Collaborative Research: EAGER: Real-time Strategies and Synchronized Time Distribution Mechanisms for Enhanced Exascale Performance-Portability and Predictability
Collaborative Research: EAGER: Real-time Strategies and Synchronized Time Distribution Mechanisms for Enhanced Exascale Performance-Portability and Predictability
批准号:
2151020
负责人:
Anthony Skjellum
金额:
$7.45万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2022
资助国家:
美国
项目状态:
已结题
起止时间:
2022-06-01 至 2023-12-31
中文摘要
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英文摘要
Advances throughout science and engineering have for several decades been driven by High Performance Computing (HPC), with the pace of discovery accelerating in concert with continued innovation in computing capabilities. But as semiconductor technology now faces fundamental physical limits, even while large-scale systems are reaching warehouse scales, new approaches are becoming essential to achieving efficient use of computing resources. In particular, given this divergence of scales, HPC systems have necessarily become more distributed and asynchronous (in the sense that system clocks are asynchronous), resulting in increasingly variable and unpredictable execution. While these effects are recognized as critical hindrances to HPC performance, the mechanisms are not yet fully understood. What is known, however, is that much HPC infrastructure is tasked with dealing with inefficiency derived from asynchrony, variability, and unpredictability, leading to a deep and complex hardware/software support stack. The project team's hypothesis is that while each stack element provides a local solution, it may also exacerbate the global problem: that complexity has resulted in more variability, not less, and made determining its causes more difficult. This project explores the possibility of reversing the trend of ever-increasing complexity by removing and simplifying support layers. This strategy’s achievable gains remain limited, however, while the underlying cause, execution asynchrony, remains unaddressed. The approach begins by leveraging recently developed technology that enables clocks to remain extremely accurate even when distributed on a planetary scale. Such accurate, distributed clocks serve to underpin a virtuous cycle where synchrony establishes baseline predictability, which, in turn, reduces variability, and at each stage of the cycle enables reduction in the complexity of the support stack. A benefit of this approach is that the individual steps are largely simple and can be applied directly to existing software systems. This one-year project aims to obtain early findings and practical demonstrations for the importance of synchrony and predictability to increase HPC compute efficiency and thereby improve large-scale program execution. Five tasks are conducted. The first is to demonstrate the feasibility of accurate clock distribution by augmenting existing HPC network infrastructure. The second is to demonstrate the application of synchrony in the establishing a virtuous cycle enabling simplifications to the software/system support stack. The third is to devise mechanisms to model, measure, and validate systems using the proposed methods. The fourth is to investigate the relative benefits of applying the synchrony-based virtuous cycle with respect to various application classes. The fifth is to demonstrate the overall efficacy of the proposed approach through a case study involving a production application. Overall, the project works to determine whether added synchronization through accurate clocks enables significant improvements to HPC computations in terms of how efficiently they use computational resources.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
期刊论文(1)
专著(0)
科研奖励(0)
会议论文
The Viability of Using Online Prediction to Perform Extra Work while Executing BSP Applications
在执行 BSP 应用程序时使用在线预测执行额外工作的可行性
DOI:
--
发表时间:
2022
期刊:
IEEE High Performance Extreme Computing
影响因子:
--
作者:
[P.H. Chen, P. Haghi]
通讯作者:
P.H. Chen, P. Haghi
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资助金额:$45.01万
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财政年份:2023
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负责人:Anthony Skjellum
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依托单位:
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批准号:2405142
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项目类别:Standard Grant
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项目类别:Continuing Grant
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负责人:Anthony Skjellum
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依托单位:
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项目类别:Standard Grant
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资助金额:$8.1万
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财政年份:2016
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负责人:Anthony Skjellum
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依托单位:
CICI: Data Provenance: Collaborative Research: Provenance Assurance Using Currency Primitives
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批准号:1547245
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项目类别:Standard Grant
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财政年份:2016
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负责人:Anthony Skjellum
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依托单位:
SHF: Small: Collaborative Research: Coupling Computation and Communication in FPGA-Enhanced Clouds and Clusters
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项目类别:Standard Grant
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EAGER: A Transactional, Fault-Aware MPI Prototype to Enable MPI-3 Standardization
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负责人:Anthony Skjellum
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依托单位:
EAGER: Collaborative Research: A Peer-to-Peer based Storage System for High-End Computing
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批准号:1064247
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项目类别:Standard Grant
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MRI: Development of a GPU-Enabled Integrated Storage Computation Architecture and System
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MRI: and Information Sciences Grid Node Research Facility
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依托单位:
Collaborative Research: A Systematic Approach to the Derivation, Representation, Analysis, and Correctness of Dense and Banded Linear Algebra Algorithms for HPC Architectures
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国内基金
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