Exploratory Data Science on Supercomputers for Quantum Mechanical Calculations
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866916249826492416 |
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| author | Dawson, William Beal, Louis Ratcliff, Laura E. Stella, Martina Nakajima, Takahito Genovese, Luigi |
| author_facet | Dawson, William Beal, Louis Ratcliff, Laura E. Stella, Martina Nakajima, Takahito Genovese, Luigi |
| contents | Literate programming - the bringing together of program code and natural language narratives - has become a ubiquitous approach in the realm of data science. This methodology is appealing as well for the domain of Density Functional Theory (DFT) calculations, particularly for interactively developing new methodologies and workflows. However, effective use of literate programming is hampered by old programming paradigms and the difficulties associated with using High Performance Computing (HPC) resources. Here we present two Python libraries that aim to remove these hurdles. First, we describe the PyBigDFT library, which can be used to setup materials or molecular systems and provides high-level access to the wavelet based BigDFT code. We then present the related remotemanager library, which is able to serialize and execute arbitrary Python functions on remote supercomputers. We show how together these libraries enable transparent access to HPC based DFT calculations and can serve as building blocks for rapid prototyping and data exploration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_09552 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Exploratory Data Science on Supercomputers for Quantum Mechanical Calculations Dawson, William Beal, Louis Ratcliff, Laura E. Stella, Martina Nakajima, Takahito Genovese, Luigi Chemical Physics Literate programming - the bringing together of program code and natural language narratives - has become a ubiquitous approach in the realm of data science. This methodology is appealing as well for the domain of Density Functional Theory (DFT) calculations, particularly for interactively developing new methodologies and workflows. However, effective use of literate programming is hampered by old programming paradigms and the difficulties associated with using High Performance Computing (HPC) resources. Here we present two Python libraries that aim to remove these hurdles. First, we describe the PyBigDFT library, which can be used to setup materials or molecular systems and provides high-level access to the wavelet based BigDFT code. We then present the related remotemanager library, which is able to serialize and execute arbitrary Python functions on remote supercomputers. We show how together these libraries enable transparent access to HPC based DFT calculations and can serve as building blocks for rapid prototyping and data exploration. |
| title | Exploratory Data Science on Supercomputers for Quantum Mechanical Calculations |
| topic | Chemical Physics |
| url | https://arxiv.org/abs/2310.09552 |