Enabling Performance Engineering in Hesse and Rhineland-Palatinate - A Cooperation of the Rhine-Main-Universities and the Technische Universität Kaiserslautern

这项建议的目的是深化服务范围，并使达姆施塔特理工学院、法兰克福歌德大学、美因茨理工学院(莱茵-美因茨大学)和凯泽斯劳滕理工学院为基本的HPC规划和调整提供支持的现有结构相互配合。在这里展示的四个站点中，我们既有来自一流科学的富有挑战性的HPC应用程序池，也有在关键性能相关研究中站稳脚跟的研究人员，我们希望利用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HPC服务，将GPU性能工程、基于模型的可扩展性分析以及算法稳定性和重现性方面的专业知识推向性能工程实践。GPU和矢量化性能的问题至关重要，因为它与所有HPC架构相关。性能建模至关重要，因为如果没有性能建模，我们就无法在不必要的资源消耗之前识别潜在的性能瓶颈，而资源消耗对于应用程序的向上扩展至关重要，尤其是在第3层、第2层和第1层计算中心之间的高效过渡。最后，关于高性能计算模拟的真实性和可信性，算法的稳定性、性能和重复性的问题变得越来越重要。因此，这个主题的选择绝不是随机的，而是经过深思熟虑的；项目团队的科学专业知识与真实的高性能计算机用户需求相吻合。在治理层面，我们希望将已经在黑森州高性能计算机能力中心和莱茵兰-帕拉蒂纳的高性能计算机联盟内建立的高性能计算机支持和性能工程分布的专业知识捆绑在一个新的伞形组织中，用于黑森州和莱茵兰-巴拉特纳州所有大学的高性能计算机支持和性能工程活动。特别是，我们将确保在所有地点提供专门的现场基层支持，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，他们拥有编程和调优的基本知识以及上述专题能力。我们将提供教程、编码研讨会和功能展示案例来推介我们的特殊功能。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，为我们所在州的HPC用户提供组织和运营协同效应，并从涉及的地点的近距离空间上获利。此外，我们将提供一个模型，将分布式组织中的基本现场HPC支持与相关主题的重点支持结合起来。

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.