Vpm tokens: virtual machine-aware power budgeting in datacenters
HPDC '08 Proceedings of the 17th international symposium on High performance distributed computing
Accomodating Diversity in CMPs with Heterogeneous Frequencies
HiPEAC '09 Proceedings of the 4th International Conference on High Performance Embedded Architectures and Compilers
Green Computing: Energy Consumption Optimized Service Hosting
SOFSEM '09 Proceedings of the 35th Conference on Current Trends in Theory and Practice of Computer Science
Adagio: making DVS practical for complex HPC applications
Proceedings of the 23rd international conference on Supercomputing
Energy-Efficient Cluster Computing via Accurate Workload Characterization
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
VPM tokens: virtual machine-aware power budgeting in datacenters
Cluster Computing
A power aware study for VTDIRECT95 using DVFS
SpringSim '09 Proceedings of the 2009 Spring Simulation Multiconference
Towards Energy Aware Scheduling for Precedence Constrained Parallel Tasks in a Cluster with DVFS
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
vGreen: A System for Energy-Efficient Management of Virtual Machines
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Power Control by Distribution Tree with Classified Power Capping in Cloud Computing
GREENCOM-CPSCOM '10 Proceedings of the 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing
Designing Energy Efficient Communication Runtime Systems for Data Centric Programming Models
GREENCOM-CPSCOM '10 Proceedings of the 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing
Energy efficient scheduling for multithreaded programs on general-purpose processors
Proceedings of the 17th IEEE/ACM international symposium on Low-power electronics and design
SERA-IO: Integrating Energy Consciousness into Parallel I/O Middleware
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
Energy-efficient deadline scheduling for heterogeneous systems
Journal of Parallel and Distributed Computing
Designing energy efficient communication runtime systems: a view from PGAS models
The Journal of Supercomputing
An overview of energy efficiency techniques in cluster computing systems
Cluster Computing
Exploring hardware overprovisioning in power-constrained, high performance computing
Proceedings of the 27th international ACM conference on International conference on supercomputing
Energy-aware parallel task scheduling in a cluster
Future Generation Computer Systems
Energy saving strategies for parallel applications with point-to-point communication phases
Journal of Parallel and Distributed Computing
Evaluating energy savings for checkpoint/restart
E2SC '13 Proceedings of the 1st International Workshop on Energy Efficient Supercomputing
E2SC '13 Proceedings of the 1st International Workshop on Energy Efficient Supercomputing
Energy-efficient work-stealing language runtimes
Proceedings of the 19th international conference on Architectural support for programming languages and operating systems
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Performance and power are critical design constraints in today's high-end computing systems. Reducing power consumption without impacting system performance is a challenge for the HPC community. We present a runtime system (CPU MISER) and an integrated performance model for performance-directed, power-aware cluster computing. CPU MISER supports system-wide, application-independent, fine-grain, dynamic voltage and frequency scaling (DVFS) based power management for a generic power-aware cluster. Experimental results show that CPU MISER can achieve as much as 20% energy savings for the NAS parallel benchmarks. In addition to energy savings, CPU MISER is able to constrain performance loss for most applications within user-specified limits. These constraints are achieved through accurate performance modeling and prediction, coupled with advanced control techniques.