Power Monitoring with PAPI for Extreme Scale Architectures and Dataflow-based Programming Models

TitlePower Monitoring with PAPI for Extreme Scale Architectures and Dataflow-based Programming Models
Publication TypeConference Paper
Year of Publication2014
AuthorsMcCraw, H., J. Ralph, A. Danalis, and J. Dongarra
Conference NameWorkshop on Monitoring and Analysis for High Performance Computing Systems Plus Applications (HPCMASPA 2014), IEEE Cluster 2014
Date PublishedSeptember
PublisherIEEE
Conference LocationMadrid, Spain
Other Numberspp. 385-391
Abstract

For more than a decade, the PAPI performance- monitoring library has provided a clear, portable interface to the hardware performance counters available on all modern CPUs and other components of interest (e.g., GPUs, network, and I/O systems). Most major end-user tools that application developers use to analyze the performance of their applications rely on PAPI to gain access to these performance counters.
One of the critical roadblocks on the way to larger, more complex high performance systems, has been widely identified as being the energy efficiency constraints. With modern extreme scale machines having hundreds of thousands of cores, the ability to reduce power consumption for each CPU at the software level becomes critically important, both for economic and environmental reasons. In order for PAPI to continue playing its well established role in HPC, it is pressing to provide valuable performance data that not only originates from within the processing cores but also delivers insight into the power consumption of the system as a whole.
An extensive effort has been made to extend the Performance API to support power monitoring capabilities for various platforms. This paper provides detailed information about three components that allow power monitoring on the Intel Xeon Phi and Blue Gene/Q. Furthermore, we discuss the integration of PAPI in PARSEC – a task-based dataflow-driven execution engine – enabling hardware performance counter and power monitoring at true task granularity.

DOI
Refereed DesignationRefereed