ColPack's Doxygen documentation is available here: http://cscapes.cs.purdue.edu/coloringpage/software.htm
ColPack's project home page: http://cscapes.cs.purdue.edu/coloringpage/
- ColPack
- Installation Guilds
2.1 Compile ColPack Without Install
2.2 Ubuntu Install
2.3 Windows Install
2.4 MacOS Install
2.5 Utilize the Installed Library - Usages
- HowToCite
ColPack is a package comprising of implementations of algorithms for the specialized vertex coloring problems discussed in the previous section as well as algorithms for a variety of related supporting tasks in derivative computation.
Vertex graph coloring problem is nothing but a way of labelling graph vertices under the constraints that no two adjacent vertices has the same lable (color). Here it is an example from wikipedia.
the table below gives a quick summary of all the coloring problems (on general and bipartite graphs) supported by ColPack.
| General Graph Coloring | Bipartite Graph one-sided coloring | Bipartite Graph Bicoloring |
|---|---|---|
| Distance 1 coloring | Partial distance-2 coloring | Star bicoloring |
| Distance 2 coloring | Partial distance-2 coloring | |
| Star coloring | ||
| Acyclic coloring | ||
| Restricted star coloring | ||
| Triangular coloring |
All of the coloring problems listed in the above table are NP-hard. Their corresponding algorithms in ColPack are greedy heuristics in the sense that the algorithms progressively extend a partial coloring by processing one vertex at a time, in some order, in each step assigning a vertex the smallest allowable color. Listed beneath each coloring problem in the table is the complexity of the corresponding algorithm in ColPack. In the cases where ColPack has multiple algorithms for a problem (these are designated by the superscript †), the complexity expression corresponds to that of the fastest algorithm. In the complexity expressions,
the complexity of the corresponding algorithm can be found here ColPack's project
The order in which vertices are processed in a greedy coloring algorithm determines the number of colors used by the algorithm. ColPack has implementations of various effective ordering techniques for each of the supported coloring problems. These are summarized in the table below.
| General Graph Coloring | Bipartite Graph one-sided coloring | Bipartite Graph Bicoloring |
|---|---|---|
| Natural | Column Natural | Natural |
| Largest First | Column Largest First | Largest First |
| Smallest Last | Column Smallest Last | Smallest Last |
| Incidence Degree | Column Incidence Degree | Incidence Degree |
| Dynamic Largest First | Row Natural | Dynamic Largest First |
| Distance-2 Largest First | Row Largest First | Selective Largest First |
| Distance-2 Smallest Last | Row Smallest Last | Selective Smallest Last |
| Distance-2 Incidence Degree | Row Incidence Degree | Selective Incidence Degree |
| Distance-2 Dynamic Largest First |
Besides coloring and ordering capabilities, ColPack also has routines for recovering the numerical values of the entries of a derivative matrix from a compressed representation. In particular the following reconstruction routines are currently available:
- Recovery routines for direct (via star coloring ) and substitution-based (via acyclic coloring) Hessian computation
- Recovery routines for unidirectional, direct Jacobian computation (via column-wise or row-wise distance-2 coloring)
- Recovery routines for bidirectional, direct Jacobian computation via star bicoloring
Finally, as a supporting functionality, ColPack has routines for constructing bipartite graphs (for Jacobians) and adjacency graphs (for Hessians) from files specifying matrix sparsity structures in various formats, including Matrix Market, Harwell-Boeing and MeTis.
ColPack is written in an object-oriented fashion in C++ heavily using the Standard Template Library (STL). It is designed to be simple, modular, extensible and efficient. Figure 1 below gives an overview of the structure of the major classes of ColPack.
There are two ways to use ColPack, Try without Installiation and Build and Install. The former is fast and easy to use, but is vulnerable for various OS enviroments settings, thus it requires the user know how to modify the makefile if met some compiling issue. The later one is more robust and it will also collect the ColPack into a shared library which makes ColPack easy to cooperate with other applications. But it requires to pre-install automake(or CMake) software.
You can just try ColPack by download, compile and run it. This is the fastest and simplest way to use ColPack. Do the following instructions in terminals.
cd
git clone https://github.com/CSCsw/ColPack.git #Download ColPack
cd ColPack # go to ColPack Root Directory
cd Examples/ColPackAll # go to ColPack Example folder
make # compile the code
After all source codes been compiled, we will generate a executable file ColPack under current folder.
The above instruction are tested under Ubuntu system. You may need to modify the Makefile to fit the different OS environments and compilers.(delete -fopenmp for mac os. Replace -fopenmp to -Qopenmp )for intel compiler.)
Install ColPack makes ColPack easy to use and it can also decreases the size of the execuable file. GNU autotools and CMake are supported. To install ColPack using autotools (requires that have installed automake on your machine.), follows the instructions below.:
cd
git clone https://github.com/CSCsw/ColPack.git #Download ColPack
cd ColPack # ColPack Root Directory
cd build/automake # automake folder
autoreconf -vif # generate configure files based on the machince
mkdir mywork
cd mywork
fullpath=$(pwd) # modify fullpath to your destination folder if need
../configure --prefix=${fullpath}
make -j 4 # Where "4" is the number of cores on your machine
make install # install lib and include/ColPack to destination
Append --disable-openmp to ./configure above if you need to disable OpenMP.(MAC user and some Windows user)
ColPack also has support for building with CMake, which you can do via the following:
mkdir build/cmake/mywork
cd build/cmake/mywork
fullpath=$(pwd) # modify fullpath to your destination folder if need
cmake .. -DCMAKE_INSTALL_PREFIX:PATH=${fullpath}
make -j 4 # Where "4" is the number of cores on your machine
make install # install the libararies
Use cmake -LH . or ccmake . in the build directory to see a list of
options, such as ENABLE_EXAMPLES and ENABLE_OPENMP, which you can set by
running the following from the build directory:
cmake .. -DENABLE_OPENMP=ON
If not using-DCMAKE_INSTALL_PREFIX:PATH, the library files will be installed under /usr/lib/ by default which may requires privilege.
You can build ColPack's static library on Windows using Visual Studio (tested with Visual Studio 2015) and CMake. Note, however, that you are not able to use OpenMP (Visual Studio supports only OpenMP 2.0), and cannot compile the ColPack executable (it depends on the POSIX getopt.h).
If you are using CMake 3.4 or greater, you can build and use ColPack's shared library. If you have an older CMake, we still build the shared library, but you will not be able to use it because none of the symbols will be exported (Visual Studio will not generate a .lib file).
On Windows, the examples link to the static library instead of the shared library.
Unlike on UNIX, the static library is named ColPack_static (ColPack_static.lib) to avoid a name conflict with the shared library's ColPack.lib.
Finally, some of the examples do not compile, seemingly because their filenames are too long.
To install ColPack on Mac, you first need to install Apple Xcode and automake. Since (it is well known that) Mac's default compiler clang doesn't support OpenMP well, you need either install OpenMP and gcc compiler or disable OpenMP by --disable-openmp .(It's a well known problem, MAC's default compiler clang doesn't support OpenMP well.)
cd
git clone https://github.com/CSCsw/ColPack.git #Download ColPack
cd ColPack # ColPack Root Directory
cd build/automake
autoreconf -vif
mkdir mywork
cd mywork
fullpath=$(pwd) # modify fullpath to your destination folder if need
./configure --prefix=${fullpath} --disable-openmp
make -j 4 # Where "4" is the number of cores on your machine
make install # install lib and include/ColPack to destination
Another recommend altinative way is to install an Ubuntu system on your MAC with VirtualBox (or any other virtual machine software), then install ColPack on your virtual machines.
After the build, we have already generate an shared library under the $fullpath directory, and an executable file 'ColPack' under the colpack root directory. And you can use it.
However if you want to write your own code and use ColPack as an shared library. Then follow the following ways:
- export library's path to
LD_LIBRARY_PATH - create your own code.
- include the relative ColPack header files within your code.
#include "ColPackHeaders.h" - added
-ldl path/to/installed/libraryand-I /path/to/installed/includeto the compiler - compile the code
We provide a template codes in Example_Use_Library
After building (or compile), you can run the following commands from where the executable file ColPack generated (ColPack root directory if using autotools, from the cmake directory if using CMake, or current directory if directly compile):
$./ColPack -f <graph_file_name> -o <ordering> -m <methods> [-v] ...
$./ColPack
<gfile_name>: Input file name
<ordering> : LARGEST_FIRST
SMALLEST_LAST,
DYNAMIC_LARGEST_FIRST,
INCIDENCE_DEGREE,
NATURAL,
RANDOM,
...
<methods> : DISTANCE_ONE
ACYCLIC
ACYCLIC_FOR_INDIRECT_RECOVERY
STAR
RESTRICTED_STAR
DISTANCE_TWO
--------------------
IMPLICIT_COVERING__STAR_BICOLORING
EXPLICIT_COVERING__STAR_BICOLORING
EXPLICIT_COVERING__MODIFIED_STAR_BICOLORING
IMPLICIT_COVERING__GREEDY_STAR_BICOLORING
--------------------
COLUMN_PARTIAL_DISTANCE_TWO
ROW_PARTIAL_DISTANCE_TWO
--------------------
D1_OMP_GMMP
D1_OMP_GM3P
D1_OMP_GMMP_LOLF
D1_OMP_GM3P_LOLF
D1_OMP_...
...
--------------------
D2_OMP_GMMP
D2_OMP_GM3P
D2_OMP_GMMP_LOLF
D2_OMP_GM3P_LOLF
--------------------
PD2_OMP_GMMP
PD2_OMP_GM3P
PD2_OMP_GMMP_LOLF
PD2_OMP_GM3P_LOLF
...
-v : # verbose for debug infomation
-fmt : MM/SQRT # only used by Partial Distance Two Parallel graph coloring. SQRT will read sqrt of grahp.
-low : # only used by Partial Distance Two Parallel graph coloring. The lower bound of coloring information will be displayed.
./ColPack -f ./Graphs/bcsstk01.mtx -o LARGEST_FIRST -m DISTANCE_ONE -v
./ColPack -f ./Graphs/bcsstk01.mtx -o SMALLEST_LAST -m ACYCLIC -v
./ColPack -f ./Graphs/bcsstk01.mtx -o DYNAMIC_LARGEST_FIRST -m DISTANCE_ONE_OMP -v
./ColPack -f ./Graphs/bcsstk01.mtx -o RANDOM -m D1_OMP_GMMP D2_OMP_GMMP -nT 1 2 4 -v
./ColPack -f ./Graphs/bcsstk01.mtx -o RANDOM -m PD2_OMP_GMMP PD2_OMP_GMMP_LOLF -nT 1 2 4 -v
ReadMatrixMarketAdjacencyGraph
Found file Graphs/bcsstk01.mtx
Graph of Market Market type: [matrix coordinate real symmetric]
Graph structure and VALUES will be read
#DISTANCE_ONE Result:
6 : (NATURAL)
6 : (LARGEST_FIRST)
6 : (DYNAMIC_LARGEST_FIRST)
6 : (SMALLEST_LAST)
6 : (INCIDENCE_DEGREE)
6 : (RANDOM)
#ACYCLIC Result:
8 : (NATURAL)
8 : (LARGEST_FIRST)
8 : (DYNAMIC_LARGEST_FIRST)
8 : (SMALLEST_LAST)
8 : (INCIDENCE_DEGREE)
8 : (RANDOM)
#ACYCLIC_FOR_INDIRECT_RECOVERY Result:
8 : (NATURAL)
8 : (LARGEST_FIRST)
8 : (DYNAMIC_LARGEST_FIRST)
8 : (SMALLEST_LAST)
8 : (INCIDENCE_DEGREE)
8 : (RANDOM)
#STAR Result:
12 : (NATURAL)
12 : (LARGEST_FIRST)
12 : (DYNAMIC_LARGEST_FIRST)
12 : (SMALLEST_LAST)
12 : (INCIDENCE_DEGREE)
12 : (RANDOM)
#RESTRICTED_STAR Result:
15 : (NATURAL)
15 : (LARGEST_FIRST)
15 : (DYNAMIC_LARGEST_FIRST)
15 : (SMALLEST_LAST)
15 : (INCIDENCE_DEGREE)
15 : (RANDOM)
#DISTANCE_TWO Result:
15 : (NATURAL)
15 : (LARGEST_FIRST)
15 : (DYNAMIC_LARGEST_FIRST)
15 : (SMALLEST_LAST)
15 : (INCIDENCE_DEGREE)
15 : (RANDOM)
Assefaw H. Gebremedhin, Duc Nguyen, Mostofa Ali Patwary, and Alex Pothen, ColPack: Graph coloring software for derivative computation and beyond, ACM Transactions on Mathematical Software, 40 (1), 30 pp., 2013.