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Add Gurobi solver as an option
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# Pluto README
# Uday Bondhugula
# uday@csa.iisc.ernet.in


Pluto is available under GPL v3, and libpluto is available under LGPL v2.1.



A Linux distribution. Pluto has been tested on x86 and x86-64 machines 
running Fedora, Ubuntu, and RedHat Enterprise Server.  Solaris should also 
be fine if you have GNU utilities. In order to use the development version 
from Pluto's git repository, automatic build system tools including 
autoconf, automake, and libtool are needed. GMP (GNU multi precision 
arithmetic library) is needed by ISL (one of the included libraries). If 
it's not already on your system, it can be installed easily with, for 
eg., eg., 'sudo yum -y install gmp gmp-devel' on a Fedora. It is also 
recommended astyle and indent be installed if a user wishes to browse 
through generated code.

Pluto includes all polyhedral libraries that it depends on.


Stable release

$ tar zxvf pluto-0.11.4.tar.gz
$ cd pluto-0.11.4/
$ ./configure
$ make
$ make test

configure can be provided --with-isl-prefix=<isl install location> to 
build with another isl, otherwise the bundled isl is used.

Development version from Git

$ git clone git://repo.or.cz/pluto.git
$ cd pluto/
$ git submodule init 
$ git submodule update
$ ./autogen.sh
$ ./configure [--enable-debug] [--with-isl-prefix=<isl install location>]
$ make
$ make test

* --with-isl-prefix=<location> to compile and link with an already installed 
isl. By default, the version of isl bundled with Pluto will be used.

'polycc' is the wrapper script around src/pluto (core transformer) and all 
other components. 'polycc' runs all of these in sequence on an input C 
program (with the section to  parallelize/optimize marked) and is what a 
user should use on input. Output generated is OpenMP parallel C code that 
can be readily compiled and run on shared-memory parallel machines like 
general-purpose multicores. libpluto.{so,a} is also built and can be found 
in src/.libs/. 'make install' will install it.


- Use '#pragma scop' and '#pragma endscop' around the section of code 
  you want to parallelize/optimize.

- Then, just run 
    ./polycc <C source file> --parallel --tile

  The transformation is also printed out, and test.par.c will have the 
  parallelized code. If you want to see intermediate files, like the 
  .cloog file generated (.opt.cloog, .tiled.cloog, or .par.cloog 
  depending on command-line options provided), use --debug on command 

- Tile sizes can be specified in a file 'tile.sizes', otherwise default 
  sizes will be set. See doc/DOC.txt on how to specify the sizes.

To run a good number of experiments on a code, it is best to use the setup 
created for example codes in the examples/ directory.  If you do not have 
ICC (Intel C compiler), uncomment line 7 and comment line 
8 of examples/common.mk to use GCC.

- Just copy one of the sample directories in examples/, edit Makefile (SRC = 

- do a make (this will build all executables; 'orig' is the original code 
  compiled with the native compiler, 'tiled' is the tiled code, 'par' is the 
  OpenMP parallelized+locality optimized code, 'lbpar' with diamond tiling 
  when possible. One could do 'make <target>' where target can be orig, 
  orig_par, opt, tiled, par, lbpar, etc. (see examples/common.mk for full 

- 'make test' to test for correctness, 'make perf', 'make lbperf' to compare 



./polycc -h 

or see documentation (doc/DOC.txt) for details


Lets say we are trying the 2-d gauss seidel kernel. In examples/seidel, do 
'make par'; this will generate seidel.par.c from seidel.c and also compile 
it to generate 'par'.  Likewise, 'make tiled' for 'tiled' and 'make orig' 
for 'orig'.

$ cd examples/seidel

seidel.c: This is the original code (the kernel in this code is extracted).  
'orig' is the corresponding executable when compiled with the native 
compiler (gcc or icc for eg.) with optimization flags, 'orig_par' with the 
native compiler's auto-parallelization enabled.

seidel.opt.c: This is the transformed code without tiling (this is of not 
much use, except for seeing benefits of fusion in some cases). 'opt' is the 
corresponding executable.

seidel.tiled.c: This is Pluto generated code optimized for locality with 
tiling and other transformations, but not not parallelized - this should be 
used for sequential execution. 'tiled' is the corresponding executable.

seidel.par.c: This is Pluto parallelized code optimized for locality and 
parallelism  with tiling and other transformations. This code has OpenMP 
pragmas. 'par' is the corresponding executable.

- To change any of the flags used for an example, edit the top section of 
  examples/common.mk or the Makefile in the example directory

- To manually specify tile sizes, create tile.sizes; see examples/matmul/ 
   for example or doc/DOC.txt for more information on setting tile sizes. 

The executables already have timers; you just have to run them and that will 
print execution time for the core part of the computation as well.

To run the Pluto parallelized version:

$ OMP_NUM_THREADS=4; ./par

To run native compiler optimized/auto-parallelized version:

$ OMP_NUM_THREADS=4; ./orig_par

To run the original unparallelized code:

$ ./orig

To run the locality optimized version generated by Pluto:

$ ./tiled

- 'make clean' in the particular example's directory removes all executables 
    as well as generated codes

To launch a complete verification that compares output of tiled, par
with orig for all examples, in examples/, run 'make test'.

[examples/ ]$ make test


* See doc/DOC.txt for an overview of the system and details on all 
command-line options.

* For specifying custom tile sizes through 'tile.sizes' file, see 

* For specifying custom fusion structure through '.fst' file, see 


Please send all bugs reports and comments to Uday Bondhugula 
<uday@csa.iisc.ernet.in> or post of pluto-development@googlegroups.com.