Install notes for TACC/Stampede
Install notes for building our python3 stack on TACC/Stampede, using the intel compiler suite. Many thanks to Yaakoub El Khamra at TACC for help in sorting out the python3 build and numpy linking against a fast MKL BLAS.
On Stampede, we can in principle either install with a
OpenBLAS, or with an
intel/mvapich2/fftw3 stack with
MKL. Mpvaich2 is causing problems for us, and this
appears to be a known issue with
mvapich2/1.9, so for now we must
intel/mvapich2/fftw3 stack, which has
The intel stack should also, in principle,
allow us to explore auto-offloading with the Xenon MIC hardware
gcc instructions can be found under NASA Pleiades.
Here is my current build environment (from running
To get here from a gcc default do the following:
module unload mkl module swap gcc intel/184.108.40.206
intel compiler stack, we need to use
which then implies
intel/220.127.116.11. Right now, TACC has not built
fftw3 for this stack, so we’ll be doing our own FFTW build.
See the Stampede user guide for more details. If you would like to always auto-load the same modules at startup, build your desired module configuration and then run:
For ease in structuring the build, for now we’ll define:
~\build_intel and then proceed with downloading and installing Python-3.3:
cd ~/build_intel wget http://www.python.org/ftp/python/3.3.3/Python-3.3.3.tgz tar -xzf Python-3.3.3.tgz cd Python-3.3.3 # make sure you have the python patch, put it in Python-3.3.3 wget http://dedalus-project.readthedocs.org/en/latest/_downloads/python_intel_patch.tar tar xvf python_intel_patch.tar ./configure --prefix=$BUILD_HOME \ CC=icc CFLAGS="-mkl -O3 -xHost -fPIC -ipo" \ CXX=icpc CPPFLAGS="-mkl -O3 -xHost -fPIC -ipo" \ F90=ifort F90FLAGS="-mkl -O3 -xHost -fPIC -ipo" \ --enable-shared LDFLAGS="-lpthread" \ --with-cxx-main=icpc --with-system-ffi make make install
To successfully build
the key is replacing the file
ffi64.c, which is done
automatically by downloading and unpacking this crude patch
Python-3.3.3 directory. Unpack it in
tar xvf python_intel_patch.tar line above)
and it will overwrite
ffi64.c. If you forget to do this, you’ll
see a warning/error that
_ctypes couldn’t be built. This is important.
Here we are building everything in
~/build_intel; you can do it
whereever, but adjust things appropriately in the above instructions.
The build proceeeds quickly (few minutes).
We need to build our own FFTW3, under intel 14 and mvapich2/2.0b:
wget http://www.fftw.org/fftw-3.3.3.tar.gz tar -xzf fftw-3.3.3.tar.gz cd fftw-3.3.3 ./configure --prefix=$BUILD_HOME \ CC=mpicc \ CXX=mpicxx \ F77=mpif90 \ MPICC=mpicc MPICXX=mpicxx \ --enable-shared \ --enable-mpi --enable-openmp --enable-threads make make install
It’s critical that you use
mpicc as the C-compiler, etc.
Otherwise the libmpich libraries are not being correctly linked into
libfftw3_mpi.so and dedalus failes on fftw import.
Updating shell settings
At this point,
python3 is installed in
~/build_intel/bin/. Add this
to your path and confirm (currently there is no
python3 in the
default path, so doing a
which python3 will fail if you haven’t
On Stampede, login shells (interactive connections via ssh) source
~/.profile, in that
order, and do not source
~/.bashrc. Meanwhile non-login shells
(see Stampede user guide).
In the bash shell, add the following to
export PATH=~/build_intel/bin:$PATH export LD_LIBRARY_PATH=~/build_intel/lib:$LD_LIBRARY_PATH
and the following to
if [ -f ~/.bashrc ]; then . ~/.bashrc; fi
(from bash reference manual) to obtain the same behaviour in both shell types.
pip to install our python library depdencies.
Instructions on doing this are available here
and summarized below. First
download and install setup tools:
cd ~/build wget https://bitbucket.org/pypa/setuptools/raw/bootstrap/ez_setup.py python3 ez_setup.py
wget --no-check-certificate https://raw.github.com/pypa/pip/master/contrib/get-pip.py python3 get-pip.py --cert /etc/ssl/certs/ca-bundle.crt
[global] cert = /etc/ssl/certs/ca-bundle.crt
You will now have
pip installed in
You might try doing
pip -V to confirm that
pip is built
against python 3.3. We will use
pip3 throughout this
documentation to remain compatible with systems (e.g., Mac OS) where
multiple versions of python coexist.
Nose is useful for unit testing, especially in checking our numpy build:
pip3 install nose
Numpy and BLAS libraries
Building numpy against MKL
cd ~/build_intel wget http://sourceforge.net/projects/numpy/files/NumPy/1.8.0/numpy-1.8.0.tar.gz tar -xvf numpy-1.8.0.tar.gz cd numpy-1.8.0 wget http://lcd-www.colorado.edu/bpbrown/dedalus_documentation/_downloads/numpy_intel_patch.tar tar xvf numpy_inte_patch.tar
This last step saves you from needing to hand edit two
numpy/distutils; these are
fcompiler/intel.py. I’ve built a crude patch,
which can be auto-deployed by within the
numpy-1.8.0 directory by
the instructions above. This will unpack and overwrite:
We’ll now need to make sure that
numpy is building against the MKL
libraries. Start by making a
cp site.cfg.example site.cfg emacs -nw site.cfg
site.cfg in the
[mkl] section; modify the
library directory so that it correctly point to TACC’s
With the modules loaded above, this looks like:
[mkl] library_dirs = /opt/apps/intel/13/composer_xe_2013_sp1.1.106/mkl/lib/intel64 include_dirs = /opt/apps/intel/13/composer_xe_2013_sp1.1.106/mkl/include mkl_libs = mkl_rt lapack_libs =
These are based on intels instructions for compiling numpy with ifort and they seem to work so far.
Then proceed with:
python3 setup.py config --compiler=intelem build_clib --compiler=intelem build_ext --compiler=intelem install
This will config, build and install numpy.
Test numpy install
Test that things worked with this executable script
numpy_test_full. You can do this
full-auto by doing:
wget http://lcd-www.colorado.edu/bpbrown/dedalus_documentation/_downloads/numpy_test_full chmod +x numpy_test_full ./numpy_test_full
or do so manually by launching
and then doing:
import numpy as np np.__config__.show()
If you’ve installed
pip3 install nose),
we can further test our numpy build with:
np.test() with two failures, while
3 failures and 19 errors. But we do successfully link against the
fast BLAS libraries (look for
FAST BLAS output, and fast dot
We should check what impact these failed tests have on our results.
Python library stack
numpy has been built (see links above)
we will proceed with the rest of our python stack.
Right now, all of these need to be installed in each existing
virtualenv instance (e.g.,
For now, skip the venv process.
Scipy is easier, because it just gets its config from numpy. Download
an install in your appropriate
wget http://sourceforge.net/projects/scipy/files/scipy/0.13.2/scipy-0.13.2.tar.gz tar -xvf scipy-0.13.2.tar.gz cd scipy-0.13.2
python3 setup.py config --compiler=intelem --fcompiler=intelem build_clib \ --compiler=intelem --fcompiler=intelem build_ext \ --compiler=intelem --fcompiler=intelem install
This should just be pip installed:
pip3 install mpi4py==2.0.0
If we use use
pip3 install mpi4py
then stampede tries to pull version 0.6.0 of mpi4py. Hence the explicit version pull above.
This should just be pip installed:
pip3 install -v https://pypi.python.org/packages/source/C/Cython/Cython-0.20.tar.gz
The Feb 11, 2014 update to cython (0.20.1) seems to have broken (at least with intel compilers).:
pip3 install cython
This should just be pip installed:
pip3 install -v https://downloads.sourceforge.net/project/matplotlib/matplotlib/matplotlib-1.3.1/matplotlib-1.3.1.tar.gz
If we use use
pip3 install matplotlib
then stampede tries to pull version 1.1.1 of matplotlib. Hence the explicit version pull above.
Do this with a regular pip install:
pip3 install sympy
Installing HDF5 with parallel support
The new analysis package brings HDF5 file writing capbaility. This needs to be compiled with support for parallel (mpi) I/O:
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 --prefix=$BUILD_HOME \ CC=mpicc \ CXX=mpicxx \ F77=mpif90 \ MPICC=mpicc MPICXX=mpicxx \ --enable-shared --enable-parallel make make install
Next, install h5py. We wish for full HDF5 parallel goodness, so we can do parallel file access during both simulations and post analysis as well. This will require building directly from source (see Parallel HDF5 in h5py for further details). Here we go:
git clone https://github.com/h5py/h5py.git cd h5py export CC=mpicc export HDF5_DIR=$BUILD_HOME python3 setup.py configure --mpi python3 setup.py build python3 setup.py install
After this install,
h5py shows up as an
site-packages, but it looks like we pass the
test from Parallel HDF5 in h5py.
Installing h5py with collectives
We’ve been exploring the use of collectives for faster parallel file writing. To build that version of the h5py library:
git clone https://github.com/andrewcollette/h5py.git cd h5py git checkout mpi_collective export CC=mpicc export HDF5_DIR=$BUILD_HOME python3 setup.py configure --mpi python3 setup.py build python3 setup.py install
To enable collective outputs within dedalus, edit
# Assemble nonconstant subspace subshape, subslices, subdata = self.get_subspace(out) dset = task_group.create_dataset(name=name, shape=subshape, dtype=dtype) dset[subslices] = subdata
# Assemble nonconstant subspace subshape, subslices, subdata = self.get_subspace(out) dset = task_group.create_dataset(name=name, shape=subshape, dtype=dtype) with dset.collective: dset[subslices] = subdata
Alternatively, you can see this same edit in some of the forks (Lecoanet, Brown).
There are some serious problems with this right now; in particular, there seems to be an issue with empty arrays causing h5py to hang. Troubleshooting is ongoing.
With the modules set as above, set:
export BUILD_HOME=$HOME/build_intel export FFTW_PATH=$BUILD_HOME export MPI_PATH=$MPICH_HOME export HDF5_DIR=$BUILD_HOME export CC=mpicc
Then change into your root dedalus directory and run:
python setup.py build_ext --inplace
Our new stack (
mvapich2/2.0b) builds to completion
and runs test problems successfully. We have good scaling in limited
Running Dedalus on Stampede
Source the appropriate virtualenv:
grab an interactive dev node with
Freetype is necessary for matplotlib
cd ~/build wget http://sourceforge.net/projects/freetype/files/freetype2/2.5.2/freetype-2.5.2.tar.gz tar -xvf freetype-2.5.2.tar.gz cd freetype-2.5.2 ./configure --prefix=$HOME/build make make install
Skipping for now
May need this for matplotlib?:
cd ~/build wget http://prdownloads.sourceforge.net/libpng/libpng-1.6.8.tar.gz ./configure --prefix=$HOME/build make make install
Skipping for now
We may wish to deploy UMFPACK for sparse matrix solves. Keaton is starting to look at this now. If we do, both numpy and scipy will require UMFPACK, so we should build it before proceeding with those builds.
UMFPACK requires AMD (another package by the same group, not processor) and SuiteSparse_config, too.
If we need UMFPACK, we
can try installing it from
suite-sparse as in the Mac install.
Here are links to UMFPACK docs
We’ll check and update this later. (1/9/14)
All I want for christmas is suitesparse
Well, maybe :) Let’s give it a try, and lets grab the whole library:
wget http://www.cise.ufl.edu/research/sparse/SuiteSparse/current/SuiteSparse.tar.gz tar xvf SuiteSparse.tar.gz <edit SuiteSparse_config/SuiteSparse_config.mk>
Notes from the original successful build process:
Just got a direct call from Yaakoub. Very, very helpful. Here’s the quick rundown.
He got _ctypes to work by editing the following file:
Do build with intel 14 use mvapich2/2.0b Will need to do our own build of fftw3
set mpicc as c compiler rather than icc, same for CXX, FC and others, when configuring python. should help with mpi4py.
in mpi4py, can edit mpi.cfg (non-pip install).
Keep Yaakoub updated with direct e-mail on progress.
Also, Yaakoub is spear-heading TACCs efforts in doing auto-offload to Xenon Phi.
Beware of disk quotas if you’re trying many builds; I hit 5GB pretty fast and blew my matplotlib install due to quota limits :)
Installing virtualenv (skipped)
In order to test multiple numpys and scipys (and really, their
underlying BLAS libraries), we will use
pip3 install virtualenv
Next, construct a virtualenv to hold all of your python modules. We suggest doing this in your home directory:
With help from Yaakoub, we now build
Also, the mpicc build is much, much slower than icc. Interesting. And we crashed out. Here’s what we tried with mpicc:
./configure --prefix=$BUILD_HOME \ CC=mpicc CFLAGS="-mkl -O3 -xHost -fPIC -ipo" \ CXX=mpicxx CPPFLAGS="-mkl -O3 -xHost -fPIC -ipo" \ F90=mpif90 F90FLAGS="-mkl -O3 -xHost -fPIC -ipo" \ --enable-shared LDFLAGS="-lpthread" \ --with-cxx-main=mpicxx --with-system-ffi