Power Measurement and Concurrency Throttling for Energy Reduction in OpenMP Programs

Porterfield, Allan; Olivier, Stephen Lecler; Bhalachandra, Sridutt; Prins, Jan F.

doi:10.1109/ipdpsw.2013.15

Cited by 46 publications

(40 citation statements)

References 18 publications

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“…DVFS is used to reduce energy consumption by lowering the operating voltage and frequency whilst causing acceptable performance degradation [Etinski et al 2012]. DCT selects the number of concurrent processing cores and threads during run-time to manage application parallelism and exchange performance for energy [Porterfield et al 2013;. Both DVFS and DCT control have been used in conjunction as run-time control approaches to achieve minimized energy consumption and a required performance target [Curtis-Maury et al 2008;Hwang and Chung 2013].…”

Section: Run-time Power and Performance Optimizationmentioning

confidence: 99%

“…Firstly, existing approaches [Porterfield et al 2013] and [Curtis-Maury et al 2008] ignore energy minimization in the sequential part of the application, which can be significant. Secondly, these approaches [Curtis-Maury et al 2008;Hwang and Chung 2013] use offline training processes to learn the system architecture and control DVFS and/or DCT.…”

Section: Run-time Power and Performance Optimizationmentioning

confidence: 99%

See 1 more Smart Citation

Runtime Performance and Power Optimization of Parallel Disparity Estimation on Many-Core Platforms

Leech

Kumar

Acharyya

et al. 2017

ACM Trans. Embed. Comput. Syst.

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This paper investigates the use of many-core systems to execute the disparity estimation algorithm, used in stereo vision applications, as these systems can provide flexibility between performance scaling and power consumption. We present a learning-based run-time management approach which achieves a required performance threshold whilst minimizing power consumption through dynamic control of frequency and core allocation. Experimental results are obtained from a 61-core Intel Xeon Phi platform for the above investigation. The same performance can be achieved with an average reduction in power consumption of 27.8% and increased energy efficiency by 30.04% when compared to DVFS control alone without run-time management.

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Section: Run-time Power and Performance Optimizationmentioning

confidence: 99%

Section: Run-time Power and Performance Optimizationmentioning

confidence: 99%

Runtime Performance and Power Optimization of Parallel Disparity Estimation on Many-Core Platforms

Leech

Kumar

Acharyya

et al. 2017

ACM Trans. Embed. Comput. Syst.

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show abstract

“…Their has been a lot of work on reducing the energy consumption by applications while minimizing the degradation in performance. For instance, Porterfield et al in [13] conserve energy consumption by using an adaptive runtime system to automatically throttle the number of concurrently used threads based on power and energy consumption gathered by using the RAPL interface. Mammela [14] et al have proposed a scheduling scheme to reduce energy consumption of an entire data center using DVFS.…”

Section: Related Workmentioning

confidence: 99%

Maximizing Throughput of Overprovisioned HPC Data Centers Under a Strict Power Budget

Sarood

Langer

Gupta

et al. 2014

SC14: International Conference for High Performance Computing, Networking, Storage and Analysis

105

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Abstract-Building future generation supercomputers while constraining their power consumption is one of the biggest challenges faced by the HPC community. For example, US Department of Energy has set a goal of 20 MW for an exascale (10 18 flops) supercomputer. To realize this goal, a lot of research is being done to revolutionize hardware design to build power efficient computers and network interconnects. In this work, we propose a software-based online resource management system that leverages hardware facilitated capability to constrain the power consumption of each node in order to optimally allocate power and nodes to a job. Our scheme uses this hardware capability in conjunction with an adaptive runtime system that can dynamically change the resource configuration of a running job allowing our resource manager to re-optimize allocation decisions to running jobs as new jobs arrive, or a running job terminates.We also propose a performance modeling scheme that estimates the essential power characteristics of a job at any scale. The proposed online resource manager uses these performance characteristics for making scheduling and resource allocation decisions that maximize the job throughput of the supercomputer under a given power budget. We demonstrate the benefits of our approach by using a mix of jobs with different powerresponse characteristics. We show that with a power budget of 4.75 MW, we can obtain up to 5.2X improvement in job throughput when compared with the SLURM scheduling policy that is power-unaware. We corroborate our results with real experiments on a relatively small scale cluster, in which we obtain a 1.7X improvement.

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“…DCT selects the number of concurrent processing cores and the threads running on them during runtime to manage application parallelism and trade-off performance for energy consumption. Porterfield et al [6] showed a DCT control approach highlighting a study of the energy and performance variations (from 20% to 2X) for various core allocations. Based on the study an OpenMP-based adaptive runtime core allocation method was shown using CPU performance counters at regular intervals.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Energy Minimization of OpenMP Parallel Applications on Many-Core Systems

Shafik

Das

Yang

et al. 2015

Proceedings of the 6th Workshop on Parallel Programming and Run-Time Management Techniques for Many-Core Architectures

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Abstract-Energy minimization of parallel applications is an emerging challenge for current and future generations of manycore computing systems. In this paper, we propose a novel and scalable energy minimization approach that suitably applies DVFS in the sequential part and jointly considers DVFS and dynamic core allocations in the parallel part. Fundamental to this approach is an iterative learning based control algorithm that adapt the voltage/frequency scaling and core allocations dynamically based on workload predictions and is guided by the CPU performance counters at regular intervals. The adaptation is facilitated through performance annotations in the application codes, defined in a modified OpenMP runtime library. The proposed approach is validated on an Intel Xeon E5-2630 platform with up to 24 CPUs running NAS parallel benchmark applications. We show that our proposed approach can effectively adapt to different architecture and core allocations and minimize energy consumption by up to 17% compared to the existing approaches for a given performance requirement.Keywords-Many-core, OpenMP, Energy minimization.I. INTRODUCTION Silicon technology scaling has enabled the fabrication of many interconnected cores on a single chip for current and future generations of computing systems. The emergence of such systems has facilitated computing performance at unprecedented levels with application parallelization and architectural support. However, higher device-level integration and operating frequency in these systems have rendered exponentially increased power density and energy consumption [1]. Hence, minimizing the energy consumption, while delivering the required performance is a key design challenge for many-core applications [2], [5].The continuing performance growth of many-core applications has been facilitated by parallel programming models. OpenMP is one such programming model, considered as the de facto standard of shared memory multiprocessing [3]. It features compiler-enabled annotations that can achieve data-or task-level parallelization. The parallelization is facilitated by runtime libraries that can allocate the number of computing nodes and suitably schedule the parallel tasks and threads during runtime to achieve high performance [9].Over the years, there has been growing interest in OpenMP-based dynamic adaptation between energy and performance trade-offs of parallel applications [4]. Dynamic voltage/frequency scaling (DVFS) is a major runtime control knob to achieve such adaptation. The main principle of DVFS is to suitably lower the operating voltage/frequency to exponentially reduce the energy consumption at the cost of linear performance degradation [5]. Shirako et al.[10] proposed a DVFS-based energy minimization approach using a modified OpenMP compiler, named OSCAR. The compiler analyses the criticality of various parallel tasks and sections and identifies suitable DVFS for them. Cochran et al. [11] proposed another adaptive DVFS approach to control the peak power budget of an applicati...

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Power Measurement and Concurrency Throttling for Energy Reduction in OpenMP Programs

Cited by 46 publications

References 18 publications

Runtime Performance and Power Optimization of Parallel Disparity Estimation on Many-Core Platforms

Runtime Performance and Power Optimization of Parallel Disparity Estimation on Many-Core Platforms

Maximizing Throughput of Overprovisioned HPC Data Centers Under a Strict Power Budget

Adaptive Energy Minimization of OpenMP Parallel Applications on Many-Core Systems

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