Implementing a Tiled Singular Value Decomposition: A Framework for Tiled Linear Algebra in Julia
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 t...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2024
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| Online Access: | https://hdl.handle.net/1721.1/157092 |
| _version_ | 1824458368175046656 |
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| author | Ringoot, Evelyne |
| author2 | Edelman, Alan |
| author_facet | Edelman, Alan Ringoot, Evelyne |
| author_sort | Ringoot, Evelyne |
| collection | MIT |
| description | 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. |
| first_indexed | 2025-02-19T04:24:47Z |
| format | Thesis |
| id | mit-1721.1/157092 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2025-02-19T04:24:47Z |
| publishDate | 2024 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1570922024-10-03T03:07:39Z Implementing a Tiled Singular Value Decomposition: A Framework for Tiled Linear Algebra in Julia Ringoot, Evelyne Edelman, Alan Massachusetts Institute of Technology. Center for Computational Science and Engineering 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. 2024-10-02T17:29:56Z 2024-10-02T17:29:56Z 2024-09 2024-09-04T15:34:36.918Z Thesis https://hdl.handle.net/1721.1/157092 In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Ringoot, Evelyne Implementing a Tiled Singular Value Decomposition: A Framework for Tiled Linear Algebra in Julia |
| title | Implementing a Tiled Singular Value Decomposition: A Framework for Tiled Linear Algebra in Julia |
| title_full | Implementing a Tiled Singular Value Decomposition: A Framework for Tiled Linear Algebra in Julia |
| title_fullStr | Implementing a Tiled Singular Value Decomposition: A Framework for Tiled Linear Algebra in Julia |
| title_full_unstemmed | Implementing a Tiled Singular Value Decomposition: A Framework for Tiled Linear Algebra in Julia |
| title_short | Implementing a Tiled Singular Value Decomposition: A Framework for Tiled Linear Algebra in Julia |
| title_sort | implementing a tiled singular value decomposition a framework for tiled linear algebra in julia |
| url | https://hdl.handle.net/1721.1/157092 |
| work_keys_str_mv | AT ringootevelyne implementingatiledsingularvaluedecompositionaframeworkfortiledlinearalgebrainjulia |