In-Situ Techniques on GPU-Accelerated Data-Intensive Applications
Ju Y, Li M, Perez A, Bellentani L, Jansson N, Markidis S, Schlatter P, Laure E (2023)
Publication Type: Conference contribution
Publication year: 2023
Publisher: Institute of Electrical and Electronics Engineers Inc.
Conference Proceedings Title: Proceedings 2023 IEEE 19th International Conference on e-Science, e-Science 2023
Event location: Limassol, CYP
ISBN: 9798350322231
DOI: 10.1109/e-Science58273.2023.10254865
Abstract
The computational power of High-Performance Computing (HPC) systems is constantly increasing, however, their input/output (IO) performance grows relatively slowly, and their storage capacity is also limited. This unbalance presents significant challenges for applications such as Molecular Dynamics (MD) and Computational Fluid Dynamics (CFD), which generate massive amounts of data for further visualization or analysis. At the same time, checkpointing is crucial for long runs on HPC clusters, due to limited walltimes and/or failures of system components, and typically requires the storage of large amount of data. Thus, restricted IO performance and storage capacity can lead to bottlenecks for the performance of full application workflows (as compared to computational kernels without IO). In-situ techniques, where data is further processed while still in memory rather to write it out over the I/O subsystem, can help to tackle these problems. In contrast to traditional post-processing methods, in-situ techniques can reduce or avoid the need to write or read data via the IO subsystem. They offer a promising approach for applications aiming to leverage the full power of large scale HPC systems. In-situ techniques can also be applied to hybrid computational nodes on HPC systems consisting of graphics processing units (GPUs) and central processing units (CPUs). On one node, the GPUs would have significant performance advantages over the CPUs. Therefore, current approaches for GPU-accelerated applications often focus on maximizing GPU usage, leaving CPUs underutilized. In-situ tasks using CPUs to perform data analysis or preprocess data concurrently to the running simulation, offer a possibility to improve this underutilization.
Authors with CRIS profile
Involved external institutions
Rechenzentrum Garching der Max-Planck-Gesellschaft (MPCDF) / Max Planck Computing & Data Facility
Germany (DE)
Technische Universität München (TUM)
Germany (DE)
Royal Institute of Technology / Kungliga Tekniska Högskolan (KTH)
Sweden (SE)
Cineca Consorzio Interuniversitario
Italy (IT)
Germany (DE)
Technische Universität München (TUM)
Germany (DE)
Royal Institute of Technology / Kungliga Tekniska Högskolan (KTH)
Sweden (SE)
Cineca Consorzio Interuniversitario
Italy (IT)
How to cite
APA:
Ju, Y., Li, M., Perez, A., Bellentani, L., Jansson, N., Markidis, S.,... Laure, E. (2023). In-Situ Techniques on GPU-Accelerated Data-Intensive Applications. In Proceedings 2023 IEEE 19th International Conference on e-Science, e-Science 2023. Limassol, CYP: Institute of Electrical and Electronics Engineers Inc..
MLA:
Ju, Yi, et al. "In-Situ Techniques on GPU-Accelerated Data-Intensive Applications." Proceedings of the 19th IEEE International Conference on e-Science, e-Science 2023, Limassol, CYP Institute of Electrical and Electronics Engineers Inc., 2023.
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