A Scalable Inference Pipeline for 3D Axon Tracing Algorithms
Name
Scalability_AxonTracing_HPEC_2022_submission.pdf
Description
Accepted version
Size
496.61 KB
Format
Adobe PDF
Checksum (MD5)
801ae1f42c1ffc4ccfca78dd2305c690
Author(s)
Fenelon, Benjamin
Gjesteby, Lars A.
Guan, Webster
Park, Juhyuk
Chung, Kwanghun
Brattain, Laura J.
Date Issued
September 19, 2022
Publisher
IEEE|2022 IEEE High Performance Extreme Computing Conference (HPEC)
Citation
Fenelon, Benjamin, Gjesteby, Lars A., Guan, Webster, Park, Juhyuk, Chung, Kwanghun et al. 2022. "A Scalable Inference Pipeline for 3D Axon Tracing Algorithms." 00.
Version
Author's final manuscript
Abstract
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.
Description
2022 IEEE High Performance Extreme Computing Conference (HPEC) 19-23 September 2022 Waltham, MA, USA
MIT Department
Lincoln Laboratory
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Terms of Use
Creative Commons Attribution-Noncommercial-ShareAlike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/hpec55821.2022.9926403
