A Novel Shard-Based Approach for Asynchronous Many-Task Models for In Situ Analysis
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We present the current status of our work towards a scalable, asynchronous many-task, in situ statistical analysis engine using the Legion runtime system, expanding upon earlier work, that was limited to a prototype implementation with a proxy mini-application as a surrogate for a full-scale scientific simulation code. In contrast, we have more recently integrated our in situ analysis engines with S3D, a full-size scientific application, and conducted numerical tests therewith on the largest computational platform currently available for DOE science applications. The goal of this article is thus to describe the SPMD-Legion methodology we devised in this context, and compare the data aggregation technique deployed herein to the approach taken within our previous work.

Philippe P. Pébaÿ (Sandia National Laboratories)
Giulio Borghesi (Sandia National Laboratories)
Hemanth Kolla (Sandia National Laboratories)
Janine C. Bennett (Sandia National Laboratories)
Sean Treichler (NVIDIA)
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