Optimal Compressed Sensing and Reconstruction of Unstructured Mesh Datasets
Abstract Exascale computing promises quantities of data too large to efficiently store and transfer across networks in order to be able to analyze and visualize the results. We investigate compressed sensing (CS) as an in situ method to reduce the size of the data as it is being generated during a l...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2017-08-01
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| Series: | Data Science and Engineering |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1007/s41019-017-0042-4 |
| _version_ | 1819275671256432640 |
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| author | Maher Salloum Nathan D. Fabian David M. Hensinger Jina Lee Elizabeth M. Allendorf Ankit Bhagatwala Myra L. Blaylock Jacqueline H. Chen Jeremy A. Templeton Irina Tezaur |
| author_facet | Maher Salloum Nathan D. Fabian David M. Hensinger Jina Lee Elizabeth M. Allendorf Ankit Bhagatwala Myra L. Blaylock Jacqueline H. Chen Jeremy A. Templeton Irina Tezaur |
| author_sort | Maher Salloum |
| collection | DOAJ |
| description | Abstract Exascale computing promises quantities of data too large to efficiently store and transfer across networks in order to be able to analyze and visualize the results. We investigate compressed sensing (CS) as an in situ method to reduce the size of the data as it is being generated during a large-scale simulation. CS works by sampling the data on the computational cluster within an alternative function space such as wavelet bases and then reconstructing back to the original space on visualization platforms. While much work has gone into exploring CS on structured datasets, such as image data, we investigate its usefulness for point clouds such as unstructured mesh datasets often found in finite element simulations. We sample using a technique that exhibits low coherence with tree wavelets found to be suitable for point clouds. We reconstruct using the stagewise orthogonal matching pursuit algorithm that we improved to facilitate automated use in batch jobs. We analyze the achievable compression ratios and the quality and accuracy of reconstructed results at each compression ratio. In the considered case studies, we are able to achieve compression ratios up to two orders of magnitude with reasonable reconstruction accuracy and minimal visual deterioration in the data. Our results suggest that, compared to other compression techniques, CS is attractive in cases where the compression overhead has to be minimized and where the reconstruction cost is not a significant concern. |
| first_indexed | 2024-12-23T23:28:02Z |
| format | Article |
| id | doaj.art-a64fa43256494184a5cbaf6fddcba48f |
| institution | Directory Open Access Journal |
| issn | 2364-1185 2364-1541 |
| language | English |
| last_indexed | 2024-12-23T23:28:02Z |
| publishDate | 2017-08-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Data Science and Engineering |
| spelling | doaj.art-a64fa43256494184a5cbaf6fddcba48f2022-12-21T17:26:10ZengSpringerOpenData Science and Engineering2364-11852364-15412017-08-013112310.1007/s41019-017-0042-4Optimal Compressed Sensing and Reconstruction of Unstructured Mesh DatasetsMaher Salloum0Nathan D. Fabian1David M. Hensinger2Jina Lee3Elizabeth M. Allendorf4Ankit Bhagatwala5Myra L. Blaylock6Jacqueline H. Chen7Jeremy A. Templeton8Irina Tezaur9Sandia National LaboratoriesSandia National LaboratoriesSandia National LaboratoriesSandia National LaboratoriesUniversity of CaliforniaPilot AI LabsSandia National LaboratoriesSandia National LaboratoriesSandia National LaboratoriesSandia National LaboratoriesAbstract Exascale computing promises quantities of data too large to efficiently store and transfer across networks in order to be able to analyze and visualize the results. We investigate compressed sensing (CS) as an in situ method to reduce the size of the data as it is being generated during a large-scale simulation. CS works by sampling the data on the computational cluster within an alternative function space such as wavelet bases and then reconstructing back to the original space on visualization platforms. While much work has gone into exploring CS on structured datasets, such as image data, we investigate its usefulness for point clouds such as unstructured mesh datasets often found in finite element simulations. We sample using a technique that exhibits low coherence with tree wavelets found to be suitable for point clouds. We reconstruct using the stagewise orthogonal matching pursuit algorithm that we improved to facilitate automated use in batch jobs. We analyze the achievable compression ratios and the quality and accuracy of reconstructed results at each compression ratio. In the considered case studies, we are able to achieve compression ratios up to two orders of magnitude with reasonable reconstruction accuracy and minimal visual deterioration in the data. Our results suggest that, compared to other compression techniques, CS is attractive in cases where the compression overhead has to be minimized and where the reconstruction cost is not a significant concern.http://link.springer.com/article/10.1007/s41019-017-0042-4Compressed sensingTree waveletsCompressionIn situLarge-scale simulationUnstructured mesh |
| spellingShingle | Maher Salloum Nathan D. Fabian David M. Hensinger Jina Lee Elizabeth M. Allendorf Ankit Bhagatwala Myra L. Blaylock Jacqueline H. Chen Jeremy A. Templeton Irina Tezaur Optimal Compressed Sensing and Reconstruction of Unstructured Mesh Datasets Data Science and Engineering Compressed sensing Tree wavelets Compression In situ Large-scale simulation Unstructured mesh |
| title | Optimal Compressed Sensing and Reconstruction of Unstructured Mesh Datasets |
| title_full | Optimal Compressed Sensing and Reconstruction of Unstructured Mesh Datasets |
| title_fullStr | Optimal Compressed Sensing and Reconstruction of Unstructured Mesh Datasets |
| title_full_unstemmed | Optimal Compressed Sensing and Reconstruction of Unstructured Mesh Datasets |
| title_short | Optimal Compressed Sensing and Reconstruction of Unstructured Mesh Datasets |
| title_sort | optimal compressed sensing and reconstruction of unstructured mesh datasets |
| topic | Compressed sensing Tree wavelets Compression In situ Large-scale simulation Unstructured mesh |
| url | http://link.springer.com/article/10.1007/s41019-017-0042-4 |
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