Abstraction of Computation and Data Motion in High-Performance Computing Systems

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dc.contributor.advisorCheung, Margaret S.
dc.contributor.committeeMemberShi, Weidong
dc.contributor.committeeMemberGabriel, Edgar
dc.contributor.committeeMemberChandrasekaran, Sunita
dc.creatorGhane, Millad 1985-
dc.date.accessioned2019-12-17T20:14:06Z
dc.date.createdDecember 2019
dc.date.issued2019-12
dc.date.submittedDecember 2019
dc.date.updated2019-12-17T20:14:07Z
dc.description.abstractSupercomputers are at the forefront of science and technology and play a crucial role in the advancement of contemporary scientific and industrial domains. Such advancement is due to the rapid developments of the underlying hardware of supercomputers, which in turn, have led to complicated hardware designs. Unlike decades ago when supercomputers were homogeneous in their design, their current developments have been widely heterogeneous to lower their energy and power consumption. As hardware architectures of supercomputers become complex, so do the software applications that target them. In recent years, scientists have been utilizing directive-based programming models, such as OpenMP and OpenACC, to mitigate the complexity of developing software. These programming models enable scientists to parallelize their code with minimum code interventions from their developers. However, targeting heterogeneous systems effectively is still a challenge despite having productive programming environments. In this dissertation, we will introduce a directive-based programming model and a hierarchical model to improve the usability and portability of several scientific applications and prepare them for the exascale era. For the first model, our pointerchain directive replaces a chain of pointers with its corresponding effective address inside a parallel region of code. Pointerchain enables developers to efficiently perform deep copying of the data structures in heterogeneous platforms. Based on our analysis, pointerchain has led to 39% and 38% reductions in the amount of generated code and the total executed instructions, respectively. Secondly, our hierarchical model, Gecko, abstracts the underlying memory hierarchy of the exascale platforms. This abstraction paves the way for developing scientific applications on supercomputers. To prove its feasibility, we developed an implementation of Gecko as a directive-based programming model. Moreover, to evaluate its effectiveness, we ported real scientific benchmark applications — ranging from linear algebra to fluid dynamics — to Gecko. Furthermore, we also demonstrated how Gecko helps developers with code portability and ease-of-use in real scenarios. Gecko achieved a 3.3 speedup on a four-GPU system with respect to one single GPU while having only a single source-code base.
dc.description.departmentComputer Science, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.citationPortions of this document appear in: Ghane, Millad, Sunita Chandrasekaran, and Margaret S. Cheung. "Gecko: Hierarchical Distributed View of Heterogeneous Shared Memory Architectures." In Proceedings of the 10th International Workshop on Programming Models and Applications for Multicores and Manycores, pp. 21-30. ACM, 2019. And in: Ghane, Millad, Sunita Chandrasekaran, and Margaret S. Cheung. "pointerchain: Tracing pointers to their roots–A case study in molecular dynamics simulations." Parallel Computing 85 (2019): 190-203. And in: Ghane, Millad, Sunita Chandrasekaran, and Margaret S. Cheung. "Assessing Performance Implications of Deep Copy Operations via Microbenchmarking." arXiv preprint arXiv:1906.01128 (2019).
dc.identifier.urihttps://hdl.handle.net/10657/5597
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. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectAbstraction
dc.subjectHierarchy
dc.subjectHeterogeneity
dc.subjectPortable
dc.subjectShared Memory
dc.subjectProgramming
dc.subjectModel
dc.subjectLanguage
dc.subjectExascale
dc.subjectHPC
dc.subjectDirectives
dc.subjectSupercomputer
dc.titleAbstraction of Computation and Data Motion in High-Performance Computing Systems
dc.type.dcmiText
dc.type.genreThesis
local.embargo.lift2021-12-01
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.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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