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A Novel Shard-Based Approach for Asynchronous Many-Task Models for In Situ Analysis.

, , , , and . ISAV@SC, page 27-31. ACM, (2017)

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Enabling Adaptive Scientific Workflows Via Trigger Detection., , , , and . ISAV@SC, page 41-45. ACM, (2015)Evolving the Message Passing Programming Model via a Fault-Tolerant, Object-oriented Transport Layer., , , , , and . FTXS@HPDC, page 41-46. ACM, (2015)Trigger detection for adaptive scientific workflows using percentile sampling., , , , , and . CoRR, (2015)Analysis of Uncertain Scalar Data with Hixels., , , , , , and . Scientific Visualization, Springer, (2014)Towards Asynchronous Many-Task in Situ Data Analysis Using Legion., , , , , , , and . IPDPS Workshops, page 1033-1037. IEEE Computer Society, (2016)Priority research directions for in situ data management: Enabling scientific discovery from diverse data sources., , , , , , , and . Int. J. High Perform. Comput. Appl., (2020)Scalable Collectives for Distributed Asynchronous Many-Task Runtimes., , , , and . IPDPS Workshops, page 436-445. IEEE Computer Society, (2018)Metaprogramming-Enabled Parallel Execution of Apparently Sequential C++ Code., , , , , and . ESPM2@SC, page 24-31. IEEE Computer Society, (2016)A Novel Shard-Based Approach for Asynchronous Many-Task Models for In Situ Analysis., , , , and . ISAV@SC, page 27-31. ACM, (2017)In Situ Visualization for Computational Science (Dagstuhl Seminar 18271)., , , and . Dagstuhl Reports, 8 (7): 1-43 (2018)