Using multiple energy gears in MPI programs on a power-scalable cluster

Freeh, Vincent W.; Lowenthal, David K.

doi:10.1145/1065944.1065967

Cited by 140 publications

(106 citation statements)

References 74 publications

(56 reference statements)

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“…Prior work has applied DVFS to single cores [11,17,23], clusters of multi-chip multiprocessors [7,15,22,25], and shared-memory multi-cores [1,3,4,12]. These studies have the underlying assumption that each core has full, independent control over its voltage and frequency settings, whereas the design space for several cores sharing one DVFS domain has not been explored much.…”

Section: Introductionmentioning

confidence: 99%

Phase-Based Application-Driven Hierarchical Power Management on the Single-chip Cloud Computer

Ioannou

Kauschke

Gries

et al. 2011

2011 International Conference on Parallel Architectures and Compilation Techniques

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To improve energy efficiency processors allow for Dynamic Voltage and Frequency Scaling (DVFS), which enables changing their performance and power consumption on-thefly. Many-core architectures, such as the Single-chip Cloud Computer (SCC) experimental processor from Intel Labs, have DVFS infrastructures that scale by having many more independent voltage and frequency domains on-die than today's multi-cores. This paper proposes a novel, hierarchical, and transparent client-server power management scheme applicable to such architectures. The scheme tries to minimize energy consumption within a performance window taking into consideration not only the local information for cores within frequency domains but also information that spans multiple frequency and voltage domains.We implement our proposed hierarchical power control using a novel application-driven phase detection and prediction approach for Message Passing Interface (MPI) applications, a natural choice on the SCC with its fast on-chip network and its non-coherent memory hierarchy. This phase predictor operates as the front-end to the hierarchical DVFS controller, providing the necessary DVFS scheduling points.Experimental results with SCC hardware show that our approach provides significant improvement of the Energy Delay Product (EDP) of as much as 27.2%, and 11.4% on average, with an average increase in execution time of 7.7% over a baseline version without DVFS. These improvements come from both improved phase prediction accuracy and more effective DVFS control of the domains, compared to existing approaches.Keywords: phase detection, message passing interface, DVFS, resource management * Work mainly done while N. Ioannou was an intern at Intel Labs. M. Cintra is currently on sabbatical leave at Intel Labs. M. Kauschke is now affiliated with Intel Corp. and the ExaCluster Laboratory at FZ Jülich, Germany.

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Section: Introductionmentioning

confidence: 99%

Phase-Based Application-Driven Hierarchical Power Management on the Single-chip Cloud Computer

Ioannou

Kauschke

Gries

et al. 2011

2011 International Conference on Parallel Architectures and Compilation Techniques

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“…• Some work applies DVFS during the communication phases of high performance computing, for example MPI [17], [18].…”

Section: B Energy Reduction Via Dvfs Techniquementioning

confidence: 99%

Towards Energy Aware Scheduling for Precedence Constrained Parallel Tasks in a Cluster with DVFS

Wang

Laszewski

Dayal

et al. 2010

2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing

187

108

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Abstract-Reducing energy consumption for high end computing can bring various benefits such as, reduce operating costs, increase system reliability, and environment respect. This paper aims to develop scheduling heuristics and to present application experience for reducing power consumption of parallel tasks in a cluster with the Dynamic Voltage Frequency Scaling (DVFS) technique. In this paper, formal models are presented for precedence-constrained parallel tasks, DVFS enabled clusters, and energy consumption. This paper studies the slack time for non-critical jobs, extends their execution time and reduces the energy consumption without increasing the task's execution time as a whole. Additionally, Green Service Level Agreement is also considered in this paper. By increasing task execution time within an affordable limit, this paper develops scheduling heuristics to reduce energy consumption of a tasks execution and discusses the relationship between energy consumption and task execution time. Models and scheduling heuristics are examined with a simulation study. Test results justify the design and implementation of proposed energy aware scheduling heuristics in the paper.

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“…• Parallel applications are identified with different execution phase, in contrast to compiler or other system level software schedules proper processor voltage and frequency for different execution phases [19].…”

Section: A Power-aware Cluster Computingmentioning

confidence: 99%

Power-aware scheduling of virtual machines in DVFS-enabled clusters

Laszewski

Wang

Younge

et al. 2009

2009 IEEE International Conference on Cluster Computing and Workshops

226

136

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Abstract-With the advent of Cloud computing, large-scale virtualized compute and data centers are becoming common in the computing industry. These distributed systems leverage commodity server hardware in mass quantity, similar in theory to many of the fastest Supercomputers in existence today. However these systems can consume a cities worth of power just to run idle, and require equally massive cooling systems to keep the servers within normal operating temperatures. This produces CO2 emissions and significantly contributes to the growing environmental issue of Global Warming. Green computing, a new trend for high-end computing, attempts to alleviate this problem by delivering both high performance and reduced power consumption, effectively maximizing total system efficiency. This paper focuses on scheduling virtual machines in a compute cluster to reduce power consumption via the technique of Dynamic Voltage Frequency Scaling (DVFS). Specifically, we present the design and implementation of an efficient scheduling algorithm to allocate virtual machines in a DVFS-enabled cluster by dynamically scaling the supplied voltages. The algorithm is studied via simulation and implementation in a multi-core cluster. Test results and performance discussion justify the design and implementation of the scheduling algorithm.

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Using multiple energy gears in MPI programs on a power-scalable cluster

Cited by 140 publications

References 74 publications

Phase-Based Application-Driven Hierarchical Power Management on the Single-chip Cloud Computer

Phase-Based Application-Driven Hierarchical Power Management on the Single-chip Cloud Computer

Towards Energy Aware Scheduling for Precedence Constrained Parallel Tasks in a Cluster with DVFS

Power-aware scheduling of virtual machines in DVFS-enabled clusters

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