Enabling Performance Engineering in Hesse and Rhineland-Palatinate - A Cooperation of the Rhine-Main-Universities and the Technische Universität Kaiserslautern
在黑森州和莱茵兰-普法尔茨州实现性能工程——莱茵美因大学和凯泽斯劳滕技术大学的合作
基本信息
- 批准号:320898076
- 负责人:
- 金额:--
- 依托单位:
- 依托单位国家:德国
- 项目类别:Research Grants
- 财政年份:2016
- 资助国家:德国
- 起止时间:2015-12-31 至 2019-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
The objective of this proposal is to deepen the service scope and to mesh existing structures for basic HPC programming and tuning support at the Technische Universität Darmstadt, the Goethe-Universität Frankfurt, the Johannes-Gutenberg-Universität Mainz (the Rhein-Main Universities), and the Technische Universität Kaiserslautern. Among the four sites represented here, we have both a rich pool of challenging HPC applications emanating from first-class science, but also researchers well-established in critical performance-relevant research that we want to capitalize on.Thus, we want to provide more depth in HPC service in areas where scientific expertise at these universities coincides with critical user needs, pushing expertise on GPU performance engineering, model-based scalability analysis, and algorithmic stability and reproducibility into performance engineering practices. The issue of GPU and vectorization performance is crucial, as it is relevant for all HPC architectures. Performance modeling is critical, as without it, we cannot identify potential performance bottlenecks before resources are expended unnecessarily, an issue that is crucial for the scaling-up of applications, and, in particular, the efficient transition between tier-3, tier-2, and tier-1 computing centers. Lastly, the issues of algorithmic stability, performance, and reproducibility are becoming more and more important with respect to the plausibility and credibility of HPC simulations. So this choice of topics is by no means random but deliberate; the scientific expertise of the project team dovetails with real HPC user needs.At a governance level, we want to bundle the distributed expertise for HPC support and performance engineering that has been already established within the Hessian HPC-Competence Center and the Alliance for HPC in Rhineland-Palatinate in a new umbrella organization for the HPC support and performance engineering activities of all universities of Hesse and Rhineland-Palatinate. In particular, we will ensure dedicated on-site basic level support at all sites, and mesh the postdoctoral associates that provide the translational effort described above with already-existing HPC programming and tuning staff, thus creating a vibrant distributed group of scientific staff that has a shared fundamental knowledge of programming and tuning as well as the topical capabilities mentioned above. We will provide tutorials, coding workshops, and capability showcases to push our special capabilities. To show the value of this work, we will define success metrics, and document and evaluate the work performed.We believe that in this fashion, we create a service organization that offers organizational and operational synergies for HPC users in our states, and profits from the close spatial proximity of the sites involved. In addition, we will provide a model for meshing basic on-site HPC support in a distributed organization with focus support for topics of relevance.
该提案的目标是深化服务范围,并将达姆施塔特工业大学、法兰克福歌德大学、美因茨约翰-古腾堡大学(莱茵-美因大学)和凯撒大帝工业大学的基础HPC编程和调优支持现有结构相结合。在这四个站点中,我们既有来自一流科学的丰富的高性能计算应用,也有我们希望利用的在关键性能相关研究方面具有良好声誉的研究人员。因此,我们希望在这些大学的科学专业知识与关键用户需求相吻合的领域提供更深入的高性能计算服务,推动GPU性能工程方面的专业知识,基于模型的可扩展性分析,以及算法的稳定性和可重复性到性能工程实践。GPU和矢量化性能的问题至关重要,因为它与所有HPC架构都相关。性能建模至关重要,因为如果没有它,我们就无法在不必要地消耗资源之前识别潜在的性能瓶颈,这对于应用程序的扩展至关重要,特别是第3层,第2层和第1层计算中心之间的有效过渡。最后,算法的稳定性,性能和可重复性的问题变得越来越重要的HPC模拟的可扩展性和可信度。因此,这一主题的选择绝不是随意的,而是深思熟虑的;项目团队的科学专业知识与真实的HPC用户需求相吻合。在治理级别,我们希望将已经在黑森州HPC能力中心和莱茵兰HPC联盟内建立的HPC支持和性能工程的分布式专业知识捆绑在一起,普法尔茨在一个新的伞式组织的HPC支持和性能工程活动的所有大学的黑森和莱茵兰-腭骨的。特别是,我们将确保在所有站点提供专门的现场基本支持,并将提供上述翻译工作的博士后同事与现有的HPC编程和调优人员相结合,从而创建一个充满活力的分布式科学工作人员团队,该团队具有共享的编程和调优基础知识以及上述主题能力。我们将提供教程,编码研讨会和功能展示,以推动我们的特殊功能。为了展示这项工作的价值,我们将定义成功指标,并记录和评估所执行的工作。我们相信,通过这种方式,我们创建了一个服务组织,为我们州的HPC用户提供组织和运营协同效应,并从所涉及的站点的紧密空间接近中获利。此外,我们将提供一个模型,用于在分布式组织中将基本的现场HPC支持与相关主题的重点支持相结合。
项目成果
期刊论文数量(12)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
A usability case study of algorithmic differentiation tools on the ISSM ice sheet model
ISSM 冰盖模型算法微分工具的可用性案例研究
- DOI:10.1080/10556788.2017.1396602
- 发表时间:2018
- 期刊:
- 影响因子:2.2
- 作者:Alexander ;Bischof;Christian ;Sagebaum;Gauger;Nicolas R. ;Jurgelucks ;Benjamin ;Larour ;Perez ;Gilberto
- 通讯作者:Gilberto
Following the Blind Seer - Creating Better Performance Models Using Less Information
跟随盲目的先知 - 使用更少的信息创建更好的性能模型
- DOI:10.1007/978-3-319-64203-1_8
- 发表时间:2017
- 期刊:
- 影响因子:0
- 作者:Patrick Reisert;Alexandru Calotoiu;Sergei Shudler;Felix Wolf
- 通讯作者:Felix Wolf
Proceedings of the 22nd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, Austin, TX, USA, February 4-8, 2017
第 22 届 ACM SIGPLAN 并行编程原理与实践研讨会论文集,美国德克萨斯州奥斯汀,2017 年 2 月 4-8 日
- DOI:10.1145/3155284
- 发表时间:2017
- 期刊:
- 影响因子:0
- 作者:Shudler;Sergei;Calotoiu;Alexandru;Hoefler;Torsten
- 通讯作者:Torsten
Exploring the Performance Envelope of the LLL Algorithm
探索 LLL 算法的性能范围
- DOI:10.1109/cse.2018.00012
- 发表时间:2018
- 期刊:
- 影响因子:0
- 作者:Burger;Bischof;Calotoiu;Wunderer
- 通讯作者:Wunderer
Estimating the Impact of External Interference on Application Performance
估计外部干扰对应用程序性能的影响
- DOI:10.1007/978-3-319-96983-1_4
- 发表时间:2018
- 期刊:
- 影响因子:0
- 作者:Shah A;Müller M;Wolf F.
- 通讯作者:Wolf F.
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Professor Dr. Christian Bischof其他文献
Professor Dr. Christian Bischof的其他文献
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{{ truncateString('Professor Dr. Christian Bischof', 18)}}的其他基金
CATWALK - A Quick Development Path for Performance Models
CATWALK - 性能模型的快速开发路径
- 批准号:
230954187 - 财政年份:2012
- 资助金额:
-- - 项目类别:
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