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oai:dspace.mit.edu:1721.1/1570922024-10-03T03:07:39Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023

Edelman, Alan Ringoot, Evelyne Massachusetts Institute of Technology. Center for Computational Science and Engineering 2024-10-02T17:29:56Z 2024-10-02T17:29:56Z 2024-09 2024-09-04T15:34:36.918Z https://hdl.handle.net/1721.1/157092 High-performance computing (HPC) is essential for scientific research, enabling complex simulations and analyses across various fields. However, the specialized knowledge required to utilize HPC effectively can be a barrier for many scientists. This work introduces a hardware-agnostic, large-scale tiled linear algebra framework in Julia designed to enhance accessibility and usability without compromising performance. By providing a flexible abstraction layer, the framework simplifies the development and testing of new algorithms across diverse computing architectures. Julia language’s multiple-dispatch and type inference facilitate the development of type-agnostic, hardware-agnostic, and multi-use frameworks by allowing composability. Utilizing a tiled approach, the implemented framework improves data locality, parallelism, and scalability, making it well-suited for modern heterogeneous environments. Its practical benefits are demonstrated through the implementation of tiled QR-based singular value decomposition (SVD), demonstrating how it streamlines the development process and accelerates scientific discovery. The developed framework is used to implement an in-GPU tiled SVD and an out-of-core GPU-accelerated SVD. Furthermore, its extensibility is demonstrated by implementing a tiled QR algorithm. This work aims to democratize HPC resources by bridging the gap between advanced computational capabilities and user accessibility, empowering a broader range of scientists to fully leverage modern computing technologies. S.M. Massachusetts Institute of Technology In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ Implementing a Tiled Singular Value Decomposition: A Framework for Tiled Linear Algebra in Julia Thesis application/pdf Master Master of Science in Computational Science and Engineering Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="d553bb1e-16e9-46a3-a568-7f20f5e5ad34"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Title>Implementing a Tiled Singular Value Decomposition: A Framework for Tiled Linear Algebra in Julia</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2024-09</PublicationDate> <Authors> <Author> <DisplayName>Ringoot, Evelyne</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>https://rightsstatements.org/page/InC-EDU/1.0/</License> <Abstract>High-performance computing (HPC) is essential for scientific research, enabling complex simulations and analyses across various fields. However, the specialized knowledge required to utilize HPC effectively can be a barrier for many scientists. This work introduces a hardware-agnostic, large-scale tiled linear algebra framework in Julia designed to enhance accessibility and usability without compromising performance. By providing a flexible abstraction layer, the framework simplifies the development and testing of new algorithms across diverse computing architectures. Julia language’s multiple-dispatch and type inference facilitate the development of type-agnostic, hardware-agnostic, and multi-use frameworks by allowing composability. Utilizing a tiled approach, the implemented framework improves data locality, parallelism, and scalability, making it well-suited for modern heterogeneous environments. Its practical benefits are demonstrated through the implementation of tiled QR-based singular value decomposition (SVD), demonstrating how it streamlines the development process and accelerates scientific discovery. The developed framework is used to implement an in-GPU tiled SVD and an out-of-core GPU-accelerated SVD. Furthermore, its extensibility is demonstrated by implementing a tiled QR algorithm. This work aims to democratize HPC resources by bridging the gap between advanced computational capabilities and user accessibility, empowering a broader range of scientists to fully leverage modern computing technologies.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>