Browsing by Author "Larsen, Matthew"

Now showing 1 - 3 of 3
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    Dynamic I/O Budget Reallocation For In Situ Wavelet Compression
    (The Eurographics Association, 2019) Marsaglia, Nicole J.; Li, Shaomeng; Belcher, Kristi; Larsen, Matthew; Childs, Hank; Childs, Hank and Frey, Steffen
    In situ wavelet compression is a potential solution for enabling post hoc visualization on supercomputers with slow I/O systems. While this in situ compression is typically accomplished by allocating an equal storage budget to each parallel process, we propose an adaptive approach. With our approach, we introduce an assessment step prior to compression, where each process characterizes the variation in its portion of the data, and then dynamically adapts storage budgets to the processes with the most variation. We conducted experiments comparing our adaptive approach with the traditional, non-adaptive approach, on two different simulation codes with concurrencies of 512 cores and mesh resolutions of one billion cells. Our findings show that our adaptive approach yields three orders of magnitude of improvement for one simulation and is not harmful for the other.
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    PGV 2021: Frontmatter
    (The Eurographics Association, 2021) Larsen, Matthew; Sadlo, Filip; Larsen, Matthew and Sadlo, Filip
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    Scalable In Situ Computation of Lagrangian Representations via Local Flow Maps
    (The Eurographics Association, 2021) Sane, Sudhanshu; Yenpure, Abhishek; Bujack, Roxana; Larsen, Matthew; Moreland, Kenneth; Garth, Christoph; Johnson, Chris R.; Childs, Hank; Larsen, Matthew and Sadlo, Filip
    In situ computation of Lagrangian flow maps to enable post hoc time-varying vector field analysis has recently become an active area of research. However, the current literature is largely limited to theoretical settings and lacks a solution to address scalability of the technique in distributed memory. To improve scalability, we propose and evaluate the benefits and limitations of a simple, yet novel, performance optimization. Our proposed optimization is a communication-free model resulting in local Lagrangian flow maps, requiring no message passing or synchronization between processes, intrinsically improving scalability, and thereby reducing overall execution time and alleviating the encumbrance placed on simulation codes from communication overheads. To evaluate our approach, we computed Lagrangian flow maps for four time-varying simulation vector fields and investigated how execution time and reconstruction accuracy are impacted by the number of GPUs per compute node, the total number of compute nodes, particles per rank, and storage intervals. Our study consisted of experiments computing Lagrangian flow maps with up to 67M particle trajectories over 500 cycles and used as many as 2048 GPUs across 512 compute nodes. In all, our study contributes an evaluation of a communication-free model as well as a scalability study of computing distributed Lagrangian flow maps at scale using in situ infrastructure on a modern supercomputer.