TAU Commander from ParaTools, Inc. is a production-grade performance engineering solution that makes The TAU Performance System users more productive. It presents a simple, intuitive, and systemized interface that guides users through performance engineering workflows and offers constructive feedback in case of error. TAU Commander also enhances the performance engineer's ability to mine actionable information from the application performance data by connecting to a suite of cloud-based data analysis, storage, visualization, and reporting services.
TAU Commander is especially useful for porting, developing, modernizing, benchmarking, and purchasing efforts where the intricacies of multi-layered software parallelism are difficult to intuit. The principal components of TAU Commander are:
- An intuitive user interface for the TAU Performance System that simplifies the process of acquiring and displaying software performance data on a variety of computer systems.
- A suite of cloud-based performance data analysis, storage, visualization, and reporting services that enhances the performance engineer's ability to mine actionable information from software application performance data.
Install a release version of TAU Commander with uv or pipx:
uv tool install taucmdr- or
pipx install taucmdr
To install a development version, check out this repo and give the path of
the checkout instead: uv tool install /path/to/taucmdr.
TAU Commander requires Python 3.11.4 or later.
TAU and its dependencies are built on demand. For shared and offline installs, see Installing TAU Commander.
Initialize a new TAU project:
- cd
/path/to/your/code tau initialize
- Add
--helpto any TAU Commander command line to see command line usage. - The command reference
describes every
taucommand. - Contact support@paratools.com with questions or feedback.
TAU Commander developers should visit the developer documentation.
Initial development of TAU Commander was supported by the United States Department of Energy under DOE SBIR award DE-SC0009593. Development of support for machine learning and artificial intelligence applications is supported by the United States Department of Energy under DOE SBIR award DE-SC0019700.
The work on extending TAU Commander for OpenSHMEM was supported by the United States Department of Defense (DoD) and used resources of the Computational Research and Development Programs and the Oak Ridge Leadership Computing Facility (OLCF) at Oak Ridge National Laboratory.