Hybrid MPI/OpenMP power-aware computing

Abstract-To reduce the energy consumption and build a sustainable computer infrastructure now becomes a major goal of the high performance community. A number of research projects have been carried out in the field of energy-aware high performance computing. This paper is devoted to categorize energy-aware computing methods for the high-end computing infrastructures, such as servers, clusters, data centers, and Grids/Clouds. Based on a taxonomy of methods and system scales, this paper reviews the current status of energy-aware HPC research and summarizes open questions and research directions of software architecture for future energy-aware HPC studies.

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“…[28] proposes poweraware Hybrid MPI/OpenMP programming pattern based on the interaction between communication and computation without using DVFS.…”

Section: B Programming Language and Runtime Supportmentioning

confidence: 99%

Energy-Aware High Performance Computing: A Taxonomy Study

Cai

Wang

Khan

et al. 2011

2011 IEEE 17th International Conference on Parallel and Distributed Systems

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“…Li et al in [15] models hybrid MPI/OpenMP from a performance and energy perspective examining both dynamic concurrency throttling (DCT) and DVS. In [16] Li takes a modeling approach to investigate task aggregation to reduce energy consumption by reducing the number of nodes.…”

Section: Related Workmentioning

confidence: 99%

“…Their work predicts the performance energy tradeoff up to 1024 cores (128 nodes). In both [15] and [16] the System G supercomputer at Virginia Tech is used. System G consists of 324 nodes.…”

Section: Related Workmentioning

confidence: 99%

Measuring and tuning energy efficiency on large scale high performance computing platforms.

Laros¹

2011

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Recognition of the importance of power in the field of High Performance Computing, whether it be as an obstacle, expense or design consideration, has never been greater and more pervasive. While research has been conducted on many related aspects, there is a stark absence of work focused on large scale High Performance Computing. Part of the reason is the lack of measurement capability currently available on small or large platforms. Typically, research is conducted using coarse methods of measurement such as inserting a power meter between the power source and the platform, or fine grained measurements using custom instrumented boards (with obvious limitations in scale). To collect the measurements necessary to analyze real scientific computing applications at large scale, an in-situ measurement capability must exist on a large scale capability class platform.In response to this challenge, we exploit the unique power measurement capabilities of the Cray XT architecture to gain an understanding of power use and the effects of tuning. We apply these capabilities at the operating system level by deterministically halting cores when idle. At the application level, we gain an understanding of the power requirements of a range of important DOE/NNSA production scientific computing applications running at large scale (thousands of nodes), while simultaneously collecting current and voltage measurements on the hosting nodes. We examine the effects of both CPU and network bandwidth tuning and demonstrate energy savings opportunities of up to 39% with little or no impact on run-time performance. Capturing scale effects in our 3 experimental results was key. Our results provide strong evidence that next generation large-scale platforms should not only approach CPU frequency scaling differently, but could also benefit from the capability to tune other platform components, such as the network, to achieve energy efficient performance. 5Acknowledgments I would like to acknowledge the following people who have contributed in various ways, all of which significant, to the successful completion of this work: Dr.

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“…The second thrust, which invariably depends on the first, is to attempt to minimize the amount of energy required to solve various scientific problems. This includes the use of Dynamic Voltage Frequency Scaling (DVFS) technique to exploit processor underutilization due to memory stalls [12,19] or MPI inter-task load imbalances in large scale applications [12], improvements in the process and design of hardware, or software-based techniques that change some feature of application behavior in order to lower some energy-related metric [20].…”

Section: Introductionmentioning

confidence: 99%

Auto-tuning for Energy Usage in Scientific Applications

Tiwari

Laurenzano

Carrington

et al. 2012

Euro-Par 2011: Parallel Processing Workshops

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Abstract. The power wall has become a dominant impeding factor in the realm of exascale system design. It is therefore important to understand how to most effectively create application software in order to minimize its power usage while maintaining satisfactory levels of performance. In this work, we use existing software and hardware facilities in order to tune applications to minimize for several combinations of power and performance. The tuning is done with respect to software level performance-related tunables (cache tiling factors and loop unrolling factors) as well as for processor clock frequency. These tunable parameters are explored via an offline search in order to find the parameter combinations that are optimal with respect to performance (or delay, D), energy (E), energy×delay (E × D) and energy×delay×delay (E × D 2 ). These searches are employed on a parallel application that solves Poisson's equation using stencil computations. Stencil (nearestneighbor) computations are very common operations in today's scientific applications. We show that the parameter configuration that minimizes energy consumption can save, on average, 5.4% energy with a performance loss of 4% when compared to the configuration that minimizes runtime. Furthermore, with the work presented in this paper, we provide evidence for the existence of opportunities to auto-tune for energy in parallel applications.

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Hybrid MPI/OpenMP power-aware computing

Cited by 112 publications

References 20 publications

Energy-Aware High Performance Computing: A Taxonomy Study

Energy-Aware High Performance Computing: A Taxonomy Study

Measuring and tuning energy efficiency on large scale high performance computing platforms.

Auto-tuning for Energy Usage in Scientific Applications

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