Visuals on the House: Optimizing HPC Workflows with No-Cost CPU Visualization

Victor A. Mateevitsi, Andres Sewell,Mathis Bode,Paul F. Fischer,Jens Henrik Göbbert,Joseph A. Insley, Ioannis Kavroulakis, Damaskinos Konioris,Yu-Hsiang Lan,Misun Min,Dimitrios Papageorgiou,Michael E. Papka,Steve Petruzza,Silvio Rizzi,Ananias Tomboulides

IEEE Symposium on Large Data Analysis and Visualization（2024）

Argonne National Laboratory | Utah State University | University of Illinois Urbana-Champaign | Forschungszentrum Jülich | Aristotle University of Thessaloniki

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Abstract

The rise of heterogeneous resources in modern High Performance Computing (HPC) systems has propelled the scientific community beyond the exascale threshold. To maximize simulation performance on HPCs, applications increasingly rely on device resources, such as GPUs, leading to under-utilization of host resources, partic-ularly CPUs. In situ analysis and visualization techniques minimize data movement by operating on data in-memory, but this still in-volves blocking operations that incur a small penalty on simulation performance. We explore a novel instrumentation approach where GPU-based time step data is copied from device memory to host memory, enabling CPUs to concurrently perform visualization and analysis tasks. This strategy allows simulations to continue uninter-rupted by an in situ library's analysis and visualization processes.

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Key words

High Performance Computing (HPC),In Situ Visualization,Computational Fluid Dynamics (CFD),GPU Computing,Parallel Processing

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
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【要点】：本文提出了一种无需成本的新型异构计算方法，通过将GPU时间步数据复制到主机内存，使得CPU能在不中断模拟的情况下，并发执行可视化与分析任务，从而优化高性能计算(HPC)工作流。

【方法】：研究采用了一种创新的仪器化方法，在GPU执行计算任务的同时，将计算结果的时间步数据复制到主机内存，使得CPU能够并行地进行可视化与数据分析。

【实验】：文中未具体提及实验细节和数据集名称，但根据描述，实验验证了该策略下模拟可以不受在位库的分析和可视化过程影响，继续正常运行，并可能提高了CPU资源的利用率。