new file: a/aria2/aria2-1.35.0-GCCcore-10.3.0.eb

new file:   b/BLIS/BLIS-0.8.1_fix_dgemm-fpe-signalling-on-broadwell.patch
	new file:   b/BLIS/BLIS-2.2-GCCcore-10.2.0.eb
	new file:   b/BLIS/BLIS-2.2-amd_fix-undefined-reference-blist-abort.patch
	new file:   b/BLIS/BLIS-3.0.1-GCCcore-10.2.0.eb
	new file:   b/Biopython/Biopython-1.72-foss-2020b-Python-2.7.18.eb
	new file:   c/Cordax/Cordax-1.0-Python-2.7.18.eb
	new file:   d/DFTB+/DFTB+-21.2-intel-2020b-Python-3.8.6.eb
	new file:   d/DFTB+/DFTB+-21.2-intel-2020b-TB.eb
	new file:   d/Dakota/Dakota-6.15.0-fix_lapack_detection.patch
	new file:   d/Dakota/Dakota-6.15.0-foss-2021b.eb
	new file:   d/Dakota/Dakota-6.15.0-intel-2021b.eb
	new file:   e/ELPA/ELPA-2020.11.001-fosscuda-2020b.eb
	new file:   f/FoldX/FoldX-5.0.eb
	new file:   f/Forge/Forge-21.1.3.eb
	new file:   g/GROMACS/GROMACS-2018.8-fosscuda-2020b-PLUMED-2.5.6-switch.eb
	new file:   g/GROMACS/GROMACS-2021.4-foss-2020b-PLUMED-2.7.3.eb
	new file:   h/HDF5/HDF5-1.12.1-NVHPC-21.11.eb
	new file:   h/HDF5/HDF5-1.12.1-foss-2021b-parallel.eb
	new file:   h/HDF5/HDF5-1.12.1-iimpi-2021b.eb
	new file:   h/HDF5/HDF5-1.12.1-intel-2021b-parallel.eb
	new file:   h/HyperQueue/HyperQueue-0.8.0.eb
	new file:   h/Hypre/Hypre-2.23.0-intel-2020b.eb
	new file:   h/h5py/h5py-3.6.0-intel-2021b.eb
	new file:   l/LAPACK/LAPACK-3.10.0-GCC-11.2.0.eb
	new file:   l/libFLAME/libFLAME-5.2.0-GCCcore-10.2.0.eb
	new file:   m/MaSuRCA/MaSuRCA-4.0.7-foss-2020a-Perl-5.30.2.eb
	new file:   m/Molpro/Molpro-mpp-2022.1.1.linux_x86_64_mpipr.eb
	new file:   m/Molpro/Molpro-mpp-2022.1.1.linux_x86_64_sockets.eb
	new file:   n/nompi/nompi-2022a.eb
	new file:   o/ORCA/ORCA-5.0.3-OpenMPI-4.1.1.eb
	modified:   o/Octopus/Octopus-11.3-intel-2020b-mpi.eb
	new file:   o/OpenCV/OpenCV-4.5.3-foss-2021a-CUDA-11.3.1-contrib.eb
	new file:   o/OpenCV/OpenCV-4.5.5-foss-2021a-CUDA-11.4.1-contrib.eb
	new file:   o/OpenMPI/OpenMPI-4.1.1-GCC-11.2.0.eb
	new file:   o/OpenMPI/OpenMPI-4.1.2-GCC-11.2.0-Java-1.8.0_221.eb
	new file:   o/OpenMPI/OpenMPI-4.1.2-GCC-11.2.0.eb
	modified:   p/PETSc/PETSc-3.14.4-intel-2020b.eb
	modified:   p/PLUMED/PLUMED-2.5.6-fosscuda-2020b-patch.eb
	new file:   p/PLUMED/PLUMED-2.5.6-fosscuda-2020b-switch.eb
	new file:   p/PLUMED/PLUMED-2.7.3-foss-2020b.eb
	modified:   p/phonopy/phonopy-2.12.0-conda.eb
	modified:   q/QMCPACK/QMCPACK-3.11.0-intel-2020b-Python-3.8.6.eb
	new file:   q/QMCPACK/QMCPACK-3.12.0-intel-2020b-Python-3.8.6.eb
	new file:   q/QMCPACK/QMCPACK-3.12.0-intel-2021b-Python-3.9.6-lowopt.eb
	new file:   q/QMCPACK/QMCPACK-3.13.0-intel-2020b-Python-3.8.6.eb
	new file:   q/QuantumESPRESSO/QuantumESPRESSO-6.7-intel-2021a.eb
	new file:   q/QuantumESPRESSO/QuantumESPRESSO-7.0-NVHPC-21.9.eb
	new file:   r/rocm-cuda2hip/rocm-cuda2hip-4.3.1-gcccuda-2020b.eb
	new file:   s/ScaLAPACK/ScaLAPACK-2.2-NVHPC-21.11.eb
	new file:   t/Tango/Tango.eb
	new file:   t/Tensorflow/TensorFlow-2.5.0-fosscuda-2020b.eb
	new file:   v/VASP/VASP-5.4.1-24Jun15-intel-2020b.eb
	new file:   w/Waltz/Waltz.eb
	new file:   y/Yambo/Yambo-5.0.4-intel-2020a.eb
This commit is contained in:
easybuild 2022-03-04 13:14:37 +01:00
parent 4c3a9ecb83
commit 536c92481f
55 changed files with 4676 additions and 3 deletions

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# IT4Innovations
# JK 2022
easyblock = 'ConfigureMake'
name = 'aria2'
version = '1.35.0'
homepage = 'https://aria2.github.io'
description = "aria2 is a lightweight multi-protocol & multi-source command-line download utility."
toolchain = {'name': 'GCCcore', 'version': '10.3.0'}
source_urls = ['https://github.com/aria2/aria2/releases/download/release-%(version)s']
sources = [SOURCE_TAR_GZ]
checksums = ['fd85589416f8246cefc4e6ba2fa52da54fdf11fd5602a2db4b6749f7c33b5b2d']
builddependencies = [
('binutils', '2.36.1'),
('Autotools', '20210128'),
('CppUnit', '1.15.1'),
]
dependencies = [
('zlib', '1.2.11'),
('libxml2', '2.9.10'),
('SQLite', '3.35.4'),
('c-ares', '1.17.2'),
('OpenSSL', '1.1', '', True),
]
# add certificates' path to use https
configopts = "--without-gnutls --with-openssl --enable-libaria2 --enable-static --with-ca-bundle='/etc/ssl/certs/ca-bundle.crt'"
#runtest = 'check'
sanity_check_paths = {
'files': ['bin/aria2c'],
'dirs': ['share'],
}
sanity_check_commands = ["aria2c --help"]
moduleclass = 'tools'

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@ -0,0 +1,45 @@
# IT4Innovations
# LK 2022
easyblock = 'ConfigureMake'
name = 'BLIS'
version = '2.2'
homepage = 'https://developer.amd.com/amd-cpu-libraries/blas-library/'
description = """AMD's fork of BLIS. BLIS is a portable software framework for instantiating high-performance
BLAS-like dense linear algebra libraries."""
toolchain = {'name': 'GCCcore', 'version': '10.2.0'}
source_urls = ['https://github.com/amd/blis/archive/']
sources = ['%(version)s.tar.gz']
patches = ['BLIS-2.2-amd_fix-undefined-reference-blist-abort.patch']
checksums = [
'e1feb60ac919cf6d233c43c424f6a8a11eab2c62c2c6e3f2652c15ee9063c0c9', # 2.2.tar.gz
# BLIS-2.2-amd_fix-undefined-reference-blist-abort.patch
'e879bd79e4438f7e6905461af1d483d27d14945eb9e75509b22c7584b8ba93c4',
]
builddependencies = [
('binutils', '2.35'),
('Python', '3.8.6'),
('Perl', '5.32.0'),
]
# Build Serial and multithreaded library
configopts = ['--enable-cblas --enable-shared CC="$CC" auto',
'--enable-cblas --enable-threading=openmp --enable-shared CC="$CC" auto']
runtest = 'check'
sanity_check_paths = {
'files': ['include/blis/cblas.h', 'include/blis/blis.h',
'lib/libblis.a', 'lib/libblis.%s' % SHLIB_EXT,
'lib/libblis-mt.a', 'lib/libblis-mt.%s' % SHLIB_EXT],
'dirs': [],
}
modextrapaths = {'CPATH': 'include/blis'}
moduleclass = 'numlib'

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@ -0,0 +1,14 @@
fix undefined reference to 'blis_abort'
see https://github.com/flame/blis/issues/428 + https://github.com/flame/blis/pull/429
--- blis-2.2.orig/frame/base/bli_error.h 2020-12-07 19:40:33.936990613 +0100
+++ blis-2.2/frame/base/bli_error.h 2020-12-07 19:45:35.079406108 +0100
@@ -40,6 +40,7 @@
void bli_print_msg( char* str, char* file, guint_t line );
void bli_abort( void );
+BLIS_EXPORT_BLIS void bli_abort( void );
char* bli_error_string_for_code( gint_t code );

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@ -0,0 +1,39 @@
# IT4Innovations
# LK 2022
easyblock = 'ConfigureMake'
name = 'BLIS'
version = '3.0.1'
homepage = 'https://developer.amd.com/amd-cpu-libraries/blas-library/'
description = """AMD's fork of BLIS. BLIS is a portable software framework for instantiating high-performance
BLAS-like dense linear algebra libraries."""
toolchain = {'name': 'GCCcore', 'version': '10.2.0'}
source_urls = ['https://github.com/amd/blis/archive/']
sources = ['%(version)s.tar.gz']
checksums = ['dff643e6ef946846e91e8f81b75ff8fe21f1f2d227599aecd654d184d9beff3e']
builddependencies = [
('binutils', '2.35'),
('Python', '3.8.6'),
('Perl', '5.32.0'),
]
# Build Serial and multithreaded library
configopts = ['--enable-cblas --enable-shared CC="$CC" auto',
'--enable-cblas --enable-threading=openmp --enable-shared CC="$CC" auto']
runtest = 'check'
sanity_check_paths = {
'files': ['include/blis/cblas.h', 'include/blis/blis.h',
'lib/libblis.a', 'lib/libblis.%s' % SHLIB_EXT,
'lib/libblis-mt.a', 'lib/libblis-mt.%s' % SHLIB_EXT],
'dirs': [],
}
modextrapaths = {'CPATH': 'include/blis'}
moduleclass = 'numlib'

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@ -0,0 +1,40 @@
# IT4Innovations 2022
# JK
easyblock = 'PythonPackage'
name = 'Biopython'
version = '1.72'
versionsuffix = '-Python-%(pyver)s'
homepage = 'http://www.biopython.org'
description = """Biopython is a set of freely available tools for biological
computation written in Python by an international team of developers. It is
a distributed collaborative effort to develop Python libraries and
applications which address the needs of current and future work in
bioinformatics. """
toolchain = {'name': 'foss', 'version': '2020b'}
source_urls = ['http://biopython.org/DIST']
sources = [SOURCELOWER_TAR_GZ]
checksums = ['ab6b492443adb90c66267b3d24d602ae69a93c68f4b9f135ba01cb06d36ce5a2']
dependencies = [
('Python', '2.7.18'),
]
#download_dep_fail = True
#use_pip = True
#sanity_pip_check = False
skipsteps = ['sanitycheck']
sanity_check_paths = {
'files': [],
'dirs': ['lib/python%(pyshortver)s/site-packages/Bio',
'lib/python%(pyshortver)s/site-packages/BioSQL'],
}
moduleclass = 'bio'

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@ -0,0 +1,26 @@
easyblock = 'Binary'
name = 'Cordax'
version = '1.0'
versionsuffix = '-Python-%(pyver)s'
homepage = 'N/A'
description = """CORDAX is an aggregation propensity predictor based on predicted packing energies."""
toolchain = {'name': 'foss', 'version': '2020b'}
sources = ['%(namelower)s-%(version)s.tar.gz']
dependencies = [
('Python', '2.7.18'),
('matplotlib', '2.2.5', '-Python-%(pyver)s'),
('SciPy-bundle', '2020.11', '-Python-%(pyver)s'),
('scikit-learn', '0.20.4', '-Python-%(pyver)s'),
('Biopython', '1.72', '-Python-%(pyver)s'),
('FoldX', '5.0', '', True),
]
extract_sources = True
skipsteps = ['sanitycheck']
moduleclass = 'bio'

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@ -0,0 +1,71 @@
# IT4Innovations
# LK 2022
easyblock = 'CMakeMake'
name = 'DFTB+'
version = '21.2'
versionsuffix = '-Python-3.8.6'
homepage = 'https://www.dftb-plus.info'
description = """DFTB+ is a fast and efficient versatile quantum mechanical simulation package.
It is based on the Density Functional Tight Binding (DFTB) method, containing
almost all of the useful extensions which have been developed for the DFTB
framework so far. Using DFTB+ you can carry out quantum mechanical simulations
like with ab-initio density functional theory based packages, but in an
approximate way gaining typically around two order of magnitude in speed."""
toolchain = {'name': 'intel', 'version': '2020b'}
# AMD/intel cpu
import os
if os.environ.get("CLUSTERNAME") in ["KAROLINA"]:
toolchainopts = {'lowopt': True, 'optarch': 'march=core-avx2'}
else:
toolchainopts = {'lowopt': True, 'optarch': False}
local_external_dir = '%%(builddir)s/dftbplus-%%(version)s/external/%s/origin/'
local_external_extract = 'mkdir -p %s && tar -C %s' % (local_external_dir, local_external_dir)
local_external_extract += ' --strip-components=1 -xzf %%s'
sources = [
{
# DFTB+ source code
'source_urls': ['https://github.com/dftbplus/dftbplus/archive'],
'download_filename': '%(version)s.tar.gz',
'filename': SOURCE_TAR_GZ,
},
{
# Slater-Koster (slakos) data for testing
'source_urls': ['https://github.com/dftbplus/testparams/archive'],
'download_filename': 'd0ea16df2b56d14c7c3dc9329a8d3bac9fea50a0.tar.gz',
'filename': 'slakos-data-%(version)s.tar.gz',
'extract_cmd': local_external_extract % ('slakos', 'slakos'),
},
]
builddependencies = [
('CMake', '3.18.4'),
]
dependencies = [
('Python', '3.8.6'),
('SciPy-bundle', '2020.11'),
('arpack-ng', '3.8.0'),
('dftd3-lib', '0.9.2', '', ('GCC', '10.2.0')),
]
configopts = ['-DWITH_TBLITE=TRUE -DWITH_MPI=TRUE -DWITH_DFTD3=TRUE COMPILE_DFTD3=FALSE DFTD3_INCS="-I$EBROOTDFTD3MINLIB/include" -DFTD3_LIBS="-L$EBROOTDFTD3MINLIB/lib -ldftd3" -DWITH_PYTHON=TRUE']
installopts = 'INSTALLDIR="%(installdir)s"'
sanity_check_paths = {
'files': ['bin/' + x for x in ['dftb+', 'modes', 'waveplot']],
'dirs': []
}
sanity_check_commands = [('python', '-c "import dptools"')]
modextrapaths = {'PYTHONPATH': 'lib/python%(pyshortver)s/site-packages'}
moduleclass = 'phys'

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@ -0,0 +1,81 @@
# IT4Innovations
# LK 2022
easyblock = 'CMakeMake'
name = 'DFTB+'
version = '21.2'
versionsuffix = '-TB'
homepage = 'https://www.dftb-plus.info'
description = """DFTB+ is a fast and efficient versatile quantum mechanical simulation package.
It is based on the Density Functional Tight Binding (DFTB) method, containing
almost all of the useful extensions which have been developed for the DFTB
framework so far. Using DFTB+ you can carry out quantum mechanical simulations
like with ab-initio density functional theory based packages, but in an
approximate way gaining typically around two order of magnitude in speed."""
toolchain = {'name': 'intel', 'version': '2020b'}
# AMD/intel cpu
import os
if os.environ.get("CLUSTERNAME") in ["KAROLINA"]:
toolchainopts = {'lowopt': True, 'optarch': 'march=core-avx2'}
else:
toolchainopts = {'lowopt': True, 'optarch': False}
local_external_dir = '%%(builddir)s/dftbplus-%%(version)s/external/%s/origin/'
local_external_extract = 'mkdir -p %s && tar -C %s' % (local_external_dir, local_external_dir)
local_external_extract += ' --strip-components=1 -xzf %%s'
sources = [
{
# DFTB+ source code
'source_urls': ['https://github.com/dftbplus/dftbplus/archive'],
'download_filename': '%(version)s.tar.gz',
'filename': SOURCE_TAR_GZ,
},
{
# Slater-Koster (slakos) data for testing
'source_urls': ['https://github.com/dftbplus/testparams/archive'],
'download_filename': 'd0ea16df2b56d14c7c3dc9329a8d3bac9fea50a0.tar.gz',
'filename': 'slakos-data-%(version)s.tar.gz',
'extract_cmd': local_external_extract % ('slakos', 'slakos'),
},
]
builddependencies = [
('CMake', '3.18.4'),
]
dependencies = [
# ('Python', '3.8.6'),
# ('SciPy-bundle', '2020.11'),
('arpack-ng', '3.8.0'),
('dftd3-lib', '0.9.2', '', ('GCC', '10.2.0')),
]
configopts = ['-DWITH_TBLITE=TRUE -DWITH_MPI=TRUE -DWITH_DFTD3=TRUE COMPILE_DFTD3=FALSE DFTD3_INCS="-I$EBROOTDFTD3MINLIB/include" -DFTD3_LIBS="-L$EBROOTDFTD3MINLIB/lib -ldftd3"']
# Link to Arpack
#local_makeopts = ' WITH_TBLITE=1 WITH_MPI=1 WITH_ARPACK=1 ARPACK_LIBS="-L$EBROOTARPACKMINNG/lib -larpack" ARPACK_NEEDS_LAPACK=1'
# Use DFTD3 from EB
#local_makeopts += ' WITH_DFTD3=1 COMPILE_DFTD3=0 DFTD3_INCS="-I$EBROOTDFTD3MINLIB/include"'
#local_makeopts += ' DFTD3_LIBS="-L$EBROOTDFTD3MINLIB/lib -ldftd3"'
#buildopts = local_makeopts
#runtest = 'test' + local_makeopts
installopts = 'INSTALLDIR="%(installdir)s"'
sanity_check_paths = {
'files': ['bin/' + x for x in ['dftb+', 'modes', 'waveplot']],
'dirs': []
}
#sanity_check_commands = [('python', '-c "import dptools"')]
#modextrapaths = {'PYTHONPATH': 'lib/python%(pyshortver)s/site-packages'}
moduleclass = 'phys'

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@ -0,0 +1,18 @@
# Fix LAPACK detection with CMake
# IT4Innovations
# JK 2022
--- dakota-6.15.0-public-src-cli-ORIG/cmake/DakotaFindSystemTPLs.cmake 2022-01-27 10:41:04.173529574 +0100
+++ dakota-6.15.0-public-src-cli/cmake/DakotaFindSystemTPLs.cmake 2022-01-27 10:45:08.772867478 +0100
@@ -64,9 +64,9 @@
if(NOT BLAS_LIBS OR NOT LAPACK_LIBS)
# if not a system blas and lapack, then look for a cmake built LAPACK
# with find_package
- find_package(LAPACK REQUIRED NO_MODULE)
- set(BLAS_LIBS blas)
- set(LAPACK_LIBS lapack)
+ find_package(LAPACK REQUIRED MODULE)
+ set(BLAS_LIBS ${BLAS_LIBRARIES})
+ set(LAPACK_LIBS ${LAPACK_LIBRARIES})
endif()
endif()
endif()

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@ -0,0 +1,48 @@
# https://github.com/easybuilders/easybuild-easyconfigs/pull/12275/commits/4ee39c881caa7638c4c9ddd08b967608f844e90c
# JK 2022 upraveno
easyblock = 'CMakeMake'
name = 'Dakota'
version = '6.15.0'
homepage = 'https://dakota.sandia.gov'
description = """The Dakota project delivers both state-of-the-art research and robust, usable software for optimization and UQ. Broadly, the Dakota software's advanced parametric analyses enable design exploration, model calibration, risk analysis, and quantification of margins and uncertainty with computational models."""
toolchain = {'name': 'foss', 'version': '2021b'}
toolchainopts = {'pic': True, 'usempi': True, 'optarch': False}
sources = ['%(namelower)s-%(version)s-public-src-cli.tar.gz']
source_urls = ['https://dakota.sandia.gov/sites/default/files/distributions/public/']
checksums = ['47136b14a86143d0038735638da4578e']
patches = ['Dakota-6.15.0-fix_lapack_detection.patch']
builddependencies = [('CMake', '3.21.1')]
dependencies = [
('HDF5', '1.12.1', '-parallel'),
('Python', '3.9.6'),
('Perl', '5.34.0'),
('GSL', '2.7'),
('Boost', '1.77.0'),
]
# build shared libraries
configopts = "-DBUILD_SHARED_LIBS=ON "
# set other dependencies
configopts += "-DDAKOTA_HAVE_MPI=ON "
configopts += "-DBoost_NO_SYSTEM_PATHS=ON "
configopts += "-DDAKOTA_HAVE_HDF5=ON "
configopts += "-DDAKOTA_HAVE_GSL=ON "
runtest = ' test ARGS="-L AcceptanceTest -j %(parallel)s"'
# Run install step in parallel
installopts = ' -j %(parallel)s'
sanity_check_paths = {
'files': ["bin/dakota"],
'dirs': []
}
moduleclass = 'math'

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@ -0,0 +1,48 @@
# https://github.com/easybuilders/easybuild-easyconfigs/pull/12275/commits/4ee39c881caa7638c4c9ddd08b967608f844e90c
# JK 2022 upraveno
easyblock = 'CMakeMake'
name = 'Dakota'
version = '6.15.0'
homepage = 'https://dakota.sandia.gov'
description = """The Dakota project delivers both state-of-the-art research and robust, usable software for optimization and UQ. Broadly, the Dakota software's advanced parametric analyses enable design exploration, model calibration, risk analysis, and quantification of margins and uncertainty with computational models."""
toolchain = {'name': 'intel', 'version': '2021b'}
toolchainopts = {'pic': True, 'usempi': True, 'opt': False}
sources = ['%(namelower)s-%(version)s-public-src-cli.tar.gz']
source_urls = ['https://dakota.sandia.gov/sites/default/files/distributions/public/']
checksums = ['47136b14a86143d0038735638da4578e']
patches = ['Dakota-6.15.0-fix_lapack_detection.patch']
builddependencies = [('CMake', '3.21.1')]
dependencies = [
('HDF5', '1.12.1', '-parallel'),
('Python', '3.9.6'),
('Perl', '5.34.0'),
('GSL', '2.7'),
('Boost', '1.77.0'),
]
# build shared libraries
configopts = "-DBUILD_SHARED_LIBS=ON "
# set other dependencies
configopts += "-DDAKOTA_HAVE_MPI=ON "
configopts += "-DBoost_NO_SYSTEM_PATHS=ON "
configopts += "-DDAKOTA_HAVE_HDF5=ON "
configopts += "-DDAKOTA_HAVE_GSL=ON "
runtest = ' test ARGS="-L AcceptanceTest -j %(parallel)s"'
# Run install step in parallel
installopts = ' -j %(parallel)s'
sanity_check_paths = {
'files': ["bin/dakota"],
'dirs': []
}
moduleclass = 'math'

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@ -0,0 +1,33 @@
##
# This file is an EasyBuild reciPY as per https://github.com/easybuilders/easybuild
#
# Authors:: Inge Gutheil <i.gutheil@fz-juelich.de>, Alan O'Cais <a.ocais@fz-juelich.de>
# License:: MIT/GPL
#
##
name = 'ELPA'
version = '2020.11.001'
homepage = 'https://elpa.rzg.mpg.de'
description = """Eigenvalue SoLvers for Petaflop-Applications ."""
toolchain = {'name': 'fosscuda', 'version': '2020b'}
toolchainopts = {'openmp': True, 'usempi': True}
source_urls = ['https://elpa.rzg.mpg.de/software/tarball-archive/Releases/%(version)s/']
sources = [SOURCELOWER_TAR_GZ]
checksums = ['15591f142eeaa98ab3201d27ca9ac328e21beabf0803b011a04183fcaf6efdde']
builddependencies = [
('Autotools', '20200321'),
# remove_xcompiler script requires 'python' command,
('Python', '3.8.6'),
]
# When building in parallel, the file test_setup_mpi.mod is sometimes
# used before it is built, leading to an error. This must be a bug in
# the makefile affecting parallel builds.
maxparallel = 1
moduleclass = 'math'

24
f/FoldX/FoldX-5.0.eb Normal file
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@ -0,0 +1,24 @@
# IT4Innovations 2022
# JK
easyblock = 'Binary'
name = 'FoldX'
version = '5.0'
homepage = 'http://http://foldxsuite.crg.eu/'
description = """FoldX is used to provide a fast and quantitative estimation of the importance of the interactions
contributing to the stability of proteins and protein complexes."""
toolchain = SYSTEM
sources = ['%(namelower)s%(version_major)sLinux64.tar.gz']
extract_sources = True
sanity_check_paths = {
'files': ['foldx_20221231', 'yasaraPlugin.zip'],
'dirs': ["molecules/"]
}
moduleclass = 'bio'

48
f/Forge/Forge-21.1.3.eb Normal file
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@ -0,0 +1,48 @@
# IT4Innovations 2020
# !!! --include-easyblocks /apps/easybuild/it4i-easyblocks/easyblocks/a/allineabase.py !!!
# LK
easyblock = 'AllineaBase'
name = 'Forge'
version = "21.1.3"
homepage = 'http://www.allinea.com/products/develop-allinea-forge'
description = """Allinea Forge is the local_complete toolsuite for software development
- with everything needed to debug, profile, optimize, edit and build C, C++
and FORTRAN applications on Linux for high performance - from single threads through
to local_complex parallel HPC codes with MPI, OpenMP, threads or CUDA.
"""
toolchain = SYSTEM
source_urls = [
# Use manually downloaded sources
# http://content.allinea.com/downloads/allinea-reports-latest-Redhat-6.0-x86_64.tar
# and rename it to format %(namelower)s-%(version)s.tar, so
# forge-7.0.4.tar.
]
sources = ['arm-forge-21.1.3-linux-x86_64.tar']
skipsteps = ['configure', 'build']
postinstallcmds = [
'ln -s /apps/licenses/Arm/Licence %(installdir)s/licences/Licence.16312',
'ln -s /apps/licenses/PerformanceReports/Licence %(installdir)s/licences/Licence.16313',
]
sanity_check_paths = {
'files': [
'bin/ddt-client',
'bin/map',
'bin/ddt',
#'bin/ddt-debugger',
#'bin/ddt-debugger-ll',
#'bin/ddt-debugger-mps',
'bin/ddt-mpirun',
'bin/forge',
'bin/make-profiler-libraries'],
'dirs': ['lib'],
}
moduleclass = 'debugger'

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@ -0,0 +1,53 @@
# IT4Innovations
# LK 2022
name = 'GROMACS'
version = '2018.8'
versionsuffix = '-PLUMED-2.5.6-switch'
homepage = 'https://www.gromacs.org'
description = """
GROMACS is a versatile package to perform molecular dynamics, i.e. simulate the
Newtonian equations of motion for systems with hundreds to millions of
particles.
This is a GPU enabled build, containing both MPI and threadMPI builds.
It also contains the gmxapi extension for the single precision MPI build.
"""
toolchain = {'name': 'fosscuda', 'version': '2020b'}
toolchainopts = {'openmp': True, 'usempi': True}
source_urls = [
'https://ftp.gromacs.org/pub/gromacs/',
'ftp://ftp.gromacs.org/pub/gromacs/',
]
patches = [
'GROMACS-2018_fix_search_for_nvml_include.patch',
'GROMACS-2018_amend_search_for_nvml_lib.patch',
]
sources = ['gromacs-%(version)s.tar.gz']
builddependencies = [
('CMake', '3.18.4'),
('scikit-build', '0.11.1'),
]
dependencies = [
('Python', '3.8.6'),
('SciPy-bundle', '2020.11'),
('networkx', '2.5'),
('PLUMED', '2.5.6', '-switch'),
('Boost', '1.74.0'),
]
exts_defaultclass = 'PythonPackage'
modextrapaths = {
'PYTHONPATH': 'lib/python%(pyshortver)s/site-packages',
}
moduleclass = 'bio'

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@ -0,0 +1,72 @@
# IT4Innovations
# LK 2022
name = 'GROMACS'
version = '2021.4'
local_plum_ver = '2.7.3'
versionsuffix = '-PLUMED-%s' % local_plum_ver
homepage = 'https://www.gromacs.org'
description = """
GROMACS is a versatile package to perform molecular dynamics, i.e. simulate the
Newtonian equations of motion for systems with hundreds to millions of
particles.
This is a GPU enabled build, containing both MPI and threadMPI builds.
It also contains the gmxapi extension for the single precision MPI build.
"""
toolchain = {'name': 'foss', 'version': '2020b'}
import os
if os.environ.get("CLUSTERNAME") in ["KAROLINA"]:
toolchainopts = {'openmp': True, 'usempi': True, 'optarch': 'march=core-avx2', 'pic': True}
else:
toolchainopts = {'openmp': True, 'usempi': True, 'pic': True}
source_urls = [
'https://ftp.gromacs.org/pub/gromacs/',
'ftp://ftp.gromacs.org/pub/gromacs/',
]
sources = [SOURCELOWER_TAR_GZ]
patches = [
'GROMACS-2019_fix_omp_num_threads_and_google_test_death_style_in_tests.patch',
# 'GROMACS-2019_increase_test_timeout_for_GPU.patch',
'GROMACS-2021_fix_gmxapi_gmx_allowed_cmd_name.patch',
# 'GROMACS-2020.5_fix_threads_gpu_Gmxapitests.patch',
]
builddependencies = [
('CMake', '3.18.4'),
('scikit-build', '0.11.1'),
]
dependencies = [
('Python', '3.8.6'),
('SciPy-bundle', '2020.11'),
('networkx', '2.5'),
('PLUMED', local_plum_ver),
]
exts_defaultclass = 'PythonPackage'
exts_default_options = {
'source_urls': [PYPI_SOURCE],
'use_pip': True,
'download_dep_fail': True,
'sanity_pip_check': True,
}
exts_list = [
('gmxapi', '0.2.0', {
'preinstallopts': "export GMXTOOLCHAINDIR=%(installdir)s/share/cmake/gromacs_mpi && ",
'checksums': ['3954bf123da12fc60bcfaeed8263f5e2d3e16e5136c2bb5c8207b20fa7406788'],
}),
]
modextrapaths = {
'PYTHONPATH': 'lib/python%(pyshortver)s/site-packages',
}
moduleclass = 'bio'

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@ -0,0 +1,27 @@
# IT4Innovations
# LK 2022
name = 'HDF5'
version = '1.12.1'
homepage = 'https://portal.hdfgroup.org/display/support'
description = """HDF5 is a data model, library, and file format for storing and managing data.
It supports an unlimited variety of datatypes, and is designed for flexible
and efficient I/O and for high volume and complex data."""
toolchain = {'name': 'NVHPC', 'version': '21.11'}
toolchainopts = {'pic': True}
source_urls = ['https://support.hdfgroup.org/ftp/HDF5/releases/hdf5-%(version_major_minor)s/hdf5-%(version)s/src']
sources = [SOURCELOWER_TAR_GZ]
checksums = ['79c66ff67e666665369396e9c90b32e238e501f345afd2234186bfb8331081ca']
configopts = '--enable-fortran --enable-fortran 2003 --enable-cxx --enable-parallel --enable-shared'
dependencies = [
('OpenMPI', '4.0.6', '-CUDA-11.4.1-v2'),
('zlib', '1.2.11'),
('Szip', '2.1.1'),
]
moduleclass = 'data'

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@ -0,0 +1,31 @@
# IT4Innovations 2021
# LK
name = 'HDF5'
version = '1.12.1'
versionsuffix = '-parallel'
homepage = 'http://www.hdfgroup.org/HDF5/'
description = """HDF5 is a unique technology suite that makes possible the management of
extremely large and local_complex data collections."""
toolchain = {'name': 'foss', 'version': '2021b'}
toolchainopts = {'pic': True, 'usempi': True}
source_urls = [
'https://support.hdfgroup.org/ftp/HDF5/releases/hdf5-%(version_major_minor)s/hdf5-%(version)s/src']
sources = [SOURCELOWER_TAR_GZ]
# AMD/intel cpu
import os
if os.environ.get("CLUSTERNAME") in ["KAROLINA"]:
prebuildopts = "echo %(builddir)s && while read i; do echo $i; sed 's|-xHost|-avx2|g' -i $i; done < <(grep xHost %(builddir)s -R | cut -d ':' -f 1 | sort -u) && "
configopts = '--enable-fortran --enable-fortran 2003 --enable-cxx'
dependencies = [
('zlib', '1.2.11'),
('Szip', '2.1.1'),
]
moduleclass = 'data'

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@ -0,0 +1,30 @@
# IT4Innovations
# JK 2022
name = 'HDF5'
version = '1.12.1'
homepage = 'https://portal.hdfgroup.org/display/support'
description = """HDF5 is a data model, library, and file format for storing and managing data.
It supports an unlimited variety of datatypes, and is designed for flexible
and efficient I/O and for high volume and complex data."""
toolchain = {'name': 'iimpi', 'version': '2021b'}
toolchainopts = {'pic': True, 'usempi': True}
import os
if os.environ.get("CLUSTERNAME") in ["KAROLINA"]:
toolchainopts = {'pic': True, 'usempi': True, 'optarch': 'march=core-avx2'}
else:
toolchainopts = {'pic': True, 'usempi': True}
source_urls = ['https://support.hdfgroup.org/ftp/HDF5/releases/hdf5-%(version_major_minor)s/hdf5-%(version)s/src']
sources = [SOURCELOWER_TAR_GZ]
checksums = ['79c66ff67e666665369396e9c90b32e238e501f345afd2234186bfb8331081ca']
dependencies = [
('zlib', '1.2.11'),
('Szip', '2.1.1'),
]
moduleclass = 'data'

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@ -0,0 +1,31 @@
# IT4Innovations 2021
# LK
name = 'HDF5'
version = '1.12.1'
versionsuffix = '-parallel'
homepage = 'http://www.hdfgroup.org/HDF5/'
description = """HDF5 is a unique technology suite that makes possible the management of
extremely large and local_complex data collections."""
toolchain = {'name': 'intel', 'version': '2021b'}
toolchainopts = {'pic': True, 'usempi': True}
source_urls = [
'https://support.hdfgroup.org/ftp/HDF5/releases/hdf5-%(version_major_minor)s/hdf5-%(version)s/src']
sources = [SOURCELOWER_TAR_GZ]
# AMD/intel cpu
import os
if os.environ.get("CLUSTERNAME") in ["KAROLINA"]:
prebuildopts = "echo %(builddir)s && while read i; do echo $i; sed 's|-xHost|-avx2|g' -i $i; done < <(grep xHost %(builddir)s -R | cut -d ':' -f 1 | sort -u) && "
configopts = '--enable-fortran --enable-fortran 2003 --enable-cxx'
dependencies = [
('zlib', '1.2.11'),
('Szip', '2.1.1'),
]
moduleclass = 'data'

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@ -0,0 +1,24 @@
# IT4Innovations
# JK 2021
easyblock = 'PackedBinary'
name = 'HyperQueue'
version = '0.8.0'
homepage = 'https://it4innovations.github.io/hyperqueue/'
description = """HyperQueue lets you build a computation plan consisting of a large amount of tasks and then execute it transparently over a system like SLURM/PBS. It dynamically groups jobs into SLURM/PBS jobs and distributes them to fully utilize allocated notes. You thus do not have to manually aggregate your tasks into SLURM/PBS jobs."""
toolchain = SYSTEM
source_urls = ['https://github.com/It4innovations/hyperqueue/releases/download/v%(version)s/']
sources = ['hq-v%(version)s-linux-x64.tar.gz']
checksums = ['868b858510ef2abf4da61a66a7408283']
sanity_check_paths = {
'files': ['hq'],
'dirs': [],
}
moduleclass = 'devel'

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@ -0,0 +1,18 @@
name = 'Hypre'
version = '2.23.0'
homepage = 'https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods'
description = """Hypre is a library for solving large, sparse linear systems of equations on massively
parallel computers. The problems of interest arise in the simulation codes being developed at LLNL
and elsewhere to study physical phenomena in the defense, environmental, energy, and biological sciences."""
toolchain = {'name': 'intel', 'version': '2020b'}
toolchainopts = {'pic': True}
source_urls = ['https://github.com/hypre-space/hypre/archive/']
sources = ['v%(version)s.tar.gz']
checksums = ['8a9f9fb6f65531b77e4c319bf35bfc9d34bf529c36afe08837f56b635ac052e2']
start_dir = 'src'
moduleclass = 'numlib'

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@ -0,0 +1,34 @@
easyblock = 'PythonPackage'
name = 'h5py'
version = '3.6.0'
homepage = 'https://www.h5py.org/'
description = """HDF5 for Python (h5py) is a general-purpose Python interface to the Hierarchical Data Format library,
version 5. HDF5 is a versatile, mature scientific software library designed for the fast, flexible storage of enormous
amounts of data."""
toolchain = {'name': 'intel', 'version': '2021b'}
toolchainopts = {'usempi': True}
sources = [SOURCE_TAR_GZ]
checksums = ['8752d2814a92aba4e2b2a5922d2782d0029102d99caaf3c201a566bc0b40db29']
builddependencies = [('pkgconfig', '1.5.5', '-python')]
dependencies = [
('Python', '3.9.6'),
('SciPy-bundle', '2021.05', '', ('foss', '2021a')),
('HDF5', '1.12.1', '', ('iimpi', '2021b')),
]
use_pip = True
sanity_pip_check = True
download_dep_fail = True
# h5py's setup.py will disable setup_requires if H5PY_SETUP_REQUIRES is set to 0
# without this environment variable, pip will fetch the minimum numpy version h5py supports during install,
# even though SciPy-bundle provides a newer version that satisfies h5py's install_requires dependency.
preinstallopts = 'HDF5_MPI=ON HDF5_DIR="$EBROOTHDF5" H5PY_SETUP_REQUIRES=0 '
moduleclass = 'data'

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@ -0,0 +1,16 @@
name = 'LAPACK'
version = '3.10.0'
homepage = 'https://www.netlib.org/lapack/'
description = """LAPACK is written in Fortran90 and provides routines for solving systems of
simultaneous linear equations, least-squares solutions of linear systems of equations, eigenvalue
problems, and singular value problems."""
toolchain = {'name': 'GCC', 'version': '11.2.0'}
toolchainopts = {'pic': True}
source_urls = ['https://github.com/Reference-LAPACK/lapack/archive/']
sources = ['v%(version)s.tar.gz']
checksums = ['328c1bea493a32cac5257d84157dc686cc3ab0b004e2bea22044e0a59f6f8a19']
moduleclass = 'numlib'

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@ -0,0 +1,56 @@
# IT4Innovations
# LK 2022
easyblock = 'ConfigureMake'
name = 'libFLAME'
version = '5.2.0'
homepage = 'https://developer.amd.com/amd-cpu-libraries/blas-library/#libflame'
description = """libFLAME is a portable library for dense matrix computations,
providing much of the functionality present in LAPACK."""
toolchain = {'name': 'GCCcore', 'version': '10.2.0'}
toolchainopts = {'pic': True}
source_urls = ['https://github.com/flame/libflame/archive/']
sources = ['%(version)s.tar.gz']
checksums = ['997c860f351a5c7aaed8deec00f502167599288fd0559c92d5bfd77d0b4d475c']
# '--enable-max-arg-list-hack --enable-dynamic-build' requires 'file' function from GNU Make 4.x
builddependencies = [
('binutils', '2.35'),
('Python', '3.8.6'),
('make', '4.3'), # needed on Cent OS 7 where make 3 is installed
]
dependencies = [('BLIS', '3.0.1')]
# Use unset FLIBS to let configure pick up LDFLAGS
preconfigopts = 'unset FLIBS && '
preconfigopts += 'LIBS="-lblis $LIBS" '
preconfigopts += 'LDFLAGS="$LDFLAGS -L$EBROOTBLIS/lib -fopenmp -lm -lpthread" '
preconfigopts += 'CFLAGS="$CFLAGS -I$EBROOTBLIS/include/blis" '
configopts = '--enable-max-arg-list-hack '
configopts += '--enable-lapack2flame '
configopts += '--enable-external-lapack-interfaces '
configopts += '--enable-cblas-interfaces '
configopts += '--enable-dynamic-build '
configopts += '--enable-multithreading=openmp '
# libFLAME C++ Template API tests
# runtest = 'checkcpp LIBBLAS=$EBROOTBLIS/lib/libblis.a'
# sanity_check_commands = [
# 'cd %(builddir)s/%(namelower)s-%(version)s/test '
# '&& make LIBBLAS=$EBROOTBLIS/lib/libblis-mt.so LDFLAGS="-fopenmp -lm -lpthread" '
# '&& ./test_libfame.x'
# ]
sanity_check_paths = {
'files': ['include/FLAME.h', 'lib/libflame.a', 'lib/libflame.%s' % SHLIB_EXT],
'dirs': ['lib'],
}
moduleclass = 'numlib'

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@ -0,0 +1,66 @@
##
# This file is an EasyBuild reciPY as per https://github.com/easybuilders/easybuild
#
# Copyright:: Copyright 2017 University of Geneva
# Authors:: Yann Sagon <yann.sagon@unige.ch>
# License:: MIT/GPL
# $Id$
#
##
easyblock = 'ConfigureMake'
name = 'MaSuRCA'
version = '4.0.7'
versionsuffix = '-Perl-%(perlver)s'
homepage = 'http://www.genome.umd.edu/masurca.html'
description = '''MaSuRCA is whole genome assembly software. It combines the efficiency of the de Bruijn graph
and Overlap-Layout-Consensus (OLC) approaches. MaSuRCA can assemble data sets containing
only short reads from Illumina sequencing or a mixture of short reads and long reads
(Sanger, 454, Pacbio and Nanopore).'''
toolchain = {'name': 'foss', 'version': '2020a'}
# need a temporary url to download it. Do it manually here:
# http://www.genome.umd.edu/masurca_form.html
source_urls = ['https://github.com/alekseyzimin/masurca/releases/download/v%(version)s']
sources = ['%(name)s-%(version)s.tar.gz']
checksums = ['6ad1c06396cc1bd6025d37f0fcce617633c57d414954cdc7dd7c70eec8a09154']
dependencies = [
('libreadline', '8.0'),
('Tcl', '8.6.10'),
('Boost', '1.72.0'),
('zlib', '1.2.11'),
('Perl', '5.30.2'),
('bzip2', '1.0.8'),
]
buildopts = "install-special"
start_dir = "global-1"
postinstallcmds = [
# fix location of 'bin' in install prefix in runCA and runCA-dedupe scripts
# escaping single quotes within single quotes is tricky, so we use $'...' to use ANSI C-like escaping
"sed -i $'s|^$bin =.*|$bin = \"$ENV{\'EBROOTMASURCA\'}/bin\";|g' %(installdir)s/bin/runCA",
"sed -i $'s|^$bin =.*|$bin = \"$ENV{\'EBROOTMASURCA\'}/bin\";|g' %(installdir)s/bin/runCA-dedupe",
# fix hardcoded path in masurca script, just point back to 'bin' subdirectory instead
"sed -i 's@../CA8/Linux-amd64/bin@../bin@g' %(installdir)s/bin/masurca",
# commands to install built-in version of Flye
"cd ../Flye && make && cp -a ../Flye %(installdir)s",
]
sanity_check_paths = {
'files': ['bin/masurca', 'Flye/bin/flye'],
'dirs': ['include', 'lib'],
}
sanity_check_commands = [
"masurca --help",
"runCA --help",
]
moduleclass = 'bio'

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@ -0,0 +1,23 @@
# IT4Innovations 2021
# LK
name = 'Molpro'
version = '2022.1.1'
versionprefix = 'mpp-'
versionsuffix = '.linux_x86_64_mpipr'
homepage = 'https://www.molpro.net'
description = """Molpro is a local_complete system of ab initio programs for molecular electronic structure calculations."""
toolchain = SYSTEM
# no source URL available, requires registration to download
sources = ['%(namelower)s-%(versionprefix)s%(version)s%(versionsuffix)s.sh']
precompiled_binaries = True
# license file - uncomment if a licence file is supplied by your site and
# is valid for all users - the value of license_file may have to be changed
# license_file = HOME + '/licenses/%(name)s/license.lic'
moduleclass = 'chem'

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@ -0,0 +1,23 @@
# IT4Innovations 2021
# LK
name = 'Molpro'
version = '2022.1.1'
versionprefix = 'mpp-'
versionsuffix = '.linux_x86_64_sockets'
homepage = 'https://www.molpro.net'
description = """Molpro is a local_complete system of ab initio programs for molecular electronic structure calculations."""
toolchain = SYSTEM
# no source URL available, requires registration to download
sources = ['%(namelower)s-%(versionprefix)s%(version)s%(versionsuffix)s.sh']
precompiled_binaries = True
# license file - uncomment if a licence file is supplied by your site and
# is valid for all users - the value of license_file may have to be changed
# license_file = HOME + '/licenses/%(name)s/license.lic'
moduleclass = 'chem'

20
n/nompi/nompi-2022a.eb Normal file
View File

@ -0,0 +1,20 @@
# IT4Innovations
# LK 2022
easyblock = "Toolchain"
name = 'nompi'
version = '2022a'
homepage = '(none)'
description = """NVHPC compiler, including OpenMPI for MPI support."""
toolchain = SYSTEM
dependencies = [
('NVHPC', '21.11'),
('OpenMPI', '4.0.6', '-NVHPC-21.11-CUDA-11.4.1'),
('CUDAcore', '11.4.1'),
]
moduleclass = 'toolchain'

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@ -0,0 +1,36 @@
# IT4Innovations 2021
# LK JK
easyblock = "PackedBinary"
name = "ORCA"
version = '5.0.3'
versionsuffix = '-OpenMPI-4.1.1'
homepage = 'http://cec.mpg.de/forum/'
description = """ORCA is a flexible, efficient and easy-to-use general purpose tool for quantum chemistry
with specific emphasis on spectroscopic properties of open-shell molecules.
It features a wide variety of standard quantum chemical methods ranging from semiempirical methods to DFT to single-
and multireference correlated ab initio methods.
It can also treat environmental and relativistic effects."""
toolchain = SYSTEM
# Download from https://orcaforum.kofo.mpg.de
sources = ['orca_5_0_3_linux_x86-64_openmpi411.tar.gz']
checksums = ['4d860698816e83793a0e5889cbdf17a24c81bd0ec9af358f7dc9369abf6fca59']
dependencies = [('OpenMPI', '4.1.1', '-GCC-10.2.0')]
sanity_check_paths = {
'files': ['orca_%s%s' % (x, y) for x in ['anoint', 'casscf', 'cis', 'cpscf',
'eprnmr', 'gtoint', 'mdci', 'mp2', 'mrci', 'pc',
'rocis', 'scf', 'scfgrad', 'soc'] for y in ['', '_mpi']] +
['orca_%s' % x for x in ['2mkl', 'asa', 'chelpg', 'ciprep', 'eca', 'ecplib',
'euler', 'fci', 'fitpes', 'gstep', 'loc', 'mapspc',
'md', 'mergefrag', 'ndoint', 'numfreq', 'plot',
'pltvib', 'pop', 'rel', 'vib', 'vpot']] +
['orca'],
'dirs': [],
}
moduleclass = 'chem'

View File

@ -51,6 +51,7 @@ configopts += '--with-netcdf-prefix=$EBROOTNETCDFMINFORTRAN '
#configopts += '--with-etsf-io-prefix=$EBROOTETSF_IO '
#configopts += '--with-pfft-prefix=$EBROOTPFFT --with-mpifftw-prefix=$EBROOTFFTW '
# approx. 8/228 checks fail
#runtest = 'MPIEXEC=`which mpirun` check'
#sanity_check_paths = {

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@ -0,0 +1,89 @@
# IT4Innovations
# LK 2022
name = 'OpenCV'
version = '4.5.3'
versionsuffix = '-CUDA-%(cudaver)s-contrib'
# the hash is version dependent! see 3rdparty/ippicv/ippicv.cmake
local_ippicv_hash = 'a56b6ac6f030c312b2dce17430eef13aed9af274'
homepage = 'https://opencv.org/'
description = """OpenCV (Open Source Computer Vision Library) is an open source computer vision
and machine learning software library. OpenCV was built to provide
a common infrastructure for computer vision applications and to accelerate
the use of machine perception in the commercial products.
Includes extra modules for OpenCV from the contrib repository."""
toolchain = {'name': 'foss', 'version': '2021a'}
sources = [
{'source_urls': ['https://github.com/opencv/opencv/archive/'],
'download_filename': '%(version)s.zip', 'filename': SOURCELOWER_ZIP},
{'source_urls': ['https://github.com/opencv/opencv_contrib/archive/'],
'download_filename': '%(version)s.zip', 'filename': '%(namelower)s_contrib-%(version)s.zip'},
{'source_urls': ['https://raw.githubusercontent.com/opencv/opencv_3rdparty/%s/ippicv' % local_ippicv_hash],
'filename': 'ippicv_2020_lnx_intel64_20191018_general.tgz', 'extract_cmd': "cp %s %(builddir)s"},
]
checksums = [
'a61e7a4618d353140c857f25843f39b2abe5f451b018aab1604ef0bc34cd23d5', # opencv-4.5.3.zip
'dc3317950cf0d6cab6d24ec8df864d5e7c4efe39627dbd1c7c177dc12a8bcd78', # opencv_contrib-4.5.3.zip
'08627fa5660d52d59309a572dd7db5b9c8aea234cfa5aee0942a1dd903554246', # ippicv_2020_lnx_intel64_20191018_general.tgz
]
builddependencies = [
('CMake', '3.20.1'),
]
dependencies = [
('Python', '3.9.5'),
('SciPy-bundle', '2021.05'), # for numpy
('zlib', '1.2.11'),
('FFmpeg', '4.3.2'),
('freetype', '2.10.4'),
('HarfBuzz', '2.8.1'),
('libjpeg-turbo', '2.0.6'),
('libpng', '1.6.37'),
('LibTIFF', '4.2.0'),
('libwebp', '1.2.0'),
('OpenEXR', '3.0.1'),
('JasPer', '2.0.28'),
('Java', '11', '', True),
('ant', '1.10.9', '-Java-%(javaver)s', True),
('GLib', '2.68.2'),
('GTK3', '3.24.29'),
('HDF5', '1.10.7'), # needed by hdf from contrib
('CUDA', '11.3.1', '', True),
('cuDNN', '8.2.1.32', '-CUDA-%(cudaver)s', True),
]
# XXXX in configurations is a bug fix in OpenCV because ocv_check_modules is not able to recognize freetype and harfbuzz
# ref: https://github.com/opencv/opencv/blob/6e8daaec0f46aaba9ea22e2afce47307b1dbff9f/cmake/OpenCVUtils.cmake#L861
configopts = '-DOPENCV_EXTRA_MODULES_PATH=%(builddir)s/%(namelower)s_contrib-%(version)s/modules '
configopts += '-DFREETYPE_FOUND=ON '
configopts += '-DFREETYPE_INCLUDE_DIRS=$EBROOTFREETYPE/include/freetype2/ '
configopts += '-DFREETYPE_LIBRARIES=$EBROOTFREETYPE/lib64/libfreetype.so '
configopts += '-DFREETYPE_LINK_LIBRARIES=$EBROOTFREETYPE/lib64/libfreetype.so '
configopts += '-DFREETYPE_LINK_LIBRARIES_XXXXX=ON '
configopts += '-DHARFBUZZ_FOUND=ON '
configopts += '-DHARFBUZZ_INCLUDE_DIRS=$EBROOTHARFBUZZ/include/harfbuzz '
configopts += '-DHARFBUZZ_LIBRARIES=$EBROOTHARFBUZZ/lib64/libharfbuzz.so '
configopts += '-DHARFBUZZ_LINK_LIBRARIES=$EBROOTHARFBUZZ/lib64/libharfbuzz.so '
configopts += '-DHARFBUZZ_LINK_LIBRARIES_XXXXX=ON '
configopts += '-DBUILD_opencv_python2=OFF '
enhance_sanity_check = True
local_contrib_libs = [
'aruco', 'bgsegm', 'bioinspired', 'ccalib', 'datasets', 'dnn_objdetect', 'dnn_superres', 'dpm', 'face', 'freetype',
'fuzzy', 'hdf', 'hfs', 'img_hash', 'line_descriptor', 'optflow', 'phase_unwrapping', 'plot', 'quality', 'reg',
'rgbd', 'saliency', 'shape', 'stereo', 'structured_light', 'superres', 'surface_matching', 'text', 'tracking',
'videostab', 'xfeatures2d', 'ximgproc', 'xobjdetect', 'xphoto'
]
sanity_check_paths = {
'files': ['lib64/libopencv_%s.%s' % (l, SHLIB_EXT) for l in local_contrib_libs],
'dirs': [],
}
moduleclass = 'vis'

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@ -0,0 +1,84 @@
# IT4Innovations
# LK 2022
name = 'OpenCV'
version = '4.5.5'
versionsuffix = '-CUDA-%(cudaver)s-contrib'
# the hash is version dependent! see 3rdparty/ippicv/ippicv.cmake
local_ippicv_hash = 'a56b6ac6f030c312b2dce17430eef13aed9af274'
homepage = 'https://opencv.org/'
description = """OpenCV (Open Source Computer Vision Library) is an open source computer vision
and machine learning software library. OpenCV was built to provide
a common infrastructure for computer vision applications and to accelerate
the use of machine perception in the commercial products.
Includes extra modules for OpenCV from the contrib repository."""
toolchain = {'name': 'foss', 'version': '2021a'}
sources = [
{'source_urls': ['https://github.com/opencv/opencv/archive/'],
'download_filename': '%(version)s.zip', 'filename': SOURCELOWER_ZIP},
{'source_urls': ['https://github.com/opencv/opencv_contrib/archive/'],
'download_filename': '%(version)s.zip', 'filename': '%(namelower)s_contrib-%(version)s.zip'},
{'source_urls': ['https://raw.githubusercontent.com/opencv/opencv_3rdparty/%s/ippicv' % local_ippicv_hash],
'filename': 'ippicv_2020_lnx_intel64_20191018_general.tgz', 'extract_cmd': "cp %s %(builddir)s"},
]
builddependencies = [
('CMake', '3.20.1'),
]
dependencies = [
('Python', '3.9.5'),
('SciPy-bundle', '2021.05'), # for numpy
('zlib', '1.2.11'),
('FFmpeg', '4.3.2'),
('freetype', '2.10.4'),
('HarfBuzz', '2.8.1'),
('libjpeg-turbo', '2.0.6'),
('libpng', '1.6.37'),
('LibTIFF', '4.2.0'),
('libwebp', '1.2.0'),
('OpenEXR', '3.0.1'),
('JasPer', '2.0.28'),
('Java', '11', '', True),
('ant', '1.10.9', '-Java-%(javaver)s', True),
('GLib', '2.68.2'),
('GTK3', '3.24.29'),
('HDF5', '1.10.7'), # needed by hdf from contrib
('CUDA', '11.4.1', '', True),
('cuDNN', '8.2.2.26', '-CUDA-%(cudaver)s', True),
]
# XXXX in configurations is a bug fix in OpenCV because ocv_check_modules is not able to recognize freetype and harfbuzz
# ref: https://github.com/opencv/opencv/blob/6e8daaec0f46aaba9ea22e2afce47307b1dbff9f/cmake/OpenCVUtils.cmake#L861
configopts = '-DOPENCV_EXTRA_MODULES_PATH=%(builddir)s/%(namelower)s_contrib-%(version)s/modules '
configopts += '-DFREETYPE_FOUND=ON '
configopts += '-DFREETYPE_INCLUDE_DIRS=$EBROOTFREETYPE/include/freetype2/ '
configopts += '-DFREETYPE_LIBRARIES=$EBROOTFREETYPE/lib64/libfreetype.so '
configopts += '-DFREETYPE_LINK_LIBRARIES=$EBROOTFREETYPE/lib64/libfreetype.so '
configopts += '-DFREETYPE_LINK_LIBRARIES_XXXXX=ON '
configopts += '-DHARFBUZZ_FOUND=ON '
configopts += '-DHARFBUZZ_INCLUDE_DIRS=$EBROOTHARFBUZZ/include/harfbuzz '
configopts += '-DHARFBUZZ_LIBRARIES=$EBROOTHARFBUZZ/lib64/libharfbuzz.so '
configopts += '-DHARFBUZZ_LINK_LIBRARIES=$EBROOTHARFBUZZ/lib64/libharfbuzz.so '
configopts += '-DHARFBUZZ_LINK_LIBRARIES_XXXXX=ON '
configopts += '-DBUILD_opencv_python2=OFF '
enhance_sanity_check = True
local_contrib_libs = [
'aruco', 'bgsegm', 'bioinspired', 'ccalib', 'datasets', 'dnn_objdetect', 'dnn_superres', 'dpm', 'face', 'freetype',
'fuzzy', 'hdf', 'hfs', 'img_hash', 'line_descriptor', 'optflow', 'phase_unwrapping', 'plot', 'quality', 'reg',
'rgbd', 'saliency', 'shape', 'stereo', 'structured_light', 'superres', 'surface_matching', 'text', 'tracking',
'videostab', 'xfeatures2d', 'ximgproc', 'xobjdetect', 'xphoto'
]
sanity_check_paths = {
'files': ['lib64/libopencv_%s.%s' % (l, SHLIB_EXT) for l in local_contrib_libs],
'dirs': [],
}
moduleclass = 'vis'

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@ -0,0 +1,87 @@
# IT4Innovations
# LK 2022
name = 'OpenMPI'
version = '4.1.1'
homepage = 'https://www.open-mpi.org/'
description = """The Open MPI Project is an open source MPI-3 implementation."""
toolchain = {'name': 'GCC', 'version': '11.2.0'}
source_urls = ['https://www.open-mpi.org/software/ompi/v%(version_major_minor)s/downloads']
sources = [SOURCELOWER_TAR_BZ2]
patches = [
'OpenMPI-4.1.1_fix-bufferoverflow-in-common_ofi.patch',
'OpenMPI-4.0.6_remove-pmix-check-in-pmi-switch.patch',
'OpenMPI-4.1.1_opal-pmix-package-rank.patch',
'OpenMPI-4.1.1_pmix3x-protection.patch',
'OpenMPI-4.1.0-1-pml-ucx-datatype-memleak.patch',
]
checksums = [
'e24f7a778bd11a71ad0c14587a7f5b00e68a71aa5623e2157bafee3d44c07cda', # openmpi-4.1.1.tar.bz2
# OpenMPI-4.1.1_fix-bufferoverflow-in-common_ofi.patch
'a189d834506f3d7c31eda6aa184598a3631ea24a94bc551d5ed1f053772ca49e',
# OpenMPI-4.0.6_remove-pmix-check-in-pmi-switch.patch
'8acee6c9b2b4bf12873a39b85a58ca669de78e90d26186e52f221bb4853abc4d',
'04353672cf7be031e5306c94068d7012d99e6cd94b69d93230797ffcd7f31903', # OpenMPI-4.1.1_opal-pmix-package-rank.patch
'384ef9f1fa803b0d71dae2ec0748d0f20295992437532afedf21478bda164ff8', # OpenMPI-4.1.1_pmix3x-protection.patch
# OpenMPI-4.1.0-1-pml-ucx-datatype-memleak.patch
'a94a74b174ce783328abfd3656ff5196b89ef4c819fe4c8b8a0f1277123e76ea',
]
builddependencies = [
('pkg-config', '0.29.2'),
]
dependencies = [
('zlib', '1.2.11'),
('hwloc', '2.5.0'),
('libevent', '2.1.12'),
('UCX', '1.11.2'),
('libfabric', '1.13.2'),
('PMIx', '4.1.0'),
]
configopts = '--enable-shared --enable-mpi-thread-multiple --with-verbs '
configopts += '--enable-mpirun-prefix-by-default '
configopts += '--with-hwloc=$EBROOTHWLOC ' # hwloc support
configopts += '--with-tm=/opt/pbs ' # Enable PBS
configopts += '--enable-mpi-cxx ' # Enable building the C++ MPI bindings
configopts += '--with-ucx=$EBROOTUCX '
osdependencies = [('libibverbs-dev', 'libibverbs-devel', 'rdma-core-devel')]
postinstallcmds = [
'echo "# By default, for Open MPI 4.0 and later, infiniband ports on a device are not used by default." >> %(installdir)s/etc/openmpi-mca-params.conf',
'echo "btl_openib_allow_ib = true" >> %(installdir)s/etc/openmpi-mca-params.conf',
]
libs = ["mpi_mpifh", "mpi", "ompitrace", "open-pal", "open-rte"]
sanity_check_paths = {
'files': [
"bin/%s" %
binfile for binfile in [
"ompi_info", "opal_wrapper", "orterun"]] + [
"lib/lib%s.%s" %
(libfile, SHLIB_EXT) for libfile in libs] + [
"include/%s.h" %
x for x in [
"mpi-ext", "mpif-config", "mpif", "mpi", "mpi_portable_platform"]], 'dirs': [], }
import os
if os.environ.get("CLUSTERNAME") in ["BARBORA"]:
modextravars = {'OMPI_MCA_btl_openib_if_include': 'mlx5_0',
'OMPI_MCA_btl_tcp_if_include': '10.33.4.0/24',
'OMPI_MCA_orte_base_help_aggregate': '0',
}
elif os.environ.get("CLUSTERNAME") in ["KAROLINA"]:
modextravars = {'OMPI_MCA_btl_openib_if_include': 'mlx5_0',
'OMPI_MCA_orte_base_help_aggregate': '0',
}
else:
modextravars = {'OMPI_MCA_btl_openib_if_include': 'mlx4_0',
'OMPI_MCA_oob_tcp_if_include': '10.0.0.0/8',
}
moduleclass = 'mpi'

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@ -0,0 +1,73 @@
# IT4Innovations
# LK 2022
name = 'OpenMPI'
version = '4.1.2'
versionsuffix= '-Java-1.8.0_221'
homepage = 'https://www.open-mpi.org/'
description = """The Open MPI Project is an open source MPI-3 implementation."""
toolchain = {'name': 'GCC', 'version': '11.2.0'}
source_urls = ['https://www.open-mpi.org/software/ompi/v%(version_major_minor)s/downloads']
sources = [SOURCELOWER_TAR_BZ2]
checksums = ['9b78c7cf7fc32131c5cf43dd2ab9740149d9d87cadb2e2189f02685749a6b527']
builddependencies = [
('pkgconf', '1.8.0'),
]
dependencies = [
('zlib', '1.2.11'),
('hwloc', '2.5.0'),
('libevent', '2.1.12'),
('UCX', '1.11.2'),
('libfabric', '1.13.2'),
('PMIx', '4.1.0'),
('Java', '1.8.0_221', '', True),
]
configopts = '--enable-shared --enable-mpi-thread-multiple --with-verbs '
configopts += '--enable-mpirun-prefix-by-default '
configopts += '--with-hwloc=$EBROOTHWLOC ' # hwloc support
configopts += '--with-tm=/opt/pbs ' # Enable PBS
configopts += '--enable-mpi-cxx ' # Enable building the C++ MPI bindings
configopts += '--with-ucx=$EBROOTUCX '
configopts += '--enable-mpi-java ' # Java support RT#28536
osdependencies = [('libibverbs-dev', 'libibverbs-devel', 'rdma-core-devel')]
postinstallcmds = [
'echo "# By default, for Open MPI 4.0 and later, infiniband ports on a device are not used by default." >> %(installdir)s/etc/openmpi-mca-params.conf',
'echo "btl_openib_allow_ib = true" >> %(installdir)s/etc/openmpi-mca-params.conf',
]
libs = ["mpi_mpifh", "mpi", "ompitrace", "open-pal", "open-rte"]
sanity_check_paths = {
'files': [
"bin/%s" %
binfile for binfile in [
"ompi_info", "opal_wrapper", "orterun"]] + [
"lib/lib%s.%s" %
(libfile, SHLIB_EXT) for libfile in libs] + [
"include/%s.h" %
x for x in [
"mpi-ext", "mpif-config", "mpif", "mpi", "mpi_portable_platform"]], 'dirs': [], }
import os
if os.environ.get("CLUSTERNAME") in ["BARBORA"]:
modextravars = {'OMPI_MCA_btl_openib_if_include': 'mlx5_0',
'OMPI_MCA_btl_tcp_if_include': '10.33.4.0/24',
'OMPI_MCA_orte_base_help_aggregate': '0',
}
elif os.environ.get("CLUSTERNAME") in ["KAROLINA"]:
modextravars = {'OMPI_MCA_btl_openib_if_include': 'mlx5_0',
'OMPI_MCA_orte_base_help_aggregate': '0',
}
else:
modextravars = {'OMPI_MCA_btl_openib_if_include': 'mlx4_0',
'OMPI_MCA_oob_tcp_if_include': '10.0.0.0/8',
}
moduleclass = 'mpi'

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@ -0,0 +1,70 @@
# IT4Innovations
# LK 2022
name = 'OpenMPI'
version = '4.1.2'
homepage = 'https://www.open-mpi.org/'
description = """The Open MPI Project is an open source MPI-3 implementation."""
toolchain = {'name': 'GCC', 'version': '11.2.0'}
source_urls = ['https://www.open-mpi.org/software/ompi/v%(version_major_minor)s/downloads']
sources = [SOURCELOWER_TAR_BZ2]
checksums = ['9b78c7cf7fc32131c5cf43dd2ab9740149d9d87cadb2e2189f02685749a6b527']
builddependencies = [
('pkgconf', '1.8.0'),
]
dependencies = [
('zlib', '1.2.11'),
('hwloc', '2.5.0'),
('libevent', '2.1.12'),
('UCX', '1.11.2'),
('libfabric', '1.13.2'),
('PMIx', '4.1.0'),
]
configopts = '--enable-shared --enable-mpi-thread-multiple --with-verbs '
configopts += '--enable-mpirun-prefix-by-default '
configopts += '--with-hwloc=$EBROOTHWLOC ' # hwloc support
configopts += '--with-tm=/opt/pbs ' # Enable PBS
configopts += '--enable-mpi-cxx ' # Enable building the C++ MPI bindings
configopts += '--with-ucx=$EBROOTUCX '
osdependencies = [('libibverbs-dev', 'libibverbs-devel', 'rdma-core-devel')]
postinstallcmds = [
'echo "# By default, for Open MPI 4.0 and later, infiniband ports on a device are not used by default." >> %(installdir)s/etc/openmpi-mca-params.conf',
'echo "btl_openib_allow_ib = true" >> %(installdir)s/etc/openmpi-mca-params.conf',
]
libs = ["mpi_mpifh", "mpi", "ompitrace", "open-pal", "open-rte"]
sanity_check_paths = {
'files': [
"bin/%s" %
binfile for binfile in [
"ompi_info", "opal_wrapper", "orterun"]] + [
"lib/lib%s.%s" %
(libfile, SHLIB_EXT) for libfile in libs] + [
"include/%s.h" %
x for x in [
"mpi-ext", "mpif-config", "mpif", "mpi", "mpi_portable_platform"]], 'dirs': [], }
import os
if os.environ.get("CLUSTERNAME") in ["BARBORA"]:
modextravars = {'OMPI_MCA_btl_openib_if_include': 'mlx5_0',
'OMPI_MCA_btl_tcp_if_include': '10.33.4.0/24',
'OMPI_MCA_orte_base_help_aggregate': '0',
}
elif os.environ.get("CLUSTERNAME") in ["KAROLINA"]:
modextravars = {'OMPI_MCA_btl_openib_if_include': 'mlx5_0',
'OMPI_MCA_orte_base_help_aggregate': '0',
}
else:
modextravars = {'OMPI_MCA_btl_openib_if_include': 'mlx4_0',
'OMPI_MCA_oob_tcp_if_include': '10.0.0.0/8',
}
moduleclass = 'mpi'

View File

@ -6,7 +6,11 @@ description = """PETSc, pronounced PET-see (the S is silent), is a suite of data
scalable (parallel) solution of scientific applications modeled by partial differential equations."""
toolchain = {'name': 'intel', 'version': '2020b'}
toolchainopts = {'openmp': True, 'usempi': True, 'pic': True}
import os
if os.environ.get("CLUSTERNAME") in ["KAROLINA"]:
toolchainopts = {'openmp': True, 'usempi': True, 'optarch': 'march=core-avx2', 'pic': True}
else:
toolchainopts = {'openmp': True, 'usempi': True, 'pic': True}
source_urls = [
'https://ftp.mcs.anl.gov/pub/petsc/release-snapshots/',

View File

@ -16,7 +16,12 @@ description = """PLUMED is an open source library for free energy calculations i
"""
toolchain = {'name': 'fosscuda', 'version': '2020b'}
toolchainopts = {'usempi': 'True'}
import os
if os.environ.get("CLUSTERNAME") in ["KAROLINA"]:
toolchainopts = {'usempi': True, 'optarch': 'march=core-avx2'}
else:
toolchainopts = {'usempi': True}
#source_urls = ['https://github.com/plumed/plumed2/releases/download/v%(version)s/']
sources = ['plumed-2.5.6-patch.tar.gz']

View File

@ -0,0 +1,61 @@
# IT4Innovations
# LK 2022
easyblock = 'ConfigureMake'
name = 'PLUMED'
version = '2.5.6'
versionsuffix = '-switch'
homepage = 'https://www.plumed.org'
description = """PLUMED is an open source library for free energy calculations in molecular systems which
works together with some of the most popular molecular dynamics engines. Free energy calculations can be
performed as a function of many order parameters with a particular focus on biological problems, using
state of the art methods such as metadynamics, umbrella sampling and Jarzynski-equation based steered MD.
The software, written in C++, can be easily interfaced with both fortran and C/C++ codes.
"""
toolchain = {'name': 'fosscuda', 'version': '2020b'}
toolchainopts = {'usempi': 'True'}
#source_urls = ['https://github.com/plumed/plumed2/releases/download/v%(version)s/']
sources = ['plumed-2.5.6-switch.tar.gz']
checksums = ['a44b29011e0f5544470bebd3ce788e88']
dependencies = [
('zlib', '1.2.11'),
('GSL', '2.6'),
('Python', '3.8.6'),
('SciPy-bundle', '2020.11'),
('Boost', '1.74.0'),
]
preconfigopts = 'env FC=$MPIF90 LIBS="$LIBLAPACK $LIBS" '
configopts = '--exec-prefix=%(installdir)s --enable-gsl --enable-modules=all --enable-python '
configopts += '--enable-boost_graph --enable-boost_serialization '
configopts += '--enable-asmjit '
prebuildopts = 'source sourceme.sh && '
prebuildopts += 'sed "s|native|core-avx2|g" Makefile.conf -i && '
# make sure that ld.gold linker is used
# required to work around problems like "ld: BFD (GNU Binutils) 2.30 assertion fail elf.c:3564"
# (problem with intel build but maintain consistency between easyconfigs)
buildopts = 'LD_RO="ld.gold -r -o"'
# install path for PLUMED libraries must be included in $LD_LIBRARY_PATH when Python bindings get built/installed
preinstallopts = 'LD_LIBRARY_PATH="%(installdir)s/lib:$LD_LIBRARY_PATH" '
sanity_check_paths = {
'files': ['bin/plumed', 'lib/libplumedKernel.%s' % SHLIB_EXT, 'lib/libplumed.%s' % SHLIB_EXT],
'dirs': [],
}
sanity_check_commands = ["python -c 'import plumed'"]
modextrapaths = {
'PLUMED_KERNEL': 'lib/libplumedKernel.%s' % SHLIB_EXT,
'PLUMED_ROOT': 'lib/plumed',
'PYTHONPATH': 'lib/plumed/python',
}
moduleclass = 'chem'

View File

@ -0,0 +1,59 @@
# IT4Innovations
# LK 2022
easyblock = 'ConfigureMake'
name = 'PLUMED'
version = '2.7.3'
homepage = 'https://www.plumed.org'
description = """PLUMED is an open source library for free energy calculations in molecular systems which
works together with some of the most popular molecular dynamics engines. Free energy calculations can be
performed as a function of many order parameters with a particular focus on biological problems, using
state of the art methods such as metadynamics, umbrella sampling and Jarzynski-equation based steered MD.
The software, written in C++, can be easily interfaced with both fortran and C/C++ codes.
"""
toolchain = {'name': 'foss', 'version': '2020b'}
toolchainopts = {'usempi': 'True'}
source_urls = ['https://github.com/plumed/plumed2/releases/download/v%(version)s/']
sources = [SOURCE_TGZ]
checksums = ['02899545d9d83a1391b80a202f243fde']
dependencies = [
('zlib', '1.2.11'),
('GSL', '2.6'),
('Python', '3.8.6'),
('SciPy-bundle', '2020.11'),
('Boost', '1.74.0'),
]
preconfigopts = 'env FC=$MPIF90 LIBS="$LIBLAPACK $LIBS" '
configopts = '--exec-prefix=%(installdir)s --enable-gsl --enable-modules=all --enable-python '
configopts += '--enable-boost_graph --enable-boost_serialization '
configopts += '--enable-asmjit '
prebuildopts = 'source sourceme.sh && '
# make sure that ld.gold linker is used
# required to work around problems like "ld: BFD (GNU Binutils) 2.30 assertion fail elf.c:3564"
# (problem with intel build but maintain consistency between easyconfigs)
buildopts = 'LD_RO="ld.gold -r -o"'
# install path for PLUMED libraries must be included in $LD_LIBRARY_PATH when Python bindings get built/installed
preinstallopts = 'LD_LIBRARY_PATH="%(installdir)s/lib:$LD_LIBRARY_PATH" '
sanity_check_paths = {
'files': ['bin/plumed', 'lib/libplumedKernel.%s' % SHLIB_EXT, 'lib/libplumed.%s' % SHLIB_EXT],
'dirs': [],
}
sanity_check_commands = ["python -c 'import plumed'"]
modextrapaths = {
'PLUMED_KERNEL': 'lib/libplumedKernel.%s' % SHLIB_EXT,
'PLUMED_ROOT': 'lib/plumed',
'PYTHONPATH': 'lib/plumed/python',
}
moduleclass = 'chem'

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@ -1,4 +1,5 @@
# IT4Innovations 2021
# JK
easyblock = "Conda"

View File

@ -28,7 +28,7 @@ builddependencies = [('CMake', '3.20.1', '', True)]
dependencies = [
('libxml2', '2.9.10'),
('Boost', '1.74.0'),
('HDF5', '1.10.6', '-parallel'),
('HDF5', '1.10.7', '', ('iimpi', '2020b')),
('Python', '3.8.6'),
('h5py', '3.1.0'),
('SciPy-bundle', '2020.11'),

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@ -0,0 +1,64 @@
# IT4Innovations 2021
# JK2021
easyblock = 'CMakeMake'
name = 'QMCPACK'
version = '3.12.0'
versionsuffix = "-Python-%(pyver)s"
homepage = "https://qmcpack.org/"
description = """QMCPACK, is a modern high-performance open-source Quantum Monte Carlo (QMC) simulation code. Its main applications are electronic structure calculations of molecular, quasi-2D and solid-state systems. Variational Monte Carlo (VMC), diffusion Monte Carlo (DMC) and a number of other advanced QMC algorithms are implemented. Orbital space auxiliary field QMC (AFQMC) has recently been added. By directly solving the Schrodinger equation, QMC methods offer greater accuracy than methods such as density functional theory, but at a trade-off of much greater local_computational expense.
"""
# vypada to, ze od 3.12.0 uz optimalizace na avx2 nedela trable?
toolchain = {'name': 'intel', 'version': '2020b'}
import os
if os.environ.get("CLUSTERNAME") in ["KAROLINA"]:
toolchainopts = {'opt': True, 'pic': True, 'usempi': True, 'optarch': 'march=core-avx2'}
prebuildopts = "echo %(builddir)s && while read i; do echo $i; sed 's|-xHost|-march=core-avx2|g' -i $i; done < <(grep xHost %(builddir)s -R | cut -d ':' -f 1 | sort -u) &&"
else:
toolchainopts = {'opt': True, 'pic': True, 'usempi': True}
source_urls = ['https://github.com/QMCPACK/qmcpack/archive/']
sources = ['v%(version)s.tar.gz']
builddependencies = [('CMake', '3.20.1', '', True),]
# odstran z module file GCC 9.3.0 - rovnak na intel
dependencies = [
('libxml2', '2.9.10'),
('Boost', '1.74.0'),
('HDF5', '1.10.7', '', ('iimpi', '2020b')),
('Python', '3.8.6'),
('h5py', '3.1.0'),
('SciPy-bundle', '2020.11'),
('GCC', '9.3.0', '', True),
]
separate_build_dir = True
configopts = ' -DENABLE_SOA=1 '
configopts += ' -DCMAKE_C_COMPILER=mpiicc -DCMAKE_CXX_COMPILER=mpiicpc '
configopts += ' -DHDF5_PREFER_PARALLEL=1 -DENABLE_PHDF5=1 '
configopts += ' -DQMC_SYMLINK_TEST_FILES=0 '
configopts += ' -DCMAKE_BUILD_TYPE=Release '
configopts += ' -DQMC_OMP=ON '
# prekopiruje nexus knihovny o kterych install file tvrdi, ze nejsou potreba
# ale evidentne to bez nich nejede
preinstallopts = [
' mkdir -p %(installdir)s/nexus && ',
' cp -r %(builddir)s/qmcpack-%(version)s/nexus/lib %(installdir)s/nexus/lib && ',
]
# prida nexus knihovny do PYTHONPATH
modextrapaths = {'PYTHONPATH': 'nexus/lib'}
sanity_check_paths = {
'files': ['bin/qmcpack'],
'dirs': ['bin'],
}
moduleclass = 'phys'

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@ -0,0 +1,59 @@
# IT4Innovations 2021
# JK2021
easyblock = 'CMakeMake'
name = 'QMCPACK'
version = '3.12.0'
versionsuffix = "-Python-%(pyver)s-lowopt"
homepage = "https://qmcpack.org/"
description = """QMCPACK, is a modern high-performance open-source Quantum Monte Carlo (QMC) simulation code. Its main applications are electronic structure calculations of molecular, quasi-2D and solid-state systems. Variational Monte Carlo (VMC), diffusion Monte Carlo (DMC) and a number of other advanced QMC algorithms are implemented. Orbital space auxiliary field QMC (AFQMC) has recently been added. By directly solving the Schrodinger equation, QMC methods offer greater accuracy than methods such as density functional theory, but at a trade-off of much greater local_computational expense.
"""
# 3.12.0 update - už se zkompiluje bez 9.3.0
# vyžaduje GCC 9.3.0, které nemá podporu pro naše AMD procesory => kompilace s druhým AMD hackem je broken
toolchain = {'name': 'intel', 'version': '2021b'}
import os
if os.environ.get("CLUSTERNAME") in ["KAROLINA"]:
toolchainopts = {'opt': True, 'pic': True, 'usempi': True, 'optarch': 'march=core-avx2'}
# prebuildopts = "echo %(builddir)s && while read i; do echo $i; sed 's|-xHost|-march=core-avx2|g' -i $i; done < <(grep xHost %(builddir)s -R | cut -d ':' -f 1 | sort -u) &&"
else:
toolchainopts = {'opt': True, 'pic': True, 'usempi': True}
source_urls = ['https://github.com/QMCPACK/qmcpack/archive/']
sources = ['v%(version)s.tar.gz']
builddependencies = [('CMake', '3.20.1', '', True)]
dependencies = [
('libxml2', '2.9.10'),
('Boost', '1.77.0'),
('HDF5', '1.12.1', '', ('iimpi', '2021b')),
('Python', '3.9.6'),
('h5py', '3.6.0'),
('SciPy-bundle', '2021.05', '-foss-2021a', True),
# ('GCC', '9.3.0', '', True), # obejití podmínky GCC 9.3.0
]
separate_build_dir = True
configopts = ' -DENABLE_SOA=1 '
# prekopiruje nexus knihovny o kterych install file tvrdi, ze nejsou potreba
# ale evidentne to bez nich nejede
preinstallopts = [
' mkdir -p %(installdir)s/nexus && ',
' cp -r %(builddir)s/qmcpack-%(version)s/nexus/lib %(installdir)s/nexus/lib && ',
]
# prida nexus knihovny do PYTHONPATH
modextrapaths = {'PYTHONPATH': 'nexus/lib'}
sanity_check_paths = {
'files': ['bin/qmcpack'],
'dirs': ['bin'],
}
moduleclass = 'phys'

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@ -0,0 +1,61 @@
# IT4Innovations 2022
# JK
easyblock = 'CMakeMake'
name = 'QMCPACK'
version = '3.13.0'
versionsuffix = "-Python-%(pyver)s"
homepage = "https://qmcpack.org/"
description = """QMCPACK, is a modern high-performance open-source Quantum Monte Carlo (QMC) simulation code. Its main applications are electronic structure calculations of molecular, quasi-2D and solid-state systems. Variational Monte Carlo (VMC), diffusion Monte Carlo (DMC) and a number of other advanced QMC algorithms are implemented. Orbital space auxiliary field QMC (AFQMC) has recently been added. By directly solving the Schrodinger equation, QMC methods offer greater accuracy than methods such as density functional theory, but at a trade-off of much greater local_computational expense.
"""
toolchain = {'name': 'intel', 'version': '2020b'}
import os
if os.environ.get("CLUSTERNAME") in ["KAROLINA"]:
toolchainopts = {'opt': True, 'pic': True, 'usempi': True, 'optarch': 'march=core-avx2'}
prebuildopts = "echo %(builddir)s && while read i; do echo $i; sed 's|-xHost|-march=core-avx2|g' -i $i; done < <(grep xHost %(builddir)s -R | cut -d ':' -f 1 | sort -u) &&"
else:
toolchainopts = {'opt': True, 'pic': True, 'usempi': True}
source_urls = ['https://github.com/QMCPACK/qmcpack/archive/']
sources = ['v%(version)s.tar.gz']
builddependencies = [('CMake', '3.20.1', '', True),]
# odstran z module file GCC 9.3.0 - rovnak na intel
dependencies = [
('libxml2', '2.9.10'),
('Boost', '1.74.0'),
('HDF5', '1.10.7', '', ('iimpi', '2020b')),
('Python', '3.8.6'),
('h5py', '3.1.0'),
('SciPy-bundle', '2020.11'),
('GCC', '9.3.0', '', True),
]
separate_build_dir = True
configopts = ' -DENABLE_SOA=1 '
configopts += ' -DCMAKE_C_COMPILER=mpiicc -DCMAKE_CXX_COMPILER=mpiicpc '
configopts += ' -DHDF5_PREFER_PARALLEL=1 -DENABLE_PHDF5=1 '
configopts += ' -DQMC_SYMLINK_TEST_FILES=0 '
configopts += ' -DCMAKE_BUILD_TYPE=Release '
configopts += ' -DQMC_OMP=ON '
# prekopiruje nexus knihovny o kterych install file tvrdi, ze nejsou potreba
# ale evidentne to bez nich nejede
preinstallopts = [
' mkdir -p %(installdir)s/nexus && ',
' cp -r %(builddir)s/qmcpack-%(version)s/nexus/lib %(installdir)s/nexus/lib && ',
]
modextrapaths = {'PYTHONPATH': 'nexus/lib'}
sanity_check_paths = {
'files': ['bin/qmcpack'],
'dirs': ['bin'],
}
moduleclass = 'phys'

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@ -0,0 +1,54 @@
# JK 2022
name = 'QuantumESPRESSO'
version = '6.7'
homepage = 'https://www.quantum-espresso.org'
description = """Quantum ESPRESSO is an integrated suite of computer codes
for electronic-structure calculations and materials modeling at the nanoscale.
It is based on density-functional theory, plane waves, and pseudopotentials
(both norm-conserving and ultrasoft).
"""
toolchain = {'name': 'intel', 'version': '2021a'}
import os
if os.environ.get("CLUSTERNAME") in ["KAROLINA"]:
toolchainopts = {'usempi': True, 'openmp': True, 'optarch': 'march=core-avx2'}
else:
toolchainopts = {'usempi': True, 'openmp': True}
if os.environ.get("CLUSTERNAME") in ["KAROLINA"]:
prebuildopts = "echo %(builddir)s && while read i; do echo $i; sed 's|-xHost|-march=core-avx2|g' -i $i; done < <(grep xHost %(builddir)s -R | cut -d ':' -f 1 | sort -u) && "
source_urls = [
'https://github.com/QEF/q-e/releases/download/qe-%(version)s.0/',
'https://github.com/dceresoli/qe-gipaw/archive/',
'https://github.com/wannier-developers/wannier90/archive/'
]
sources = [
'qe-%(version)s-ReleasePack.tgz',
{'filename': 'qe-gipaw-%(version)s.tar.gz', 'download_filename': '%(version)sMaX.tar.gz'},
{'filename': 'wannier90-3.1.0.tar.gz', 'download_filename': 'v3.1.0.tar.gz'},
]
checksums = [
'8f06ea31ae52ad54e900a2f51afd5c70f78096d9dcf39c86c2b17dccb1ec9c87', # qe-6.7-ReleasePack.tgz
'95d2ed2f4d27f044dba171bdf8c1913a67ebc8846ed3463462828f2d414a2a61', # qe-gipaw-%(version)s.tar.gz
'40651a9832eb93dec20a8360dd535262c261c34e13c41b6755fa6915c936b254', # wannier90-3.1.0.tar.gz
]
dependencies = [
('HDF5', '1.10.7'),
('ELPA', '2021.05.001'),
('libxc', '5.1.5'),
]
# The third party packages should be installed separately and added as
# dependencies. The exception is w90, which is force built, and gipaw
# which depends on qe source
buildopts = 'all gwl xspectra couple epw gipaw w90'
# parallel build tends to fail
parallel = 1
moduleclass = 'chem'

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@ -0,0 +1,54 @@
# JK 2022
easyblock = 'ConfigureMake'
name = 'QuantumESPRESSO'
version = '7.0'
homepage = 'https://www.quantum-espresso.org'
description = """Quantum ESPRESSO is an integrated suite of computer codes
for electronic-structure calculations and materials modeling at the nanoscale.
It is based on density-functional theory, plane waves, and pseudopotentials
(both norm-conserving and ultrasoft).
"""
toolchain = {'name': 'NVHPC', 'version': '21.9'}
import os
if os.environ.get("CLUSTERNAME") in ["KAROLINA"]:
toolchainopts = {'pic': True, 'optarch': 'march=core-avx2'}
else:
toolchainopts = {'pic': True}
if os.environ.get("CLUSTERNAME") in ["KAROLINA"]:
prebuildopts = "echo %(builddir)s && while read i; do echo $i; sed 's|-xHost|-march=core-avx2|g' -i $i; done < <(grep xHost %(builddir)s -R | cut -d ':' -f 1 | sort -u) && "
source_urls = ['https://github.com/QEF/q-e/releases/download/qe-%(version)s/']
sources = ['qe-%(version)s-ReleasePack.tgz']
checksums = ['268ec506f88c56ba4e9b691c1e81e33a6ad7949f857f1c6c32197f9c2af2a957'] # qe 7.0 release pack
dependencies = [
# ('ELPA', '2021.05.001', '', ('intel', '2021a')),
# ('libxc', '5.1.3', '', ('GCC', '10.2.0')),
# ('HDF5', '1.10.7', '', ('iimpi', '2021a')),
('OpenMPI', '4.0.7', '-CUDA-11.4.1'),
]
preconfigopts = " export MPIF90=mpif90 && "
preconfigopts += " export MPIFC=mpif90 && "
preconfigopts += " export MPIF77=mpif90 && "
preconfigopts += " export MPICC=mpicc && "
preconfigopts += " export MPICXX=mpicxx && "
configopts = 'FC=pgfortran F77=pgfortran F90=pgfortran CC=pgcc CXX=pgc++ --with-cuda=$CUDA_HOME --with-cuda-cc=80 --with-cuda-runtime=11.4 --enable-openmp'
prebuildopts = "sed -i 's/-D__MPI\\b/& -D__GPU_MPI /' %(builddir)s/qe-%(version)s/make.inc && "
# only pw is available for GPU
buildopts = 'pw'
# parallel build tends to fail
parallel = 1
skipsteps = ['sanitycheck']
moduleclass = 'chem'

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@ -0,0 +1,38 @@
# IT$innovations
# LK 2022
easyblock = 'CMakeMake'
name = 'rocm-cuda2hip'
version = '4.3.1'
homepage = 'https://github.com/ROCm-Developer-Tools/HIPIFY'
description = "ROCm-HIPIFY : Tools to translate CUDA source code into portable HIP C++ automatically."
toolchain = {'name': 'gcccuda', 'version': '2020b'}
sources = ['https://github.com/ROCm-Developer-Tools/HIPIFY/archive/refs/tags/rocm-4.3.1.tar.gz']
builddependencies = [
('CMake', '3.20.1')
]
dependencies = [
('Clang', '11.0.1'),
('cuDNN', '8.2.1.32', '-CUDA-11.3.1', True),
]
postinstallcmds = [
'cp -a %(installdir)s/hipify-clang %(installdir)s/bin/rocm-cuda2hip',
'ln %(installdir)s/bin/rocm-cuda2hip %(installdir)s/bin/hipify-clang',
'ln %(installdir)s/bin/rocm-cuda2hip %(installdir)s/bin/ROCm-HIPIFY'
]
sanity_check_commands = [('hipify-clang', '--version')]
sanity_check_paths = {
'files': ['bin/%(namelower)s'],
'dirs': ['include', 'lib']
}
moduleclass = 'devel'

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@ -0,0 +1,49 @@
# IT4Innovations
# LK 2022
easyblock = 'CMakeMake'
name = 'ScaLAPACK'
version = '2.2'
homepage = 'https://www.netlib.org/scalapack/'
description = """The ScaLAPACK (or Scalable LAPACK) library includes a subset of LAPACK routines
redesigned for distributed memory MIMD parallel computers."""
toolchain = {'name': 'NVHPC', 'version': '21.11'}
toolchainopts = {'pic': True}
# https://github.com/amd/scalapack/archive/2.2.tar.gz
source_urls = ['https://github.com/amd/scalapack/archive/']
sources = ['%(version)s.tar.gz']
checksums = ['2d64926864fc6d12157b86e3f88eb1a5205e7fc157bf67e7577d0f18b9a7484c']
builddependencies = [
('CMake', '3.18.4', '', True),
]
dependencies = [
('OpenMPI', '4.0.6', '-CUDA-11.4.1-v2'),
('BLIS', '3.0.1'),
('libFLAME', '5.2.0'),
]
# Config Opts based on AOCL User Guide:
# https://developer.amd.com/wp-content/resources/AOCL_User%20Guide_2.2.pdf
configopts = '-DBUILD_SHARED_LIBS=ON '
configopts += '-DBLAS_LIBRARIES="$EBROOTBLIS/lib/libblis-mt.a" '
configopts += '-DLAPACK_LIBRARIES="$EBROOTLIBFLAME/lib/libflame.a" '
configopts += '-DCMAKE_C_COMPILER=mpicc '
configopts += '-DCMAKE_Fortran_COMPILER=mpif90 '
configopts += '-DUSE_OPTIMIZED_LAPACK_BLAS=ON '
configopts += '-DUSE_F2C=ON '
configopts += '-DCMAKE_Fortran_FLAGS="-lpthread -fopenmp $DCMAKE_Fortran_FLAGS" '
sanity_check_paths = {
'files': ['lib/libscalapack.%s' % SHLIB_EXT, 'lib64/libscalapack.%s' % SHLIB_EXT],
'dirs': ["lib", "lib64"],
}
moduleclass = 'numlib'

24
t/Tango/Tango.eb Normal file
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@ -0,0 +1,24 @@
# IT4Innovations 2022
# JK
easyblock = 'Binary'
name = 'Tango'
version = '1.0'
homepage = "N/A"
description = """N/A"""
toolchain = SYSTEM
sources = ["%(namelower)s.tar.gz"]
extract_sources = True
sanity_check_paths = {
'files': ['agadirwrapper'],
'dirs': [],
}
moduleclass = 'bio'

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@ -0,0 +1,233 @@
easyblock = 'PythonBundle'
name = 'TensorFlow'
version = '2.5.0'
homepage = 'https://www.tensorflow.org/'
description = "An open-source software library for Machine Intelligence"
toolchain = {'name': 'fosscuda', 'version': '2020b'}
toolchainopts = {'pic': True}
builddependencies = [
('Bazel', '3.7.2'),
('protobuf', '3.14.0'),
# git 2.x required, see also https://github.com/tensorflow/tensorflow/issues/29053
('git', '2.28.0', '-nodocs'),
('pybind11', '2.6.0'),
('pkgconfig', '1.5.1', '-python'), # For h5py
('UnZip', '6.0'),
]
dependencies = [
('cuDNN', '8.0.4.30', '-CUDA-%(cudaver)s', True),
('NCCL', '2.8.3', '-CUDA-%(cudaver)s'),
('Python', '3.8.6'),
('h5py', '3.1.0'),
('cURL', '7.72.0'),
('double-conversion', '3.1.5'),
('flatbuffers', '1.12.0'),
('giflib', '5.2.1'),
('hwloc', '2.2.0'),
('ICU', '67.1'),
('JsonCpp', '1.9.4'),
('libjpeg-turbo', '2.0.5'),
('LMDB', '0.9.24'),
('NASM', '2.15.05'),
('nsync', '1.24.0'),
('SQLite', '3.33.0'),
('PCRE', '8.44'),
('protobuf-python', '3.14.0'),
('flatbuffers-python', '1.12'),
('typing-extensions', '3.7.4.3'),
('libpng', '1.6.37'),
('snappy', '1.1.8'),
('zlib', '1.2.11'),
]
use_pip = True
sanity_pip_check = True
# Dependencies created and updated using findPythonDeps.sh:
# https://gist.github.com/Flamefire/49426e502cd8983757bd01a08a10ae0d
exts_list = [
('Markdown', '3.3.4', {
'checksums': ['31b5b491868dcc87d6c24b7e3d19a0d730d59d3e46f4eea6430a321bed387a49'],
}),
('pyasn1-modules', '0.2.8', {
'checksums': ['905f84c712230b2c592c19470d3ca8d552de726050d1d1716282a1f6146be65e'],
}),
('rsa', '4.7.2', {
'checksums': ['9d689e6ca1b3038bc82bf8d23e944b6b6037bc02301a574935b2dd946e0353b9'],
}),
('cachetools', '4.2.2', {
'checksums': ['61b5ed1e22a0924aed1d23b478f37e8d52549ff8a961de2909c69bf950020cff'],
}),
('google-auth', '1.30.0', {
'modulename': 'google.auth',
'checksums': ['9ad25fba07f46a628ad4d0ca09f38dcb262830df2ac95b217f9b0129c9e42206'],
}),
('oauthlib', '3.1.0', {
'checksums': ['bee41cc35fcca6e988463cacc3bcb8a96224f470ca547e697b604cc697b2f889'],
}),
('requests-oauthlib', '1.3.0', {
'checksums': ['b4261601a71fd721a8bd6d7aa1cc1d6a8a93b4a9f5e96626f8e4d91e8beeaa6a'],
}),
('google-auth-oauthlib', '0.4.4', {
'checksums': ['09832c6e75032f93818edf1affe4746121d640c625a5bef9b5c96af676e98eee'],
}),
('Werkzeug', '2.0.0', {
'checksums': ['3389bbfe6d40c6dd25e6d3f974155163c8b3de5bbda6a89342d4ab93fae80ba0'],
}),
('absl-py', '0.12.0', {
'modulename': 'absl',
'checksums': ['b44f68984a5ceb2607d135a615999b93924c771238a63920d17d3387b0d229d5'],
}),
('astunparse', '1.6.3', {
'checksums': ['5ad93a8456f0d084c3456d059fd9a92cce667963232cbf763eac3bc5b7940872'],
}),
('grpcio', '1.34.1', {
'modulename': 'grpc',
'checksums': ['1c746a3cd8a830d8d916a9d0476a786aaa98c5cc2a096344af2be955e439f8ac'],
'preinstallopts': "export GRPC_PYTHON_BUILD_EXT_COMPILER_JOBS=%(parallel)s && ",
}),
('gviz-api', '1.9.0', {
'source_tmpl': 'gviz_api-%(version)s.tar.gz',
'checksums': ['43d13ccc21834d0501b33a291ef3265e933dbb4bbdca3d34b1ed0a048c0ef640'],
}),
('tensorboard_data_server', '0.6.1', {
'source_tmpl': SOURCE_PY3_WHL, # Requires Rust to build, take the dummy for now
'checksums': ['809fe9887682d35c1f7d1f54f0f40f98bb1f771b14265b453ca051e2ce58fca7'],
}),
('tensorboard', version, {
'source_tmpl': SOURCE_PY3_WHL,
'checksums': ['e167460085b6528956b33bab1c970c989cdce47a6616273880733f5e7bde452e'],
}),
('tensorboard_plugin_wit', '1.8.0', {
'source_tmpl': SOURCE_PY3_WHL,
'checksums': ['2a80d1c551d741e99b2f197bb915d8a133e24adb8da1732b840041860f91183a'],
}),
('tensorboard_plugin_profile', '2.4.0', {
'checksums': ['dfbf254ee960440e3b2518324f876a6d6704c60b936887d99214fa36988a206a'],
}),
('google-pasta', '0.2.0', {
'modulename': 'pasta',
'checksums': ['c9f2c8dfc8f96d0d5808299920721be30c9eec37f2389f28904f454565c8a16e'],
}),
('termcolor', '1.1.0', {
'checksums': ['1d6d69ce66211143803fbc56652b41d73b4a400a2891d7bf7a1cdf4c02de613b'],
}),
('tensorflow_estimator', version, {
'source_tmpl': SOURCE_WHL,
'checksums': ['d1fe76dee8b1dcab865d807a0246da0a9c4a635b1eba6e9545bf216c3aad6955'],
}),
('astor', '0.8.1', {
'checksums': ['6a6effda93f4e1ce9f618779b2dd1d9d84f1e32812c23a29b3fff6fd7f63fa5e'],
}),
('gast', '0.4.0', {
'checksums': ['40feb7b8b8434785585ab224d1568b857edb18297e5a3047f1ba012bc83b42c1'],
}),
('opt_einsum', '3.3.0', {
'checksums': ['59f6475f77bbc37dcf7cd748519c0ec60722e91e63ca114e68821c0c54a46549'],
}),
('wrapt', '1.12.1', {
'checksums': ['b62ffa81fb85f4332a4f609cab4ac40709470da05643a082ec1eb88e6d9b97d7'],
}),
('Keras_Preprocessing', '1.1.2', {
'checksums': ['add82567c50c8bc648c14195bf544a5ce7c1f76761536956c3d2978970179ef3'],
}),
('dill', '0.3.4', {
'source_tmpl': '%(name)s-%(version)s.zip',
'checksums': ['9f9734205146b2b353ab3fec9af0070237b6ddae78452af83d2fca84d739e675'],
}),
('tblib', '1.7.0', {
'checksums': ['059bd77306ea7b419d4f76016aef6d7027cc8a0785579b5aad198803435f882c'],
}),
('portpicker', '1.3.1', {
'checksums': ['d2cdc776873635ed421315c4d22e63280042456bbfa07397817e687b142b9667'],
}),
('keras_nightly', '2.5.0.dev2021032900', {
'modulename': 'keras',
'source_tmpl': SOURCE_WHL,
'checksums': ['6ba70f738f4008222de7e7fdd5b2b18c48c49b897a9fca54c844854e25964011'],
}),
(name, version, {
'source_tmpl': 'v%(version)s.tar.gz',
'source_urls': ['https://github.com/tensorflow/tensorflow/archive/'],
'patches': [
'TensorFlow-2.1.0_fix-cuda-build.patch',
'TensorFlow-2.4.0_add-ldl.patch',
'TensorFlow-2.4.0_dont-use-var-lock.patch',
'TensorFlow-2.4.1_fix-min-priority-test.patch',
'TensorFlow-2.5.0_add-default-shell-env.patch',
'TensorFlow-2.5.0_add-protobuf-deps.patch',
'TensorFlow-2.5.0_add-support-for-large-core-systems.patch',
'TensorFlow-2.5.0_disable-avx512-extensions.patch',
'TensorFlow-2.5.0-fix-alias-violation-in-absl.patch',
'TensorFlow-2.5.0_fix-alignment-in-matmul-test.patch',
'TensorFlow-2.5.0_fix-arm-vector-intrinsics.patch',
'TensorFlow-2.5.0_fix-crash-on-shutdown.patch',
'TensorFlow-2.5.0_remove-duplicate-gpu-tests.patch',
],
'checksums': [
'233875ea27fc357f6b714b2a0de5f6ff124b50c1ee9b3b41f9e726e9e677b86c', # v2.5.0.tar.gz
'78c20aeaa7784b8ceb46238a81e8c2461137d28e0b576deeba8357d23fbe1f5a', # TensorFlow-2.1.0_fix-cuda-build.patch
'917ee7282e782e48673596d8917c3207e60e0851bb9acf230a2a439b067af2e3', # TensorFlow-2.4.0_add-ldl.patch
# TensorFlow-2.4.0_dont-use-var-lock.patch
'b14f2493fd2edf79abd1c4f2dde6c98a3e7d5cb9c25ab9386df874d5f072d6b5',
# TensorFlow-2.4.1_fix-min-priority-test.patch
'389febce9a0612fd457daf4cb18c02f77fc7311bacae0963602a3198d9f2737f',
# TensorFlow-2.5.0_add-default-shell-env.patch
'09b0c5d4ff04f56a9657875471ed78001d4201cac795aeff62019d582115b468',
# TensorFlow-2.5.0_add-protobuf-deps.patch
'2aa79b89cff13e81f83e385761917d5d6dbdffd6b0366d90580761b958f14363',
# TensorFlow-2.5.0_add-support-for-large-core-systems.patch
'915f3477d6407fafd48269fe1e684a05ce361d9b9b85e58686682df87760f636',
# TensorFlow-2.5.0_disable-avx512-extensions.patch
'3655ce24c97569ac9738c07cac85347ba6f5c815ada95b19b606ffa46d4dda03',
# TensorFlow-2.5.0-fix-alias-violation-in-absl.patch
'12454fda3330fb45cd380377e283f04488b40e0b8ae7378e786ddf731a581f75',
# TensorFlow-2.5.0_fix-alignment-in-matmul-test.patch
'6a4d6cbf45a622b8a2c3ea0b1c0171f01f595684d9c57d415bb39b1b27e1180f',
# TensorFlow-2.5.0_fix-arm-vector-intrinsics.patch
'6abfadc0f67ff3b510d70430843201cb46d7bd65db045ec9b482af70e0c8c0c8',
# TensorFlow-2.5.0_fix-crash-on-shutdown.patch
'578c7493221ebd3dc25ca43d63a72cbb28fdf4112b1e2baa7390f25781bd78fd',
# TensorFlow-2.5.0_remove-duplicate-gpu-tests.patch
'b940d438e036faac24453bff2cf1834c5e1359e87e84d1f1999fa7a30b278fec',
],
'test_script': 'TensorFlow-2.x_mnist-test.py',
'test_tag_filters_cpu': '-gpu,-tpu,-no_cuda_on_cpu_tap,-no_pip,-no_oss,-oss_serial,-benchmark-test,-v1only',
'test_tag_filters_gpu': ('gpu,-no_gpu,-nogpu,-gpu_cupti,-no_cuda11,-no_pip,-no_oss,-oss_serial,'
'-benchmark-test,-v1only'),
'test_targets': [
'//tensorflow/core/...',
'-//tensorflow/core:example_java_proto',
'-//tensorflow/core/example:example_protos_closure',
'//tensorflow/cc/...',
'//tensorflow/c/...',
'//tensorflow/python/...',
# Fails on some nodes but C API isn't installed anyway
'-//tensorflow/c/eager:c_api_test_gpu',
'-//tensorflow/c/eager:c_api_distributed_test',
'-//tensorflow/c/eager:c_api_distributed_test_gpu',
# Race condition with port picker: https://github.com/tensorflow/tensorflow/issues/46602
'-//tensorflow/c/eager:c_api_cluster_test_gpu',
'-//tensorflow/c/eager:c_api_remote_function_test_gpu',
'-//tensorflow/c/eager:c_api_remote_test_gpu',
# Fails to open its own test.xml(?)
'-//tensorflow/core/common_runtime:collective_param_resolver_local_test',
# Fails on non-AVX-512 systems: https://github.com/tensorflow/tensorflow/issues/46532
'-//tensorflow/core/common_runtime:mkl_layout_pass_test',
'-//tensorflow/core/kernels/mkl:mkl_fused_ops_test',
# Fails on AMD EPYC systems: https://github.com/tensorflow/tensorflow/issues/52151
'-//tensorflow/core/kernels/mkl:mkl_fused_batch_norm_op_test',
],
'testopts': "--test_timeout=3600 --test_size_filters=small",
'testopts_gpu': "--test_timeout=3600 --test_size_filters=small " +
"--run_under=//tensorflow/tools/ci_build/gpu_build:parallel_gpu_execute",
'with_xla': True,
}),
]
cuda_compute_capabilities = ['8.0']
moduleclass = 'lib'

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@ -0,0 +1,63 @@
# IT4Innovations
# LK 2022
easyblock = 'MakeCp'
name = 'VASP'
version = '5.4.1'
versionsuffix = '-24Jun15'
homepage = 'http://www.vasp.at'
description = """The Vienna Ab initio Simulation Package (VASP) is a local_computer program for atomic scale
materials modelling, e.g. electronic structure calculations and quantum-mechanical molecular dynamics,
from first principles.
To use VASP, You need academic licenses from University of Wiena. Follow the instructions https://www.vasp.at/index.php/faqs.
Then send us please a list of authorized users and their ID for which you need this access. Please use only http://support.it4i.cz/rt. We are responsible to verify your licenses. After succesfull verification You will be granted to use VASP in our enviroment."""
toolchain = {'name': 'intel', 'version': '2020b'}
import os
if os.environ.get("CLUSTERNAME") in ["KAROLINA"]:
toolchainopts = {'pic': True, 'usempi': True, 'optarch': 'march=core-avx2'}
else:
toolchainopts = {'pic': True, 'usempi': True}
# Vasp is proprietary software, see http://www.vasp.at/index.php/faqs on
# how to get access to the code
sources = ['vasp.5.4.1.tar.gz']
dependencies = [
('FFTW', '3.3.8'),
('zlib', '1.2.11'),
]
prebuildopts = 'cp arch/makefile.include.linux_intel ./makefile.include && '
# AMD/intel cpu
if os.environ.get("CLUSTERNAME") in ["KAROLINA"]:
prebuildopts += 'sed -i "s|-xHOST|-march=core-avx2|" makefile.include && '
# path to libfftw3xf_intel.a is hardcoded in makefile.include
prebuildopts += 'sed -i "s|\$(MKLROOT)/interfaces/fftw3xf|\$(FFTW_LIB_DIR)|" makefile.include && '
# remove mkl flag to prevent mixing dynamic libs with the static libs in
# LIBBLACS/SCALAPACK
prebuildopts += 'sed -i "s|-mkl||" makefile.include && '
# VASP uses LIBS as a list of folders
prebuildopts += 'unset LIBS && '
buildopts = 'all BLACS="$LIBBLACS" SCALAPACK="$LIBSCALAPACK"'
parallel = 1
files_to_copy = [(['bin/vasp_std', 'bin/vasp_gam', 'bin/vasp_ncl'], 'bin')]
sanity_check_paths = {
'files': ['bin/vasp_std', 'bin/vasp_gam', 'bin/vasp_ncl'],
'dirs': []
}
moduleclass = 'phys'

23
w/Waltz/Waltz.eb Normal file
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#IT4Innovations 2022
# JK
easyblock = 'Binary'
name = 'Waltz'
version = '1.0'
homepage = "N/A"
description = """N/A"""
toolchain = SYSTEM
sources = ["%(namelower)s.tar.gz"]
extract_sources = True
sanity_check_paths = {
'files': ['616.mat', 'scoreMatrixGT.pl', 'waltz616seb_nmeth2010_regions.pl'],
'dirs': [],
}
moduleclass = 'bio'

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# IT4Innovations 2021
# LK
easyblock = 'MakeCp'
name = 'Yambo'
version = '5.0.4'
homepage = 'http://www.yambo-code.org'
description = """Yambo is a FORTRAN/C code for Many-Body calculations in solid state and molecular physics.
Yambo relies on the Kohn-Sham wavefunctions generated by two DFT public codes: abinit, and PWscf."""
toolchain = {'name': 'intel', 'version': '2020a'}
import os
if os.environ.get("CLUSTERNAME") in ["KAROLINA"]:
toolchainopts = {'usempi': True, 'optarch': 'march=core-avx2'}
else:
toolchainopts = {'usempi': True}
source_urls = ['https://github.com/yambo-code/yambo/archive']
sources = ["%(version)s.tar.gz"]
dependencies = [
('netCDF-Fortran', '4.5.2'),
('libxc', '2.2.3'),
('IOTK', '1.2.2'),
]
#with_configure = True
#configopts = 'CPPFLAGS="" FCFLAGS="-nofor_main" --with-blas-libs="$LIBBLAS" '
#configopts += '--with-lapack-libs="$LIBLAPACK" --with-blacs-libs="$LIBBLACS" '
#configopts += '--with-scalapack-libs="$LIBSCALAPACK" --with-fft-libs="$LIBFFT" '
#configopts += '--with-netcdf-libs="-lnetcdff -lnetcdf" '
#configopts += '--with-hdf5-path=$EBROOTHDF5 '
#configopts += '--with-libxc-path=$EBROOTLIBXC '
#configopts += '--enable-iotk '
#onfigopts += '--with-iotk-path=$EBROOTIOTK '
#configopts += '--enable-dp --enable-memory-profile --disable-open-mp '
prebuildopts = './configure --build=x86_64-pc-linux-gnu --host=x86_64-pc-linux-gnu CPPFLAGS="" FCFLAGS="-nofor_main" --with-blas-libs="$LIBBLAS" --with-lapack-libs="$LIBLAPACK" --with-blacs-libs="$LIBBLACS" --with-scalapack-libs="$LIBSCALAPACK" --with-fft-libs="$LIBFFT" --with-netcdf-libs="-lnetcdff -lnetcdf" --with-hdf5-path=$EBROOTHDF5 --with-libxc-path=$EBROOTLIBXC --enable-iotk --with-iotk-path=$EBROOTIOTK --enable-dp --enable-memory-profile --disable-open-mp && '
buildopts = 'all'
parallel = 1
files_to_copy = [
(['bin/*'], 'bin'),
(['lib/*.a'], 'lib'),
(['include/*'], 'include'),
]
sanity_check_paths = {
'files': ['bin/' + x for x in ['a2y', 'p2y', 'yambo', 'ypp']],
'dirs': []
}
moduleclass = 'phys'