TY - GEN
T1 - Caliper
T2 - 2016 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2016
AU - Boehme, David
AU - Gamblin, Todd
AU - Beckingsale, David
AU - Bremer, Peer Timo
AU - Gimenez, Alfredo
AU - Legendre, Matthew
AU - Pearce, Olga
AU - Schulz, Martin
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/7/2
Y1 - 2016/7/2
N2 - Many performance engineering tasks, from long-term performance monitoring to post-mortem analysis and online tuning, require efficient runtime methods for introspection and performance data collection. To understand interactions between components in increasingly modular HPC software, performance introspection hooks must be integrated into runtime systems, libraries, and application codes across the software stack. This requires an interoperable, cross-stack, general-purpose approach to performance data collection, which neither application-specific performance measurement nor traditional profile or trace analysis tools provide. With Caliper, we have developed a general abstraction layer to provide performance data collection as a service to applications, runtime systems, libraries, and tools. Individual software components connect to Caliper in independent data producer, data consumer, and measurement control roles, which allows them to share performance data across software stack boundaries. We demonstrate Caliper's performance analysis capbilities with two case studies of production scenarios.
AB - Many performance engineering tasks, from long-term performance monitoring to post-mortem analysis and online tuning, require efficient runtime methods for introspection and performance data collection. To understand interactions between components in increasingly modular HPC software, performance introspection hooks must be integrated into runtime systems, libraries, and application codes across the software stack. This requires an interoperable, cross-stack, general-purpose approach to performance data collection, which neither application-specific performance measurement nor traditional profile or trace analysis tools provide. With Caliper, we have developed a general abstraction layer to provide performance data collection as a service to applications, runtime systems, libraries, and tools. Individual software components connect to Caliper in independent data producer, data consumer, and measurement control roles, which allows them to share performance data across software stack boundaries. We demonstrate Caliper's performance analysis capbilities with two case studies of production scenarios.
KW - Computer performance
KW - High performance computing
KW - Parallel processing
KW - Performance analysis
KW - Software performance
KW - Software reusability
KW - Software tools
UR - http://www.scopus.com/inward/record.url?scp=85015147335&partnerID=8YFLogxK
U2 - 10.1109/SC.2016.46
DO - 10.1109/SC.2016.46
M3 - Conference contribution
AN - SCOPUS:85015147335
T3 - International Conference for High Performance Computing, Networking, Storage and Analysis, SC
SP - 550
EP - 560
BT - Proceedings of SC 2016
PB - IEEE Computer Society
Y2 - 13 November 2016 through 18 November 2016
ER -