Towards a New Directive-based Tasking API for Distributed Systems

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dc.contributor.advisorSubhlok, Jaspal
dc.contributor.committeeMemberChapman, Barbara M.
dc.contributor.committeeMemberGabriel, Edgar
dc.contributor.committeeMemberMarcinkiewicz, Henryk
dc.creatorAltuaimi, Abdulelah 1987-
dc.date.accessioned2017-04-17T00:53:32Z
dc.date.available2017-04-17T00:53:32Z
dc.date.createdDecember 2016
dc.date.issued2016-12
dc.date.submittedDecember 2016
dc.date.updated2017-04-17T00:53:32Z
dc.description.abstractProgramming for large-scale computing requires programming models carefully designed for that purpose. MPI is often the model of choice for distributed systems, but writing MPI program is time-consuming and complicated to maintain and debug as the program size gets larger. Moreover, MPI does not exploit some of the potential benefits of shared memory systems. Using a hybrid model also requires a high level of programmer expertise. Designing algorithms in terms of tasks potentially reduces the development effort and has many performance-related advantages. In addition, directive-based programming styles have made parallel programming and migration of serial code to multicore chips easier than ever. Although directive-based tasking models have paved the way to distributed systems, they still lack capabilities necessary for efficient large-scale computing. TagHit is an API proposed by the HPCTools group in the Department of Computer Science at the University of Houston. Targeted for exascale computing, TagHit combines the benefits of task-based programming models with the simplicity of directive-based programming styles. This thesis tackles task creation and scheduling in TagHit. First, I present an overview of six existing task-based programming models. Next, I propose an experimental runtime design of TagHit's task creation and scheduling modules and then describe in detail a prototype implementation of the runtime. The goal of this work is to guide the definition of TagHit's concept and semantics and to assess the implementation cost and challenges of creating and scheduling tasks in TagHit. Finally, I present two TagHit benchmarks with results that show the design and implementation have supported the general concept of TagHit with good speedup and scheduling behavior.
dc.description.departmentComputer Science, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/10657/1707
dc.language.isoeng
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectParallel
dc.subjectDistributed
dc.subjectShared Memory
dc.subjectDistributed systems
dc.subjectDistributed memory
dc.subjectDirective-based
dc.subjectMPI
dc.subjectAPI
dc.subjectTasking
dc.subjectTask-based
dc.subjectTask Scheduling
dc.subjectWork-stealing
dc.subjectExascale
dc.subjectLarge-scale computing
dc.subjectComputing systems
dc.subjectProgramming
dc.titleTowards a New Directive-based Tasking API for Distributed Systems
dc.type.dcmiText
dc.type.genreThesis
thesis.degree.collegeCollege of Natural Sciences and Mathematics
thesis.degree.departmentComputer Science, Department of
thesis.degree.disciplineComputer Science
thesis.degree.grantorUniversity of Houston
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

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