python-setuptools¶

To get easy_install and others.

Installation

sudo apt-get install python-setuptools

g++ (>=4.7.1 for Elemental support)¶

The C++ compiler in the GNU Compiler Collection (GCC).

Installation:

sudo apt-get install g++

In case your ubuntu repository does not contain a GCC/G++ version 4.7 use the following:

sudo add-apt-repository ppa:ubuntu-toolchain-r/test
sudo apt-get update
sudo apt-get install gcc-4.7 g++-4.7

and (optionally) use 4.7 as default compiler:

sudo update-alternatives --remove gcc /usr/bin/gcc-4.6
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-4.7 60 --slave /usr/bin/g++ g++ /usr/bin/g++-4.7
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-4.6 40 --slave /usr/bin/g++ g++ /usr/bin/g++-4.6

gfortran compiler¶

gfortran is the name of the GNU Fortran compiler, which is part of the GNU Compiler Collection (GCC).

Installation:

sudo apt-get install gfortran

CMake (>= 2.8.8 for Elemental support)¶

CMake is a cross-platform free software program managing the build process of software using a compiler-independent method. It is designed to support directory hierarchies and applications that depend on multiple libraries, and for use in conjunction with native build environments such as make.

Installation:

sudo apt-get install cmake

If the available version is too old, compile CMake by hand. Head to http://www.cmake.org/cmake/resources/software.html and download the latest version.

./bootstrap --prefix=$CMAKE_INSTALL_DIR
make
make install

By specifying a CMAKE_INSTALL_DIR the install target for CMake can be set. If no --prefix is set, CMake will be installed in /usr/local. Make sure to add the install path to your PATH environment variable.

BLAS and LAPACK¶

Basic Linear Algebra Subroutine (BLAS) is a de facto application programming interface standard for publishing libraries to perform basic linear algebra operations such as vector and matrix multiplication. LAPACK (Linear Algebra PACKage) is a software library for numerical linear algebra. It provides routines systems of linear equations and linear least squares, eigenvalue problems, and singular value decomposition. It also includes routines to implement the associated QR, Cholesky and Schur decomposition.

We provide instructions for using Ubuntu’s repository and using OpenBLAS.

Installation:

sudo apt-get install libblas-dev libblas-doc libblas3gf liblapack-dev liblapack-doc liblapack3gf

Experiments show good speedups with using OpenBLAS.

wget http://github.com/xianyi/OpenBLAS/tarball/v0.2.8

Installation (to enable OpenMP support in OpenBLAS):

make USE_OPENMP=1 FC=gfortran; make install PREFIX=/path/to/install-directory

MPICH2¶

MPICH is a freely available, portable implementation of MPI, a standard for message-passing for distributed-memory applications used in parallel computing. MPICH is free software and is available for most flavors of Unix-like OS (including Linux).

Installation:

sudo apt-get install libcr-dev mpich2 mpich2-doc

NumPy and SciPy¶

NumPy is an extension to the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large library of high-level mathematica functions to operate on these arrays. SciPy is an open source library of algorithms and mathematical tools for the Python programming language that grew out of Travis Oliphant’s original collection of extension modules for Python which he released in 1999 under the name Multipack (named for the netlib packages that it brought together such as ODEPACK, QUADPACK, and MINPACK).

Installation:

sudo apt-get install python-numpy python-scipy

wget https://launchpad.net/~tukss/+archive/ppa/+build/4055943/+files/python-numpy_1.7.0~b2-1~precise1_amd64.deb
sudo dpkg -i python-numpy_1.7.0~b2-1~precise1_amd64.deb

sudo easy_install -U scipy

mpi4py¶

MPI for Python (mpi4py) provides bindings of the Message Passing Interface (MPI) standard for the Python programming language, allowing any Python program to exploit multiple processors.

Installation:

sudo easy_install mpi4py

hdf5¶

hdf5 is a portable data model, library and file format for storing and managing data.

Installation:

wget http://www.hdfgroup.org/ftp/HDF5/current/src/hdf5-1.8.12.tar
tar -xvf hdf5-1.8.12.tar
cd hdf5-1.8.12
./configure --enable-cxx --prefix=<location for HDF5 software>
make >& make.out
make check >& check.out
make install

h5py¶

h5py (h5py) provides Python bindings to hdf5, which is a data model, library, and file format for storing and managing data.

Installation:

sudo apt-get install libhdf5-serial-dev
sudo easy_install h5py

Boost C++ 1.55.0¶

Boost is a set of libraries for the C++ programming language that provide support for tasks and structures such as linear algebra, pseudorandom number generation, multithreading, image processing, regular expressions, and unit testing.

Installation:

wget http://sourceforge.net/projects/boost/files/boost/1.55.0/boost_1_55_0.tar.gz
tar xvfz boost_1_55_0.tar.gz
cd boost_1_55_0
./bootstrap.sh --with-libraries=mpi,serialization,program_options,system,filesystem
echo "using mpi ;" >> project-config.jam
./b2 link=static,shared
sudo ./b2 install

Compiling with the XL compiler

This section is inspired by the Argonne Boost configuration. First, as usual we call bootstrap.

./bootstrap.sh --with-libraries=mpi,serialization,program_options,system,filesystem

In a next step, download jam file and copy the Boost jam file to tools/build/v2/tools/. Subsequently, executing

echo "using bgq ;" >> project-config.jam
echo "using mpi : /bgsys/drivers/ppcfloor/comm/bin/xl/mpixlcxx ;" >> project-config.jam
./b2 link=static,shared toolset=bgq

compiles the selected boost libraries (you might need to adapt the path to mpixlcxx). In case all versions (debug, mt) are required, use --build-type=complete.

Elemental¶

Elemental is a framework for distributed-memory dense linear algebra that strives to be both fast and convenient. It combines ideas including: element-wise matrix distributions (Hendrickson et al.), object-oriented submatrix tracking (FLAME, van de Geijn et al.), and first-class matrix distributions (PLAPACK, van de Geijn et al.).

Elemental’s API is a moving target, so newer versions might not work as-is. Currently we only support version v0.87.4 (see https://github.com/elemental/Elemental/releases/tag/v0.87.4).

Installation: (make sure to use a compiler with c++11 support)

git clone https://github.com/elemental/Elemental.git
cd Elemental
git checkout tags/v0.87.4
mkdir build
cd build
cmake -DEL_USE_64BIT_INTS=ON -DEL_HAVE_QUADMATH=OFF -DCMAKE_BUILD_TYPE=Release -DEL_HYBRID=ON -DBUILD_SHARED_LIBS=ON -DMATH_LIBS="-L/usr/lib -llapack -lopenblas -lm" -DINSTALL_PYTHON_PACKAGE=ON ../
make
sudo make install

The installation prefix can be set using the CMAKE_INSTALL_PREFIX.

If you want to use non-default math libraries specify the MATH_LIBS variable. For example for OpenBLAS, run cmake along the following lines:

cmake -D MATH_LIBS="-L/usr/local/lib -llapack -lopenblas -lm" ..

Combinatorial BLAS (CombBLAS) 1.4¶

The Combinatorial BLAS is an extensible distributed-memory parallel graph library offering a small but powerful set of linear algebra primitives specifically targeting graph analytics. We use it in libSkylark to represent sparse matrices.

Installation:

wget http://gauss.cs.ucsb.edu/~aydin/CombBLAS_FILES/CombBLAS_beta_14_0.tgz
tar xvfz CombBLAS_beta_14_0.tgz
cd CombBLAS/
cmake .
make

Shared Libs

In order to use libSkylark through Python, make sure to compile and append the directory containing the *.so files to your LD_LIBRARY_PATH.

cd CombBLAS/
rm CMakeCache.txt
cmake -DBUILD_SHARED_LIBS:BOOL=ON .
make

KDT¶

The Knowledge Discovery Toolbox (KDT) provides a Python interface (amongst other things) to CombBLAS. libSkylark requires this package to handle sparse matrices through the Python interface.

To install kdt you need python and python-dev libs:

sudo apt-get install python
sudo apt-get install python-dev

Head over to http://kdt.sourceforge.net/wiki/index.php/Download and download kdt-0.3.tar.gz. Then execute:

tar xzf kdt-0.3.tar.gz
cd kdt-0.3
export CC=mpicxx
export CXX=mpicxx
python setup.py build
sudo python setup.py install

See http://kdt.sourceforge.net/wiki/index.php/Installation for further information.

FFTW 3.3.3¶

FFTW is a C subroutine library for computing the discrete Fourier transform (DFT) in one or more dimensions, of arbitrary input size, and of both real and complex data (as well as of even/odd data, i.e. the discrete cosine/sine transforms or DCT/DST).

Installation:

wget http://www.fftw.org/fftw-3.3.3.tar.gz
tar xvfz fftw-3.3.3.tar.gz
cd fftw-3.3.3/
./configure --enable-shared
make -j4
sudo make install

Random123 1.08¶

Random123 is a library of “counter-based” random number generators (CBRNGs). We use them in libSkylark to generate independent random number streams.

Installation:

wget http://www.thesalmons.org/john/random123/releases/1.08/Random123-1.08.tar.gz
tar xvfz Random123-1.08.tar.gz
sudo cp -r Random123-1.08/include/Random123 /usr/local/include

Doxygen¶

Doxygen is a documentation generator, a tool for writing software reference documentation. The documentation is written within code, and is thus relatively easy to keep up to date. Doxygen can cross reference documentation and code, so that the reader of a document can easily refer to the actual code.

Installation:

sudo apt-get install doxygen

Graphviz¶

Graphviz (short for Graph Visualization Software) is a package of open-source tools initiated by AT&T Labs Research for drawing graphs specified in DOT language scripts.

Installation:

sudo apt-get install graphviz

Python only user-level installation¶

In case you do not have root access, and are satisfied with an Python only installation, you can install the Python package only at user-level.

cd $BUILD_DIR
CC=mpicc CXX=mpicxx cmake $SRC_DIR
make
cd python-skylark
python ./setup.py install --user

The Python package will be installed under ~/.local. The directory ~/.local/lib should be in the library load path for the installation to function properly.

Directory setup¶

For the rest of this section let’s keep the following conventions:

• the source code is checked out and $SRC_DIR points to the base directory in the libSkylark repository, e.g.

  export SRC_DIR=/home/user/workspace/libskylark
  

• you created a separate (do not build in the $SRC_DIR$) build directory $BUILD_DIR that is used to generate object files, libraries and executables, e.g.

  export BUILD_DIR=/home/user/build/libskylark
  

Build options¶

libSkylark accepts build options in order to customize components. The following table summarizes all currently available build options:

You can specify the desired build type with -DCMAKE_BUILD_TYPE=STRING, where STRING is any of

The default is RELWITHDEBINFO.

Environment variables¶

The installation of libSkylark can be influenced with two variables:

• the CMake parameter CMAKE_INSTALL_PREFIX (i.e. pass -DCMAKE_INSTALL_PREFIX=/home/user/software when calling cmake), and
• the environment variable $PYTHON_SITE_PACKAGES to determine the installation location for python packages. Don’t forget to adapt the $PYTHONPATH environment variable as well. Example: If $PYTHON_SITE_PACKAGES is set to /home/user/local, CMake will install the Python bindings under /home/user/local/lib/python2.7/site-packages/skylark. At this point, you will have to append $PYTHONPATH with /home/user/local/lib/python2.7/site-packages!

To help CMake to locate installed dependencies (system-wide installed dependencies should be found automatically), you should set the following environment variables:

Configuring, compiling and installing libSkylark¶

Finally we are ready to configure, compile and install libSkylark. The default configuration (compiling Elemental and Python support, installed system-wide) can be compiled and installed with:

cd $BUILD_DIR
CC=mpicc CXX=mpicxx cmake $SRC_DIR
make
make install

The Python packages will most likely be installed under /usr/lib/pythonX.Y/dist-packages (OS and version dependent). Check the configure output for more details about default installation paths on your machine.

For the sake of illustration let’s assume you want to enable CombBLAS support and install everything in ~/local:

cd $BUILD_DIR
export PYTHON_SITE_PACKAGES=~/local/
CC=mpicc CXX=mpicxx cmake -DCMAKE_INSTALL_PREFIX=~/local -DWITH_COMBBLAS=ON $SRC_DIR
make
make install

Notice that we set the PYTHON_SITE_PACKAGES and passed a CMAKE_INSTALL_PREFIX to the cmake command above. Additionally we enabled CombBLAS support. Note that you have to make sure that the required libraries are compiled and the environment variables correctly point to the installation locations (i.e. export COMBBLAS_ROOT=~/software/combblas/).

Code documentation Doxygen¶

To generate the documentation (see dependency section for Doxygen installation), run

cd $BUILD_DIR
make doc

This will generate the Doxygen documentation under $BUILD_DIR/Documentation. To read the documentation open $BUILD_DIR/Documentation/html/index.html in a browser.

Sphinx¶

Make sure to install the Sphinx dependencies before you run make sphinx-doc:

apt-get install python-sphinx
cd /tmp
svn co https://svn.code.sf.net/p/matplotlib/code/trunk/sampledoc_tut
mkdir $HOME/.sphinx_ext
cp sampledoc_tut/sphinxext/*.py $HOME/.sphinx_ext
rm -rf /tmp/sampledoc_tut

export SPHINXEXT=$HOME/.sphinx_ext

apt-get python-dateutil
apt-get libfreetype6-dev libpng-dev
easy-install matplotlib
apt-get install texlive-latex-base
apt-get install texlive-latex-extra

Then run

cd $BUILD_DIR
make sphinx-doc

and point your browser to $BUILD_DIR/Documentation/sphinx/index.html.

Testing¶

In order to run unit tests, execute

cd $BUILD_DIR
make test

Running examples¶

There are two examples in the example folder (for more see python-skylark). The elemental.cpp shows how C++ code can utilize libSkylark. Run

examples/elemental -help

in the $BUILD_DIR to get a list of available command line options.

Using Cmake¶

If you are using Cmake to build your application you can use the CMake configuration file $SKYLARK_INSTALL_DIR/lib/cmake/SKYLARK/SKYLARKConfig.cmake in your CMakeLists.txt to find SKYLARK. After that you can simply include ${SKYLARK_INCLUDE_DIRS} and ${SKYLARK_LIBRARIES} when you build and link your application. A very basic CMake file for your project could look like:

cmake_minimum_required (VERSION 2.8.2)
project (SAMPLE)

find_package (MPI REQUIRED)
set (CMAKE_CXX_COMPILER ${MPI_COMPILER})

find_package (SKYLARK REQUIRED HINT ${SKYLARK_INSTALL_DIR}/lib)

include_directories ( ${SKYLARK_INCLUDE_DIRS} )
add_definitions(${SKYLARK_DEFS})

add_executable(ex_code ex_code.cpp)
target_link_libraries(ex_code ${SKYLARK_LIBRARIES} )
set_target_properties(ex_code PROPERTIES COMPILE_FLAGS "${SKYLARK_CXX_FLAGS}" )

This will pull all the required libs and add all include paths for libSkylark and its dependencies. You should be able to compile your application painless by following the above recipe.

Using XYZ Build System¶

Take a look at the file in $SKYLARK_INSTALL_DIR/lib/SKYLARK/SKYLARKConfig.cmake to see which include directories libraries you have to include when using libSkylark.

CombBLAS Installation Notes¶

The general steps to use CombBLAS with libSkylark:

• Download KDT, build and install the python package (generated with SWIG), then
• Download CombBLAS, build and install libs and include files, and finally
• When configuring libSkylark, use -DWITH_COMBBLAS=ON in the CMake configure call

Issues with CombBLAS >= 1.4.0

Unfortunately there are some issues when using CombBLAS with libSkylark. Check the subsections below if you have problem compiling or linking CombBLAS.

UINT32_MAX not declared

Additionally a CombBLAS header causes the compiler to produce an error (UINT32_MAX not declared). There are two possible ways to fix that:

• Edit the header (RefGen21.h) and replace UINT32_MAX with std::numeric_limits<unit32_t>::max(), or
• add the -D__STDC_LIMIT_MACROS compile flag in the CMake file when CombBLAS is enabled.

Compiler Warning: *enumeral* and *non-enumeral* type in conditional expression

To fix the following warning (appearing when compiling with -Werror):

CombBLAS_beta_13_0/psort-1.0/driver/MersenneTwister.h: In member function ‘void MTRand::seed(MTRand::uint32*, MTRand::uint32)’:
CombBLAS_beta_13_0/psort-1.0/driver/MersenneTwister.h:234:42: error: enumeral and non-enumeral type in conditional expression [-Werror]

apply the following patch:

diff --git a/psort-1.0/driver/MersenneTwister.h b/psort-1.0/driver/MersenneTwister.h
index 16e6458..1363423 100644
--- a/psort-1.0/driver/MersenneTwister.h
+++ b/psort-1.0/driver/MersenneTwister.h
@@ -231,7 +231,7 @@ inline void MTRand::seed( uint32 *const bigSeed, const uint32 seedLength )
        initialize(19650218UL);
        register int i = 1;
        register uint32 j = 0;
-       register int k = ( N > seedLength ? N : seedLength );
+       register int k = ( static_cast<uint32>(N) > seedLength ? static_cast<uint32>(N) : seedLength );
        for( ; k; --k )
        {
                state[i] =

Compiling Skylark/CombBLAS with CLANG

To be able to compile and link using the CLANG compiler (>=3.0.x), the following patch has to be applied to CombBLAS (<= 1.3.0):

diff --git a/DistEdgeList.cpp b/DistEdgeList.cpp
index 5ae9301..61cf16f 100644
--- a/DistEdgeList.cpp
+++ b/DistEdgeList.cpp
@@ -102,7 +102,7 @@ void DistEdgeList<IT>::Dump64bit(string filename)
        MPI_Comm_rank(World, &rank);
        MPI_Comm_size(World, &nprocs);
        MPI_File thefile;
-       MPI_File_open(World, filename.c_str(), MPI_MODE_CREATE | MPI_MODE_WRONLY, MPI_INFO_NULL, &thefile);
+       MPI_File_open(World, const_cast<char*>(filename.c_str()), MPI_MODE_CREATE | MPI_MODE_WRONLY, MPI_INFO_NULL, &thefile);

        IT * prelens = new IT[nprocs];
        prelens[rank] = 2*nedges;
@@ -125,7 +125,7 @@ void DistEdgeList<IT>::Dump32bit(string filename)
        MPI_Comm_rank(World, &rank);
        MPI_Comm_size(World, &nprocs);
        MPI_File thefile;
-       MPI_File_open(World, filename.c_str(), MPI_MODE_CREATE | MPI_MODE_WRONLY, MPI_INFO_NULL, &thefile);
+       MPI_File_open(World, const_cast<char*>(filename.c_str()), MPI_MODE_CREATE | MPI_MODE_WRONLY, MPI_INFO_NULL, &thefile);

        IT * prelens = new IT[nprocs];
        prelens[rank] = 2*nedges;
diff --git a/SpParMat.cpp b/SpParMat.cpp
index ca553e8..2ef88d6 100644
--- a/SpParMat.cpp
+++ b/SpParMat.cpp
@@ -109,7 +109,7 @@ void SpParMat< IT,NT,DER >::Dump(string filename) const
        int nprocs = commGrid->GetSize();

        MPI_File thefile;
-       MPI_File_open(World, filename.c_str(), MPI_MODE_CREATE | MPI_MODE_WRONLY, MPI_INFO_NULL, &thefile);
+       MPI_File_open(World, const_cast<char*>(filename.c_str()), MPI_MODE_CREATE | MPI_MODE_WRONLY, MPI_INFO_NULL, &thefile);

        int rankinrow = commGrid->GetRankInProcRow();
        int rankincol = commGrid->GetRankInProcCol();

Warning: variable ‘XYZ’ set but not used

CombBLAS (<=1.4.0) has set but not used variables. To remove the warning apply the following patch.

diff --git a/SpMat.cpp b/SpMat.cpp
index d00f37c..6ae6050 100644
--- a/SpMat.cpp
+++ b/SpMat.cpp
@@ -108,27 +108,28 @@ SpTuples<IU, NUO> * MultiplyReturnTuples
                                bool clearA = false, bool clearB = false)

{
-       IU A_m, A_n, B_m, B_n;
+       //IU A_m, A_n, B_m, B_n;
+       IU A_n, B_m;

        if(isAT)
        {
-               A_m = A.getncol();
+               //A_m = A.getncol();
                A_n = A.getnrow();
        }
        else
        {
-               A_m = A.getnrow();
+               //A_m = A.getnrow();
                A_n = A.getncol();
        }
        if(isBT)
        {
                B_m = B.getncol();
-               B_n = B.getnrow();
+               //B_n = B.getnrow();
        }
        else
        {
                B_m = B.getnrow();
-               B_n = B.getncol();
+               //B_n = B.getncol();
        }

        if(A_n == B_m)

Disable KDT Log

To disable the KDT log use the following patch:

diff --git a/kdt/Graph.py b/kdt/Graph.py
index 51c55b2..e61e9cf 100644
--- a/kdt/Graph.py
+++ b/kdt/Graph.py
@@ -1,7 +1,7 @@
 import math
 #import numpy as np # Adam: TRY TO AVOID THIS IF AT ALL POSSIBLE.
import pyCombBLAS as pcb
-import feedback
+#import feedback
import UFget as uf

from Util import info, master, version, revision
diff --git a/kdt/Vec.py b/kdt/Vec.py
index 00b7c1c..50aaa73 100644
--- a/kdt/Vec.py
+++ b/kdt/Vec.py
@@ -1,6 +1,6 @@
import math
import kdt.pyCombBLAS as pcb
-import feedback
+#import feedback
import UFget as uf
import Mat as Mat
import ctypes
diff --git a/kdt/__init__.py b/kdt/__init__.py
index 7f0992f..6c3a10f 100644
--- a/kdt/__init__.py
+++ b/kdt/__init__.py
@@ -10,7 +10,7 @@ from Vec import Vec
from Mat import Mat
#from SpVec import SpVec, info
#from DeVec import DeVec
-from feedback import sendFeedback
+#from feedback import sendFeedback
from UFget import UFget, UFdownload
try:
        import kdt.pyCombBLAS as pcb
diff --git a/setup.py b/setup.py
index a2813e3..10f5bdf 100644
--- a/setup.py
+++ b/setup.py
@@ -369,7 +369,7 @@ pyCombBLAS_ext = Extension('kdt._pyCombBLAS',
        extra_link_args = extra_link_args, extra_compile_args = extra_compile_args,
        define_macros=[('GRAPH_GENERATOR_SEQ', '1')] + headerDefs + define_macros)

-py_modules = ['kdt.pyCombBLAS', 'kdt.Graph', 'kdt.DiGraph', 'kdt.HyGraph', 'kdt.feedback', 'kdt.UFget', 'kdt.Mat', 'kdt.Vec', 'kdt.ObjMethods', 'kdt.Algorithms', 'kdt.Util', 'kdt.SpectralClustering', 'kdt.eig']
+py_modules = ['kdt.pyCombBLAS', 'kdt.Graph', 'kdt.DiGraph', 'kdt.HyGraph', 'kdt.UFget', 'kdt.Mat', 'kdt.Vec', 'kdt.ObjMethods', 'kdt.Algorithms', 'kdt.Util', 'kdt.SpectralClustering', 'kdt.eig']
# SEJITS modules:
py_modules.extend(['kdt.specializer.__init__', 'kdt.specializer.parsetab', 'kdt.specializer.pcb_function', 'kdt.specializer.pcb_function_frontend', 'kdt.specializer.pcb_function_sm', 'kdt.specializer.pcb_operator_convert', 'kdt.specializer.pcb_predicate', 'kdt.specializer.pcb_predicate_frontend', 'kdt.specializer.pcb_predicate_sm'])

Issues with CombBLAS < 1.4.0¶

std:: namespace clash (CombBLAS <= 1.3.0)

Depending on your compiler version (and if you installed CombBLAS <= 1.3.0) you might need to apply the following patch:

diff --git a/psort-1.0/src/psort_merge.h b/psort-1.0/src/psort_merge.h
index 858580b..256c3db 100644
--- a/psort-1.0/src/psort_merge.h
+++ b/psort-1.0/src/psort_merge.h
@@ -163,11 +163,11 @@ namespace vpsort {
                    out, comp);
       } else if (locs[next] == 0) {
        // 10 => backwards out of place
-       std::merge (reverse_iterator<_RandomAccessIter> (in + disps[nproc]),
-                   reverse_iterator<_RandomAccessIter> (in + disps[next]),
-                   reverse_iterator<_RandomAccessIter> (out + disps[next]),
-                   reverse_iterator<_RandomAccessIter> (out),
-                   reverse_iterator<_RandomAccessIter> (out + disps[nproc]),
+       std::merge (std::reverse_iterator<_RandomAccessIter> (in + disps[nproc]),
+                   std::reverse_iterator<_RandomAccessIter> (in + disps[next]),
+                   std::reverse_iterator<_RandomAccessIter> (out + disps[next]),
+                   std::reverse_iterator<_RandomAccessIter> (out),
+                   std::reverse_iterator<_RandomAccessIter> (out + disps[nproc]),
                    not2 (comp));
       } else {
        // 11 => in-place

LLVM/Clang¶

Because of various dependencies and c++11 compatibility issues the installation requires a c++11 compiler. For this we are installing LLVM/Clang (until the XL compiler provides the necessary c++11 features.

Argonne provides rpm packages for the BG/Q. Download and follow the instructions as stated on https://trac.alcf.anl.gov/projects/llvm-bgq.

There is a fairly painless way to install nightly builds using these installation scripts https://github.com/sloede/install-bgq.

After this step we assume you have installed LLVM/CLANG under $HOME/bgclang/.

Elemental¶

Download elemental 0.83 (http://libelemental.org/pub/releases/elemental-0.83.tgz). Then use the following toolchain file (e.g. save to BGQ-toolchain.cmake)

set(GCC_ROOT  "/bgsys/drivers/ppcfloor/gnu-linux")
set(GCC_NAME  "powerpc64-bgq-linux")
set(CLANG_ROOT "$ENV{HOME}/bgclang/bin")
set(CLANG_MPI_ROOT "$ENV{HOME}/bgclang/mpi/bgclang")
set(IBMCMP_ROOT "$ENV{IBM_MAIN_DIR}")

set(BLAS_LIB "$ENV{HOME}/src/lapack-3.5.0/")
set(LAPACK_LIB "$ENV{HOME}/src/lapack-3.5.0/")
set(ESSL_LIB "/opt/ibmmath/lib64")

set(MPI_ROOT   "/bgsys/drivers/ppcfloor/comm/gcc")
set(PAMI_ROOT  "/bgsys/drivers/ppcfloor/comm/sys")
set(SPI_ROOT   "/bgsys/drivers/ppcfloor/spi")

# The serial compilers
set(CMAKE_C_COMPILER   "${CLANG_MPI_ROOT}/bin/mpiclang")
set(CMAKE_CXX_COMPILER "${CLANG_MPI_ROOT}/bin/mpiclang++11")
set(CMAKE_Fortran_COMPILER "${GCC_ROOT}/bin/${GCC_NAME}-gfortran")

# The MPI wrappers for the C and C++ compilers
set(MPI_C_COMPILER   "${CLANG_MPI_ROOT}/bin/mpiclang")
set(MPI_CXX_COMPILER "${CLANG_MPI_ROOT}/bin/mpiclang++11")

set(MPI_C_COMPILE_FLAGS    "")
set(MPI_CXX_COMPILE_FLAGS  "")
set(MPI_C_INCLUDE_PATH     "${MPI_ROOT}/include")
set(MPI_CXX_INCLUDE_PATH   "${MPI_ROOT}/include")
set(MPI_C_LINK_FLAGS       "-L${MPI_ROOT}/lib -L${PAMI_ROOT}/lib -L${SPI_ROOT}/lib")
set(MPI_CXX_LINK_FLAGS     "${MPI_C_LINK_FLAGS}")
set(MPI_C_LIBRARIES       "${MPI_C_LINK_FLAGS}   -lSPI -lSPI_cnk -lrt -lpthread -lstdc++ -lpthread")
set(MPI_CXX_LIBRARIES     "${MPI_CXX_LINK_FLAGS} ${MPI_C_LIBRARIES}")

if(CMAKE_BUILD_TYPE MATCHES PureDebug OR
CMAKE_BUILD_TYPE MATCHES HybridDebug)
set(CXX_FLAGS "-g")
else()
set(CXX_FLAGS "-O3")
endif()

set(CMAKE_THREAD_LIBS_INIT "-fopenmp")
set(OpenMP_CXX_FLAGS "-fopenmp")

##############################################################

# set the search path for the environment coming with the compiler
# and a directory where you can install your own compiled software
set(CMAKE_FIND_ROOT_PATH
    /bgsys/drivers/ppcfloor
    /bgsys/drivers/ppcfloor/spi
    $ENV{HOME}/bgclang/mpi/bgclang
)

# adjust the default behaviour of the FIND_XXX() commands:
# search headers and libraries in the target environment, search
# programs in the host environment
set(CMAKE_FIND_ROOT_PATH_MODE_PROGRAM NEVER)
set(CMAKE_FIND_ROOT_PATH_MODE_LIBRARY ONLY)
set(CMAKE_FIND_ROOT_PATH_MODE_INCLUDE ONLY)

##############################################################

set(XLF_LIB "/opt/ibmcmp/xlf/bg/14.1/bglib64")
set(XLSMP_LIB "/opt/ibmcmp/xlsmp/bg/3.1/bglib64")
if(CMAKE_BUILD_TYPE MATCHES PureDebug OR
CMAKE_BUILD_TYPE MATCHES PureRelease)
set(MATH_LIBS "-L${ESSL_LIB} -lesslbg -L${LAPACK_LIB} -llapack -L${ESSL_LIB} -lesslbg -L${XLF_LIB} -lxlf90_r -L${XLSMP_LIB} -lxlomp_ser -lxlopt -lxlfmath -lxl -lpthread -ldl -Wl,--allow-multiple-definition")
else()
set(MATH_LIBS "-L${ESSL_LIB} -lesslsmpbg -L${LAPACK_LIB} -llapack -L${ESSL_LIB} -lesslsmpbg -L${XLF_LIB} -lxlf90_r -L${XLSMP_LIB} -lxlsmp -lxlopt -lxlfmath -lxl -lpthread -ldl -Wl,--allow-multiple-definition")
endif()

This toolchain file is based on the (Elemental) provided BGQ-Mira-clang-essl.cmake file. Finally, run the following to compile (and install) Elemental.

rm -rf CMake*; \
cmake -DCMAKE_TOOLCHAIN_FILE=BGQ-toolchain.cmake \
    -DCMAKE_INSTALL_PREFIX=$HOME/local \
    -DCMAKE_BUILD_TYPE=HybridRelease -DEL_EXAMPLES=OFF \
    -DBUILD_SHARED_LIBS=OFF \
    -DEL_USE_64BIT_INTS=ON -DBUILD_METIS=OFF ..
make install

In case you run into errors or problems, check the CMakeFiles/CMakeError.log file in the build directory.

CombBLAS¶

Download http://gauss.cs.ucsb.edu/~aydin/CombBLAS_FILES/CombBLAS_beta_14_0.tgz untar and change to the CombBLAS directory.

First we need to fix the CombBLAS CMakeLists.txt by commenting out the following two lines:

#SET(CMAKE_CXX_COMPILER mpicxx)
#SET(CMAKE_C_COMPILER mpicc)

and then run to build the required libraries

CC=$HOME/bgclang/mpi/bgclang/bin/mpiclang \
CXX=$HOME/bgclang/mpi/bgclang/bin/mpiclang++11 \
cmake .

FFTW¶

Download http://www.fftw.org/fftw-3.3.3.tar.gz.

CC=$HOME/bgclang/mpi/bgclang/bin/mpiclang \
CXX=$HOME/bgclang/mpi/bgclang/bin/mpiclang++11 \
./configure
make

Boost¶

Download Boost 1.55.0, untar, change directory and then execute:

./bootstrap.sh --with-libraries=mpi,serialization,program_options
echo "using mpi : $HOME/bgclang/mpi/bgclang/bin/mpiclang++11 ;" >> project-config.jam
./b2 toolset=clang

HDF5¶

Make sure you have a compiled libz around then run:

CC=$HOME/bgclang/mpi/bgclang/bin/mpiclang \
CXX=$HOME/bgclang/mpi/bgclang/bin/mpiclang++11 \
./configure --enable-cxx --prefix=$HOME/local/ \
    --with-pic --disable-shared --with-zlib=$HOME/local/

make lib
make install

libSkylark¶

Finally we can turn to building libSkylark. First change the CMake file by applying the following patch:

diff --git a/CMakeLists.txt b/CMakeLists.txt
index ef18fa7..825dbaa 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -81,11 +81,14 @@ if ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang")
    )

    set (COMPILER_SPEC_FLAGS
-        "-W -Wall -Wno-write-strings -Wno-strict-aliasing -Wno-format -Wno-deprecated -Wno-unused-variable -Wno-unused-parameter -Wno-sign-compare"
+        "-W -Wall -Wno-write-strings -Wno-strict-aliasing -Wno-format -Wno-deprecated -Wno-unused-variable -Wno-unused-parameter -Wno-sign-compare -Wno-overloaded-virtual -Wno-unsupported-friend"
    )

    #set (LINK_FLAGS
    #)

    set (CMAKE_LIB_LINKER_FLAGS  "${CMAKE_LIB_LINKER_FLAGS} -fPIC")

@@ -157,7 +160,7 @@ include_directories (${CMAKE_SOURCE_DIR})
# dependent packages
# 1. Find MPI --- we are not adding any include directories here because we
# will be using the mpi compilers, which adds these libraries by default.
-find_package (MPI REQUIRED)
+find_package (MPI)
set (CMAKE_CXX_COMPILER ${MPI_COMPILER})

Make sure that the following environment variables (adapt if necessary) are exported

# LAPACK library
export LAPACK_LIB=$HOME/src/lapack-3.5.0/

# root installation dir of CLANG compiler
export CLANG_ROOT=$HOME/bgclang/

# dependencies
export FFTW_ROOT=$HOME/local/fftw-3.3.3/
export HDF5_ROOT=$HOME/local
export ELEMENTAL_ROOT=$HOME/local
export COMBBLAS_ROOT=$HOME/src/CombBLAS
export RANDOM123_ROOT=$HOME/local/Random123-1.08
export BOOST_ROOT=$HOME/local/boost_1_55_0

and compile with:

mkdir build; cd build
rm CMakeCache.txt
rm -rf CMakeFiles

cmake -DCMAKE_TOOLCHAIN_FILE=../CMake/toolchains/BGQ-clang-essl.cmake \
      -DUSE_FFTW=ON -DUSE_COMBBLAS=ON -DBUILD_PYTHON=OFF -DUSE_HYBRID=ON \
      ../

For compiling the async example driver you need the following patch (the OpenMP statement seems to crash the CLANG compiler):

diff --git a/algorithms/asynch/AsyRGS.hpp b/algorithms/asynch/AsyRGS.hpp
index 92158f1..5eadf94 100644
--- a/algorithms/asynch/AsyRGS.hpp
+++ b/algorithms/asynch/AsyRGS.hpp
@@ -180,7 +180,7 @@ int AsyRGS(const base::sparse_matrix_t<T1>& A, const El::Matrix<T2>& B,
             utility::random_samples_array_t<dtype> stepidxs =
                 context.allocate_random_samples_array(sweeps * n, distribution);

-#           pragma omp parallel for default(shared) private(j, d)
+//#           pragma omp parallel for default(shared) private(j, d)
             for(j = 0; j < sweeps * n ; j++)
                 internal::jstep(colptr, rowind, vals, Bd, Xd, k, d, stepidxs[j]);

SLURM¶

On Watson Q the following SLURM script can be used to run examples:

#!/bin/bash
RUN_DIR=$HOME/work/libskylark/build
RUN_EXE=examples/elemental

if [ -z "$SLURM_JOBID" ]; then
    sbatch --gid=`hostname -s` --time=10:00 --nodes=8 --ntasks-per-node=2  -O --qos=umax-8 $0
else
    srun  --chdir=$RUN_DIR \
        --output=elemental.out \
        --error=error.out \
        $RUN_EXE
fi

which outputs

# cat error.out

# cat elemental.out
Default
0.558572 -0.316131 -0.0357262 0.572592 0.142264
-0.149105 0.0597079 0.228256 -0.53152 -0.0828558
-0.201294 0.499321 0.397655 0.6972 -0.303714
-0.36505 -0.120876 -0.276908 -0.461459 -0.255837
-0.154734 -0.0392899 0.202231 -0.298655 -0.262944