Energy Efficiency in HPC Systems

Rodero, Iván; Parashar, Manish

doi:10.1002/9781118342015.ch3

Cited by 6 publications

(4 citation statements)

References 56 publications

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“…There exists a large body of literature on power and energy modeling at different levels, ranging from the microprocessor to entire systems. Our models are built using a traditional coarse-grained system level energy/power formulation [10,24,31,39,64,65,68]. These models are simple, fast, have low overhead, and are accurate enough to characterize energy at the system level.…”

Section: Related Workmentioning

confidence: 99%

Exploring power behaviors and trade-offs of in-situ data analytics

Gamell

Rodero

Parashar

et al. 2013

Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis

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As scientific applications target exascale, challenges related to data and energy are becoming dominating concerns. For example, coupled simulation workflows are increasingly adopting in-situ data processing and analysis techniques to address costs and overheads due to data movement and I/O. However it is also critical to understand these overheads and associated trade-offs from an energy perspective. The goal of this paper is exploring data-related energy/performance trade-offs for end-to-end simulation workflows running at scale on current high-end computing systems. Specifically, this paper presents: (1) an analysis of the data-related behaviors of a combustion simulation workflow with an insitu data analytics pipeline, running on the Titan system at ORNL; (2) a power model based on system power and data exchange patterns, which is empirically validated; and (3) the use of the model to characterize the energy behavior of the workflow and to explore energy/performance tradeoffs on current as well as emerging systems.

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Section: Related Workmentioning

confidence: 99%

Exploring power behaviors and trade-offs of in-situ data analytics

Gamell

Rodero

Parashar

et al. 2013

Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis

Self Cite

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“…In addition, the cost of building a power provisioning facility ranges at $10-22 per deployed IT watt [114] and every 10 • C temperature-increase results in a doubling of the system failure rate, thereby reducing the reliability of HPC systems [115]. Designing accurate models for energy efficiency can help better predict the power and energy requirements of an application and aid developers optimize the parameters of their codes for better energy efficiency on HPC systems.…”

Section: Energy Model For Tiled Nested-loop Codesmentioning

confidence: 99%

Analytical Cost Metrics : Days of Future Past

Prajapati

Rajopadhye

Djidjev

2018

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ANALYTICAL COST METRICS : DAYS OF FUTURE PASTFuture exascale high-performance computing (HPC) systems are expected to be increasingly heterogeneous, consisting of several multi-core CPUs and a large number of accelerators, specialpurpose hardware that will increase the computing power of the system in a very energy-efficient way. Specialized, energy-efficient accelerators are also an important component in many diverse systems beyond HPC: gaming machines, general purpose workstations, tablets, phones and other media devices. With Moore's law driving the evolution of hardware platforms towards exascale, the dominant performance metric (time efficiency) has now expanded to also incorporate power/energy efficiency.This work builds analytical cost models for cost metrics such as time, energy, memory access, and silicon area. These models are used to predict the performance of applications, for performance tuning, and chip design. The idea is to work with domain specific accelerators where analytical cost models can be accurately used for performance optimization. The performance optimization problems are formulated as mathematical optimization problems. This work explores the analytical cost modeling and mathematical optimization approach in a few ways. For stencil applications and GPU architectures, the analytical cost models are developed for execution time as well as energy.The models are used for performance tuning over existing architectures, and are coupled with silicon area models of GPU architectures to generate highly efficient architecture configurations.For matrix chain products, analytical closed form solutions for off-chip data movement are built and used to minimize the total data movement cost of a minimum op count tree.ii ACKNOWLEDGEMENTS I would like to thank my advisor, Sanjay Rajopadhye, for his guidance and support. He has been a continuous source of motivation. His brilliant ideas and farsightedness have awed me throughout my Ph.D. I learnt a lot from him and enjoyed being his student. I not only gained technical knowledge in the field but also learnt values of a good human being like patience, kindness, selflessness, compassion, and dedication. I feel honored to have the opportunity to work with him.

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“…In addition, the cost of building a power provisioning facility ranges at $10-22 per deployed IT watt [61] and every 10 • C temperature-increase results in a doubling of the system failure rate, thereby reducing the reliability of HPC systems [62]. Designing accurate models for energy efficiency can help better predict the power and energy requirements of an application and aid developers optimize the parameters of their codes for better energy efficiency on HPC systems.…”

Section: Energy Model For Tiled Nested-loop Codesmentioning

confidence: 99%