Parallel streaming between heterogeneous HPC resources for real-time analysis

Journal Article · 01 February 2019 · Journal of Computational Science

DOI: https://doi.org/10.1016/j.jocs.2019.01.003 · OSTI ID:1530577

Marrinan, Thomas  ^[1]; Eisenhauer, Greg ^[2]; Wolf, Matthew ^[3]; Insley, Joseph A. ^[4]; Rizzi, Silvio  ^[5]; Papka, Michael E.  ^[5]

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1. Univ. of St. Thomas, St. Paul, MN (United States); Argonne National Lab. (ANL), Argonne, IL (United States)
2. Georgia Inst. of Technology, Atlanta, GA (United States)
3. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Georgia Inst. of Technology, Atlanta, GA (United States)
4. Argonne National Lab. (ANL), Argonne, IL (United States); Northern Illinois Univ., DeKalb, IL (United States)
5. Argonne National Lab. (ANL), Argonne, IL (United States)

Performing analysis or generating visualizations concurrently with high performance simulations can yield great benefits compared to post-processing data. Writing and reading large volumes of data can be reduced or eliminated, thereby producing an I/O cost savings. One such method for concurrent simulation and analysis is in transit - streaming data from the resource running the simulation to a separate resource running the analysis. In transit analysis can be beneficial since computational resources may not have certain resources needed for visualization and analysis (e.g. GPUs) and to reduce the impact of performing analysis tasks to the run time of the simulation. When sending and receiving data in transit, data redistribution mechanisms are needed in order to support heterogeneous data layouts that may be required by the simulation and analysis applications. The work described in this paper compares two mechanisms for on-the-fly data redistribution when streaming data in parallel between two distributed memory applications. Finally, our results show that it is often more advantageous to stream data in the same layout as the sender and redistribute data amongst processes on the receiving end than to stream data in the final layout needed by the receiver.

• Research Organization: Argonne National Laboratory (ANL), Argonne, IL (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
• Grant/Contract Number: AC02-06CH11357; AC05-00OR22725
• OSTI ID: 1530577
• Journal Information: Journal of Computational Science, Journal Name: Journal of Computational Science Vol. 31; ISSN 1877-7503
• Publisher: ElsevierCopyright Statement
• Country of Publication: United States
• Language: English