pyCubexR is a Python package for reading the Cube4 (.cubex) file format. Cube is used as a performance report explorer for Scalasca and Score-P. It is used as a generic tool for displaying a multidimensional 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. The Cube4 (.cubex) data format is provided for Cube files produced with the Score-P performance instrumentation and measurement infrastructure or the Scalasca version 2.x trace analyzer (and other compatible tools).
For additional information about the supported features of the Cube4 file format, see the Cube file format documentation. The report contains general information about Cube, pyCubexR, and other related tools.
For questions regarding pyCubexR please send a message to [email protected].
To install the current release, which includes support for Ubuntu and Windows:
$ pip install pycubexr
To update pyCubexR to the latest version, add --upgrade flag to the above commands.
The following code provides a minimal example that shows how pyCubexR can be used to read all metrics, callpaths, and measurement values of a .cubex file:
from pycubexr import CubexParser
cubex_file_path = "some/profile.cubex"
with CubexParser(cubex_file_path) as cubex:
# iterate over all metrics in cubex file
for metric in cubex.get_metrics():
metric_values = cubex.get_metric_values(metric=metric)
# return the name of the current metric
metric_name = metric.name
# iterate over all callpaths in cubex file
for cnode_id in metric_values.cnode_indices:
cnode = cubex.get_cnode(cnode_id)
# return the current region i.e. callpath
region = cubex.get_region(cnode)
# return the name of the current region
region_name = region.name
# return the measurement values for all mpi processes for the current metric and callpath
cnode_values = metric_values.cnode_values(cnode)Not all .cubex files must contain measurement values for all metrics for each callpath. This is especially true for MPI
functions such as MPI_Waitall. In some cases, metrics can be missing. Use the MissingMetricError to deal with these
exceptions.
from pycubexr import CubexParser
from pycubexr.utils.exceptions import MissingMetricError
cubex_file_path = "some/profile.cubex"
with CubexParser(cubex_file_path) as cubex:
for metric in cubex.get_metrics():
try:
metric_values = cubex.get_metric_values(metric=metric)
for cnode_id in metric_values.cnode_indices:
cnode = cubex.get_cnode(cnode_id)
# return only a specific number of measurement values for the current metric and callpath
cnode_values = metric_values.cnode_values(cnode)[:5]
region = cubex.get_region(cnode)
# print the data read from the file
print('\t' + '-' * 100)
print(f'\tRegion: {region.name}\n\tMetric: {metric.name}\n\tMetricValues: {cnode_values})')
except MissingMetricError as e:
# Ignore missing metrics
passThe call tree of a .cubex file can be displayed with the following code:
from pycubexr import CubexParser
cubex_file_path = "some/profile.cubex"
with CubexParser(cubex_file_path) as cubex:
# print the call tree of the .cubex file
cubex.print_calltree() In special cases, it is also possible that a .cubex file is missing measurement values for some of the callpaths of a metric or that a .cubex file of the same application contains fewer callpaths than another file. These cases need to be handled externally and are not supported by pyCubexR.
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