15088

Large-Scale Compute-Intensive Analysis via a Combined In-Situ and Co-Scheduling Workflow Approach

Christopher Sewell, Katrin Heitmann, Hal Finkel, George Zagaris, Suzanne T. Parete-Koon, Patricia K. Fasel, Adrian Pope, Nicholas Frontiere, Li-ta Lo, Bronson Messer, Salman Habib, James Ahrens
CCS-7, Los Alamos National Lab, Los Alamos, NM 87545
International Conference for High Performance Computing, Networking, Storage and Analysis (SC ’15), 2015

@inproceedings{Sewell:2015b,

   author={Sewell, Christopher and Heitmann, Katrin and Finkel, Hal and Zagaris, George and Parete-Koon, Suzanne and Fasel, Patricia and Pope, Adrian and Frontiere, Nicholas and Lo, Li-Ta and Messer, Bronson and Habib, Salman and Ahrens, James},

   title={Large-Scale Compute-Intensive Analysis via a Combined In-situ and Co-scheduling Workflow Approach},

   booktitle={Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis},

   series={SC ’15},

   year={2015},

   location={Austin, Texas},

   numpages={11},

   publisher={IEEE Press},

   address={Piscataway, NJ, USA}

}

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Large-scale simulations can produce hundreds of terabytes to petabytes of data, complicating and limiting the efficiency of work-flows. Traditionally, outputs are stored on the file system and analyzed in post-processing. With the rapidly increasing size and complexity of simulations, this approach faces an uncertain future. Trending techniques consist of performing the analysis in-situ, utilizing the same resources as the simulation, and/or off-loading subsets of the data to a compute-intensive analysis system. We introduce an analysis framework developed for HACC, a cosmological N-body code, that uses both in-situ and co-scheduling approaches for handling petabyte-scale outputs. We compare different analysis set-ups ranging from purely off-line, to purely in-situ to insitu/co-scheduling. The analysis routines are implemented using the PISTON/VTK-m framework, allowing a single implementation of an algorithm that simultaneously targets a variety of GPU, multicore, and many-core architectures.
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