pFlogger: The Parallel Fortran Logging Framework for HPC
Applications
Author/Presenters
Event Type
Workshop
Software Engineering
TimeSunday, November 12th4pm -
4:15pm
Location501
DescriptionIn the context of HPC, software investments in support
of text-based diagnostics, which monitor a running
application, are typically limited compared to those for
other types of IO. Examples of such diagnostics include
reiteration of configuration parameters, progress
indicators, simple metrics (e.g., mass conservation,
convergence of solvers, etc.), and timers. To some
degree, this difference in priority is justifiable as
other forms of output are the primary products of a
scientific model, and, due to their large data volume,
much more likely to be a significant performance
concern. In contrast, text-based diagnostic content is
generally not shared beyond the individual or group
running an application and is most often used to
troubleshoot when something goes wrong.
We suggest that a more systematic approach enabled by a logging facility (or 'logger') similar to those routinely used by many communities would provide significant value to complex scientific applications. In the context of high-performance computing, an appropriate logger would provide specialized support for distributed and shared-memory parallelism and have low performance overhead. In this presentation, we present our prototype implementation of pFlogger -- a parallel Fortran-based logging framework, and assess its suitability for use in a complex scientific application.
We suggest that a more systematic approach enabled by a logging facility (or 'logger') similar to those routinely used by many communities would provide significant value to complex scientific applications. In the context of high-performance computing, an appropriate logger would provide specialized support for distributed and shared-memory parallelism and have low performance overhead. In this presentation, we present our prototype implementation of pFlogger -- a parallel Fortran-based logging framework, and assess its suitability for use in a complex scientific application.
