CSR: Medium: Collaborative Research: GridPac: A Resource Management System for Energy and Performance Optimization on Computational Grids
CSR:媒介:协作研究:GridPac:计算网格能源和性能优化的资源管理系统
基本信息
- 批准号:0905188
- 负责人:
- 金额:$ 34万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2009
- 资助国家:美国
- 起止时间:2009-09-01 至 2013-08-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Massive energy consumption is an escalating threat to the environment. Large-scale computational grids consume a substantial amount of energy, and their energy requirements for powering and cooling are becoming comparable to the costs of acquisition. There is a lack of generally applicable methods for reducing energy consumption while ensuring good quality of service. This project will develop GridPac (Grid with Power-Aware Computing), a middleware environment that will allow grid managers and service providers to schedule multiple workflows across a distributed grid for system-wide optimization. GridPac will be based on a novel framework to support a variety of task-level workflows. The main features of this work are to develop:(a) Novel static and dynamic algorithms for scheduling single and multiple workflows, which can be flexibly utilized by service providers in scalable grid environments.(b) Control algorithms to account for dynamic adjustment of schedules using energy monitoring of the grid resources.(c) Extensive benchmarking using a suite of commonly used grid workflows.(d) A prototype middleware to assist IT organizations to better support their users while reducing energy costs.The proposed work will lead to original scholarly contributions while harnessing the usage of computational grids. The project carries tremendous potential for economic, environmental, and societal impact. We will initiate new graduate and undergraduate level courses on related topics, and develop relevant tutorials, which will help to create awareness and educate a large audience on a critically important research topic.
大量的能源消耗是对环境的不断升级的威胁。大规模计算网格消耗大量能源,其供电和冷却的能源需求正变得与采购成本相当。缺乏在确保良好服务质量的同时降低能源消耗的普遍适用的方法。该项目将开发GridPac(支持节能计算的网格),这是一个中间件环境,允许网格管理人员和服务提供商跨分布式网格调度多个工作流,以实现系统范围的优化。GridPac将基于一个新的框架来支持各种任务级工作流。这项工作的主要特点是:(A)新的静态和动态调度单个和多个工作流的算法,可供服务提供商在可扩展的网格环境中灵活使用。(B)控制算法,通过对网格资源的能量监控来考虑调度的动态调整。(C)使用一套常用的网格工作流进行广泛的基准测试。(D)一个原型中间件,以帮助IT组织在降低能源成本的同时更好地支持他们的用户。拟议的工作将在利用计算网格的使用的同时产生原创性的学术贡献。该项目具有巨大的经济、环境和社会影响潜力。我们将开设关于相关主题的新的研究生和本科生课程,并开发相关教程,这将有助于在一个至关重要的研究主题上提高认识和教育广大受众。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Jack Dongarra其他文献
The co-evolution of computational physics and high-performance computing
计算物理与高性能计算的协同演化
- DOI:
10.1038/s42254-024-00750-z - 发表时间:
2024-08-23 - 期刊:
- 影响因子:39.500
- 作者:
Jack Dongarra;David Keyes - 通讯作者:
David Keyes
hipMAGMA v1.0
hipMAGMA v1.0
- DOI:
- 发表时间:
2020 - 期刊:
- 影响因子:0
- 作者:
Cade Brown;Ahmad Abdelfattah;Stanimire Tomov;Jack Dongarra - 通讯作者:
Jack Dongarra
The eigenvalue problem for Hermitian matrices with time reversal symmetry
具有时间反演对称性的 Hermitian 矩阵的特征值问题
- DOI:
10.1016/0024-3795(84)90068-5 - 发表时间:
1984 - 期刊:
- 影响因子:1.1
- 作者:
Jack Dongarra;J. R. Gabriel;D. D. Koelling;James Hardy Wilkinson - 通讯作者:
James Hardy Wilkinson
Analyzing Performance of BiCGStab with Hierarchical Matrix on GPU clusters
使用分层矩阵分析 BiCGStab 在 GPU 集群上的性能
- DOI:
- 发表时间:
2018 - 期刊:
- 影响因子:0
- 作者:
Ichitaro Yamazaki;Ahmad Abdelfattah;Akihiro Ida;Satoshi Ohshima;Stanimire Tomov;Rio Yokota;Jack Dongarra - 通讯作者:
Jack Dongarra
Self-healing network for scalable fault-tolerant runtime environments
- DOI:
10.1016/j.future.2009.04.001 - 发表时间:
2010-03-01 - 期刊:
- 影响因子:
- 作者:
Thara Angskun;Graham Fagg;George Bosilca;Jelena Pješivac-Grbović;Jack Dongarra - 通讯作者:
Jack Dongarra
Jack Dongarra的其他文献
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{{ truncateString('Jack Dongarra', 18)}}的其他基金
Travel: Workshop on Clusters, Clouds, and Data Analytics for Scientific Computing 2024
旅行:2024 年科学计算集群、云和数据分析研讨会
- 批准号:
2336813 - 财政年份:2023
- 资助金额:
$ 34万 - 项目类别:
Standard Grant
Workshop on Clusters, Clouds, and Data Analytics for Scientific Computing
科学计算集群、云和数据分析研讨会
- 批准号:
2001329 - 财政年份:2020
- 资助金额:
$ 34万 - 项目类别:
Standard Grant
Workshop on Clusters, Clouds, and Data Analytics in Scientific Computing
科学计算中的集群、云和数据分析研讨会
- 批准号:
1800946 - 财政年份:2018
- 资助金额:
$ 34万 - 项目类别:
Standard Grant
Toward a common digital continuum platform for big data and extreme-scale computing (BDEC2)
迈向大数据和超大规模计算的通用数字连续平台 (BDEC2)
- 批准号:
1849625 - 财政年份:2018
- 资助金额:
$ 34万 - 项目类别:
Standard Grant
Collaborative Research: ACI-CDS&E: Highly Parallel Algorithms and Architectures for Convex Optimization for Realtime Embedded Systems (CORES)
合作研究:ACI-CDS
- 批准号:
1709069 - 财政年份:2017
- 资助金额:
$ 34万 - 项目类别:
Standard Grant
Workshop on Clusters, Clouds and Data Analytics in Scientific Computing
科学计算中的集群、云和数据分析研讨会
- 批准号:
1606551 - 财政年份:2016
- 资助金额:
$ 34万 - 项目类别:
Standard Grant
Collaborative Research: EMBRACE: Evolvable Methods for Benchmarking Realism through Application and Community Engagement
合作研究:拥抱:通过应用和社区参与对现实主义进行基准测试的演化方法
- 批准号:
1535025 - 财政年份:2015
- 资助金额:
$ 34万 - 项目类别:
Standard Grant
SHF: Small: Empirical Autotuning of Parallel Computation for Scalable Hybrid Systems
SHF:小型:可扩展混合系统并行计算的经验自动调整
- 批准号:
1527706 - 财政年份:2015
- 资助金额:
$ 34万 - 项目类别:
Standard Grant
SI2-SSI: Collaborative Proposal: Performance Application Programming Interface for Extreme-Scale Environments (PAPI-EX)
SI2-SSI:协作提案:极端规模环境的性能应用程序编程接口 (PAPI-EX)
- 批准号:
1450429 - 财政年份:2015
- 资助金额:
$ 34万 - 项目类别:
Standard Grant
CSR:Medium:Collaborative Research: SparseKaffe: high-performance, auto-tuned, energy-aware algorithms for sparse direct methods on modern heterogeneous architectures
CSR:Medium:协作研究:SparseKaffe:现代异构架构上稀疏直接方法的高性能、自动调整、能量感知算法
- 批准号:
1514286 - 财政年份:2015
- 资助金额:
$ 34万 - 项目类别:
Continuing Grant
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