Commit d92e1a47 authored by Lukáš Krupčík's avatar Lukáš Krupčík

new file: c/Cube/Cube-4.3.5.eb

	new file:   g/GROMACS/GROMACS-2016.5-intel-2017b-serial.eb
	new file:   o/OPARI2/OPARI2-2.0.2.eb
	new file:   o/OTF2/OTF2-2.1.eb
	new file:   s/Score-P/Score-P-3.1-intel-2017a.eb
	new file:   s/scikit-image/.scikit-image-0.13.1-Py-3.6.eb.swp
	modified:   s/scikit-learn/scikit-learn-0.19.1-Py-3.6.eb
parent 55fc832a
easyblock = 'EB_Score_minus_P'
name = 'Cube'
version = '4.3.5'
homepage = 'http://www.scalasca.org/software/cube-4.x/download.html'
description = """Cube, which is used as performance report explorer for Scalasca and
Score-P, is a generic tool for displaying a multi-dimensional performance space
consisting of the dimensions (i) performance metric, (ii) call path, and (iii) system
resource. Each dimension can be represented as a tree, where non-leaf nodes of the tree
can be collapsed or expanded to achieve the desired level of granularity."""
toolchain = {'name': 'dummy', 'version': ''}
sources = [SOURCELOWER_TAR_GZ]
source_urls = ['http://apps.fz-juelich.de/scalasca/releases/cube/%(version_major_minor)s/dist']
dependencies = [
('Qt', '4.8.7'),
]
sanity_check_paths = {
'files': ["bin/cube", ("lib/libcube4.a", "lib64/libcube4.a"),
("lib/libcube4.%s" % SHLIB_EXT, "lib64/libcube4.%s" % SHLIB_EXT)],
'dirs': ["include/cube", "include/cubew"],
}
moduleclass = 'perf'
name = 'GROMACS'
version = '2016.5'
homepage = 'http://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."""
toolchain = {'name': 'intel', 'version': '2017b'}
toolchainopts = {'openmp': True, 'usempi': True}
source_urls = ['http://ftp.gromacs.org/pub/gromacs/']
sources = [SOURCELOWER_TAR_GZ]
checksums = ['f41807e5b2911ccb547a3fd11f105d47']
configopts = ' -DGMX_GPU=OFF -DBUILD_SHARED_LIBS=OFF -DGMX_PREFER_STATIC_LIBS=ON -DGMX_DOUBLE=OFF -DGMX_SIMD=AVX2_256 -DGMX_BUILD_OWN_FFTW=ON -DGMX_MPI=ON'
builddependencies = [
('CMake', '3.9.1', '', True),
]
dependencies = [
('Boost', '1.66.0', '-serial'),
]
sanity_check_paths = {
'files': ['bin/gmx_mpi'],
'dirs': [''],
}
moduleclass = 'bio'
# IT4Innovations 2018
easyblock = 'ConfigureMake'
name = 'OPARI2'
version = '2.0.2'
homepage = 'http://www.score-p.org'
description = """OPARI2, the successor of Forschungszentrum Juelich's OPARI,
is a source-to-source instrumentation tool for OpenMP and hybrid codes.
It surrounds OpenMP directives and runtime library calls with calls to
the POMP2 measurement interface."""
toolchain = {'name': 'dummy', 'version': ''}
sources = [SOURCELOWER_TAR_GZ]
source_urls = ['http://www.vi-hps.org/upload/packages/opari2/']
checksums = [
'70736e5592dd0d95a73f9b74ea625a88', # opari2-2.0.2.tar.gz
]
sanity_check_paths = {
'files': ["bin/opari2", "include/opari2/pomp2_lib.h"],
'dirs': [],
}
moduleclass = 'perf'
# IT4Innovations 2018
easyblock = 'EB_Score_minus_P'
name = 'OTF2'
version = '2.1'
homepage = 'http://www.score-p.org'
description = """The Open Trace Format 2 is a highly scalable, memory efficient event
trace data format plus support library. It is the new standard trace format for
Scalasca, Vampir, and TAU and is open for other tools."""
toolchain = {'name': 'dummy', 'version': ''}
sources = [SOURCELOWER_TAR_GZ]
source_urls = ['http://www.vi-hps.org/upload/packages/otf2/']
checksums = [
'e2994e53d9b7c2cbd0c4f564d638751e', # otf2-2.1.tar.gz
]
builddependencies = [('SIONlib', '1.6.1', '-tools')]
configopts = '--enable-shared'
sanity_check_paths = {
'files': ["bin/otf2-config", "include/otf2/otf2.h", ("lib/libotf2.a", "lib64/libotf2.a")],
'dirs': [],
}
moduleclass = 'perf'
easyblock = 'EB_Score_minus_P'
name = 'Score-P'
version = '3.1'
homepage = 'http://www.score-p.org'
description = """The Score-P measurement infrastructure is a highly scalable and
easy-to-use tool suite for profiling, event tracing, and online analysis of HPC
applications."""
toolchain = {'name': 'intel', 'version': '2017a'}
sources = ['scorep-%(version)s.tar.gz']
source_urls = ['http://www.vi-hps.org/upload/packages/scorep/']
# Backported fix to prevent build-score/configure from picking up CFLAGS etc.;
# included in Score-P 2.0.2 and above
#patches = [
# 'Score-P-2.0.1_fix_score_configure.patch',
#]
checksums = [
'065bf8eb08398e8146c895718ddb9145', # scorep-2.0.1.tar.gz
]
dependencies = [
# ('libunwind', '1.1'),
('Cube', '4.3.5', '', True),
('OPARI2', '2.0.2', '', True),
('OTF2', '2.1', '', True),
('PAPI', '5.4.3', '', True),
('PDT', '3.24', '', True),
]
configopts = '--enable-shared'
sanity_check_paths = {
'files': ["bin/scorep", "include/scorep/SCOREP_User.h",
("lib/libscorep_adapter_mpi_event.a", "lib64/libscorep_adapter_mpi_event.a"),
("lib/libscorep_adapter_mpi_event.%s" % SHLIB_EXT, "lib64/libscorep_adapter_mpi_event.%s" % SHLIB_EXT)],
'dirs': [],
}
# Ensure that local metric documentation is found by Cube GUI
modextrapaths = {'CUBE_DOCPATH': 'share/doc/scorep/profile'}
moduleclass = 'perf'
# IT4Innovations 2018
easyblock = 'PythonPackage'
name = 'scikit-image'
version = '0.12.3'
homepage = 'http://scikit-learn.org/stable/index.html'
description = """Scikit-learn integrates machine learning algorithms in the tightly-knit scientific Python world,
building upon numpy, scipy, and matplotlib. As a machine-learning module,
it provides versatile tools for data mining and analysis in any field of science and engineering.
It strives to be simple and efficient, accessible to everybody, and reusable in various contexts."""
toolchain = {'name': 'Py', 'version': '3.6'}
source_urls = [PYPI_SOURCE]
sources = [SOURCE_TAR_GZ]
dependencies = [
('matplotlib', '2.1.1'),
('numpy', '1.13.3'),
('scipy', '1.0.0'),
('six', '1.11.0'),
]
options = {'modulename': 'skimage'}
sanity_check_paths = {
'files': [],
'dirs': ['lib/python3.6/site-packages/'],
}
moduleclass = 'python'
......@@ -4,7 +4,6 @@ easyblock = 'PythonPackage'
name = 'scikit-learn'
version = '0.19.1'
versionsuffix = '-Python-%(pyver)s'
homepage = 'http://scikit-learn.org/stable/index.html'
description = """Scikit-learn integrates machine learning algorithms in the tightly-knit scientific Python world,
......@@ -12,21 +11,23 @@ building upon numpy, scipy, and matplotlib. As a machine-learning module,
it provides versatile tools for data mining and analysis in any field of science and engineering.
It strives to be simple and efficient, accessible to everybody, and reusable in various contexts."""
toolchain = {'name': 'dummy', 'version': ''}
toolchain = {'name': 'Py', 'version': '3.6'}
source_urls = [PYPI_SOURCE]
sources = [SOURCE_TAR_GZ]
dependencies = [
('Python', '3.6.1'),
('matplotlib', '2.0.2', versionsuffix + '-libpng-1.6.29'),
('matplotlib', '2.1.1'),
('numpy', '1.13.3'),
('scipy', '1.0.0'),
('scikit-image', '0.13.1'),
]
options = {'modulename': 'sklearn'}
sanity_check_paths = {
'files': [],
'dirs': ['lib/python%(pyshortver)s/site-packages/sklearn'],
'dirs': ['lib/python3.6/site-packages/sklearn'],
}
moduleclass = 'data'
moduleclass = 'python'
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