implied warranty of

MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the

GNU General Public License for more details.

You should have received a copy of the GNU General Public License

along with this code. If not, see <http://www.gnu.org/licenses/>.

Copyright (c) 2009, 2010 Mutsuo Saito, Makoto Matsumoto and Hiroshima

University.

Copyright (c) 2011, 2012 Mutsuo Saito, Makoto Matsumoto, Hiroshima

University and University of Tokyo.

All rights reserved.

Redistribution and use in source and binary forms, with or without

modification, are permitted provided that the following conditions are

met:

* Redistributions of source code must retain the above copyright

notice, this list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above

copyright notice, this list of conditions and the following

disclaimer in the documentation and/or other materials provided

with the distribution.

* Neither the name of the Hiroshima University, The Uinversity

of Tokyo nor the names of its contributors may be used to

endorse or promote products derived from this software without

specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS

"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT

LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR

A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT

OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,

SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT

LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,

DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY

THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT

(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE

OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

## Project details

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Filename, size & hash SHA256 hash help | File type | Python version | Upload date |
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