Caliper: Composite Performance Data Collection in HPC Codes
Authors: David Boehme (Lawrence Livermore National Laboratory), Todd Gamblin (Lawrence Livermore National Laboratory), Peer-Timo Bremer (Lawrence Livermore National Laboratory), Olga T. Pearce (Lawrence Livermore National Laboratory), Martin Schulz (Lawrence Livermore National Laboratory)
Abstract: Correlating performance metrics with program context information is
key to understanding HPC application behavior. Given the composite
architecture of modern HPC applications, metrics and context
information must be correlated from independent places across the
software stack. Current data-collection approaches either focus on
singular performance aspects, limiting the ability to draw
correlations, or are not flexible enough to capture custom,
application-specific performance factors. With the Caliper
framework, we introduce (1) a flexible data model that can
efficiently represent arbitrary performance-related data, and (2) a
library that transparently combines performance metrics and program
context information provided by source-code annotations and
automatic measurement modules. Measurement modules and source-code
annotations in different program and system components are
independent of each other and can be combined in an arbitrary
fashion. This composite approach allows us to easily create
powerful measurement solutions that facilitate the correlation of
performance data across the software stack.
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