Mapping High Level Parallel Programming Models to Asynchronous Many-Task (AMT) Runtimes

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dc.contributor.advisorSarkar, Vivek
dc.creatorPaul, Sri Raj
dc.date.accessioned2019-05-16T18:06:23Z
dc.date.available2019-05-16T18:06:23Z
dc.date.created2019-05
dc.date.issued2018-12-20
dc.date.submittedMay 2019
dc.date.updated2019-05-16T18:06:23Z
dc.description.abstractAsynchronous Many-Task (AMT) runtimes have recently been proposed as a promising software foundation for managing the increasing complexity of node architectures in current and future extreme-scale computing systems because of their ability to express fine-grained parallelism, to decouple computation and data from underlying machine resources, to support resilience, and to deliver scalable performance. The Open Community Runtime (OCR) is a community-led effort to explore AMT runtime principles that can support a broad range of higher-level programming models. The Habanero C/C++ library (HClib) is a library-based AMT runtime and programming interface, which focuses on lightweight task creation/termination and flexible synchronization. Unlike other AMT runtimes, both OCR and HClib include first-class support for event-driven task execution, which can help with hiding communication latencies and with reducing the number of blocking operations performed. In this thesis, we focus on the problem of mapping high-level parallel programming models to AMT runtimes. As an exemplar of modern Partitioned Global Address Space (PGAS) parallel programming models, we show how Chapel programs can be efficiently mapped on to OCR and HClib, and also how Legion, a data-centric parallel programming model, can be mapped on to OCR. Next, we show how PGAS and event-driven execution models can be synergistically integrated in a unique combination of server-side JavaScript and HClib, yielding new levels of programming productivity for high performance computing. Finally, we show how the promise of supporting resilience in AMT runtimes can be realized through programming model extensions to HClib. All these contributions are accompanied by performance evaluations of prototype implementations. Our results show that AMT runtimes can support high-level parallel programming models with comparable or improved performance relative to existing runtimes, while also providing the potential for improved resilience.
dc.format.mimetypeapplication/pdf
dc.identifier.citationPaul, Sri Raj. "Mapping High Level Parallel Programming Models to Asynchronous Many-Task (AMT) Runtimes." (2018) Diss., Rice University. <a href="https://hdl.handle.net/1911/105350">https://hdl.handle.net/1911/105350</a>.
dc.identifier.urihttps://hdl.handle.net/1911/105350
dc.language.isoeng
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.
dc.subjectParallel Programming
dc.subjectHigh-Performance Computing
dc.subjectDistributed Systems
dc.subjectAsynchronous Many-Task (AMT) Runtimes
dc.subjectResiliency
dc.subjectCommunication Latency
dc.titleMapping High Level Parallel Programming Models to Asynchronous Many-Task (AMT) Runtimes
dc.typeThesis
dc.type.materialText
thesis.degree.departmentComputer Science
thesis.degree.disciplineEngineering
thesis.degree.grantorRice University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy
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