A Scalable Inference Pipeline for 3D Axon Tracing Algorithms
2022 IEEE High Performance Extreme Computing Conference (HPEC) 19-23 September 2022 Waltham, MA, USA
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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IEEE|2022 IEEE High Performance Extreme Computing Conference (HPEC)
2024
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| Online Access: | https://hdl.handle.net/1721.1/155787 |
| _version_ | 1811081859936813056 |
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| author | Fenelon, Benjamin Gjesteby, Lars A. Guan, Webster Park, Juhyuk Chung, Kwanghun Brattain, Laura J. |
| author2 | Lincoln Laboratory |
| author_facet | Lincoln Laboratory Fenelon, Benjamin Gjesteby, Lars A. Guan, Webster Park, Juhyuk Chung, Kwanghun Brattain, Laura J. |
| author_sort | Fenelon, Benjamin |
| collection | MIT |
| description | 2022 IEEE High Performance Extreme Computing Conference (HPEC) 19-23 September 2022 Waltham, MA, USA |
| first_indexed | 2024-09-23T11:53:37Z |
| format | Article |
| id | mit-1721.1/155787 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:53:37Z |
| publishDate | 2024 |
| publisher | IEEE|2022 IEEE High Performance Extreme Computing Conference (HPEC) |
| record_format | dspace |
| spelling | mit-1721.1/1557872024-09-20T17:29:39Z A Scalable Inference Pipeline for 3D Axon Tracing Algorithms Fenelon, Benjamin Gjesteby, Lars A. Guan, Webster Park, Juhyuk Chung, Kwanghun Brattain, Laura J. Lincoln Laboratory Massachusetts Institute of Technology. Institute for Medical Engineering & Science 2022 IEEE High Performance Extreme Computing Conference (HPEC) 19-23 September 2022 Waltham, MA, USA High inference times of machine learning-based axon tracing algorithms pose a significant challenge to the practical analysis and interpretation of large-scale brain imagery. This paper explores a distributed data pipeline that employs a SLURM-based job array to run multiple machine learning algorithm predictions simultaneously. Image volumes were split into N (1–16) equal chunks that are each handled by a unique compute node and stitched back together into a single 3D prediction. Preliminary results comparing the inference speed of 1 versus 16 node job arrays demonstrated a 90.95% decrease in compute time for 32 GB input volume and 88.41% for 4 GB input volume. The general pipeline may serve as a baseline for future improved implementations on larger input volumes which can be tuned to various application domains. 2024-07-25T14:20:14Z 2024-07-25T14:20:14Z 2022-09-19 2024-07-25T13:42:40Z Article http://purl.org/eprint/type/ConferencePaper https://hdl.handle.net/1721.1/155787 Fenelon, Benjamin, Gjesteby, Lars A., Guan, Webster, Park, Juhyuk, Chung, Kwanghun et al. 2022. "A Scalable Inference Pipeline for 3D Axon Tracing Algorithms." 00. en 10.1109/hpec55821.2022.9926403 Creative Commons Attribution-Noncommercial-ShareAlike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf IEEE|2022 IEEE High Performance Extreme Computing Conference (HPEC) Author |
| spellingShingle | Fenelon, Benjamin Gjesteby, Lars A. Guan, Webster Park, Juhyuk Chung, Kwanghun Brattain, Laura J. A Scalable Inference Pipeline for 3D Axon Tracing Algorithms |
| title | A Scalable Inference Pipeline for 3D Axon Tracing Algorithms |
| title_full | A Scalable Inference Pipeline for 3D Axon Tracing Algorithms |
| title_fullStr | A Scalable Inference Pipeline for 3D Axon Tracing Algorithms |
| title_full_unstemmed | A Scalable Inference Pipeline for 3D Axon Tracing Algorithms |
| title_short | A Scalable Inference Pipeline for 3D Axon Tracing Algorithms |
| title_sort | scalable inference pipeline for 3d axon tracing algorithms |
| url | https://hdl.handle.net/1721.1/155787 |
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