Accelerating in-transit co-processing for scientific simulations using region-based data-driven analysis

Walldén, Marcus and Okita, Masao and Ino, Fumihiko and Drikakis, Dimitris and Kokkinakis, Ioannis (2021) Accelerating in-transit co-processing for scientific simulations using region-based data-driven analysis. Algorithms, 14 (5). 154. ISSN 1999-4893 (https://doi.org/10.3390/a14050154)

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Abstract

Increasing processing capabilities and input/output constraints of supercomputers have increased the use of co-processing approaches, i.e., visualizing and analyzing data sets of simulations on the fly. We present a method that evaluates the importance of different regions of simulation data and a data-driven approach that uses the proposed method to accelerate in-transit co-processing of large-scale simulations. We use the importance metrics to simultaneously employ multiple compression methods on different data regions to accelerate the in-transit co-processing. Our approach strives to adaptively compress data on the fly and uses load balancing to counteract memory imbalances. We demonstrate the method’s efficiency through a fluid mechanics application, a Richtmyer–Meshkov instability simulation, showing how to accelerate the in-transit co-processing of simulations. The results show that the proposed method expeditiously can identify regions of interest, even when using multiple metrics. Our approach achieved a speedup of 1.29× in a lossless scenario. The data decompression time was sped up by 2× compared to using a single compression method uniformly.

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