Application power profiling on IBM Blue Gene/Q

Wallace, Sean; Vishwanath, Venkatram; Coghlan, Susan; Tramm, John R.; Lan, Zhiling; Papkay, Michael E.

doi:10.1109/cluster.2013.6702682

Cited by 18 publications

(3 citation statements)

References 24 publications

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“…The observed power graph, seen in Figure 2a, demonstrates a rapid climb to full core utilization with a reduction past five minutes as memory is freed and the program winds down. This experiment reproduces data observed in [23] where the EMON API is used directly for power readings.…”

Section: B Case Studiessupporting

confidence: 69%

“…Recent studies ( [22], [23]) show that gaining insight into the power usage and energy consumption of applications running on the BG/Q system is of high interest to the HPC community. Since PAPI is a portable cross-platform interface that allows users on many types of machines to access performance information, we build on this model and extended PAPI's previous BG/Q support (described in [4]) with power monitoring capabilities.…”

Section: Power Monitoring On Blue Gene/qmentioning

confidence: 99%

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Power monitoring with PAPI for extreme scale architectures and dataflow-based programming models

McCraw

Ralph

Danalis

et al. 2014

2014 IEEE International Conference on Cluster Computing (CLUSTER)

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Abstract-For more than a decade, the PAPI performancemonitoring 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.

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Section: B Case Studiessupporting

confidence: 69%

Section: Power Monitoring On Blue Gene/qmentioning

confidence: 99%

Power monitoring with PAPI for extreme scale architectures and dataflow-based programming models

McCraw

Ralph

Danalis

et al. 2014

2014 IEEE International Conference on Cluster Computing (CLUSTER)

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“…The power sampling rate is approximately 2 samples per second (default). We also use EMON API‐based MonEQ 50 to collect power profiling data on Mira. EMON API 45 provides 7 power domains to measure the power consumption for the node, CPU, memory, and network at the node‐card level.…”

Section: System Architectures and Environmentsmentioning

confidence: 99%

Performance and power modeling and prediction using MuMMI and 10 machine learning methods

Taylor

Lan

2022

Concurrency and Computation

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Energy-efficient scientific applications require insight into how high performance computing system features impact the applications' power and performance. This insight can result from the development of performance and power models. In this article, we use the modeling and prediction tool MuMMI (Multiple Metrics Modeling Infrastructure) and 10 machine learning methods to model and predict performance and power consumption and compare their prediction error rates. We use an algorithm-based fault-tolerant linear algebra code and a multilevel checkpointing fault-tolerant heat distribution code to conduct our modeling and prediction study on the Cray XC40 Theta and IBM BG/Q Mira at Argonne National Laboratory and the Intel Haswell cluster Shepard at Sandia National Laboratories. Our experimental results show that the prediction error rates in performance and power using MuMMI are less than 10% for most cases. By utilizing the models for runtime, node power, CPU power, and memory power, we identify the most significant performance counters for potential application optimizations, and we predict theoretical outcomes of the optimizations. Based on two collected datasets, we analyze and compare the prediction accuracy in performance and power consumption using MuMMI and 10 machine learning methods.

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Analysis of the Tradeoffs Between Energy and Run Time for Multilevel Checkpointing

Balaprakash

Bautista-Gomez

Bouguerra

et al. 2015

Lecture Notes in Computer Science

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Application power profiling on IBM Blue Gene/Q

Cited by 18 publications

References 24 publications

Power monitoring with PAPI for extreme scale architectures and dataflow-based programming models

Power monitoring with PAPI for extreme scale architectures and dataflow-based programming models

Performance and power modeling and prediction using MuMMI and 10 machine learning methods

Analysis of the Tradeoffs Between Energy and Run Time for Multilevel Checkpointing

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