mLR: Scalable Laminography Reconstruction based on Memoization
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912686580695040 |
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| author | Ma, Bin Nikitin, Viktor Wang, Xi Bicer, Tekin Li, Dong |
| author_facet | Ma, Bin Nikitin, Viktor Wang, Xi Bicer, Tekin Li, Dong |
| contents | ADMM-FFT is an iterative method with high reconstruction accuracy for laminography but suffers from excessive computation time and large memory consumption. We introduce mLR, which employs memoization to replace the time-consuming Fast Fourier Transform (FFT) operations based on an unique observation that similar FFT operations appear in iterations of ADMM-FFT. We introduce a series of techniques to make the application of memoization to ADMM-FFT performance-beneficial and scalable. We also introduce variable offloading to save CPU memory and scale ADMM-FFT across GPUs within and across nodes. Using mLR, we are able to scale ADMM-FFT on an input problem of 2Kx2Kx2K, which is the largest input problem laminography reconstruction has ever worked on with the ADMM-FFT solution on limited memory; mLR brings 52.8% performance improvement on average (up to 65.4%), compared to the original ADMM-FFT. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_01893 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | mLR: Scalable Laminography Reconstruction based on Memoization Ma, Bin Nikitin, Viktor Wang, Xi Bicer, Tekin Li, Dong Distributed, Parallel, and Cluster Computing Performance ADMM-FFT is an iterative method with high reconstruction accuracy for laminography but suffers from excessive computation time and large memory consumption. We introduce mLR, which employs memoization to replace the time-consuming Fast Fourier Transform (FFT) operations based on an unique observation that similar FFT operations appear in iterations of ADMM-FFT. We introduce a series of techniques to make the application of memoization to ADMM-FFT performance-beneficial and scalable. We also introduce variable offloading to save CPU memory and scale ADMM-FFT across GPUs within and across nodes. Using mLR, we are able to scale ADMM-FFT on an input problem of 2Kx2Kx2K, which is the largest input problem laminography reconstruction has ever worked on with the ADMM-FFT solution on limited memory; mLR brings 52.8% performance improvement on average (up to 65.4%), compared to the original ADMM-FFT. |
| title | mLR: Scalable Laminography Reconstruction based on Memoization |
| topic | Distributed, Parallel, and Cluster Computing Performance |
| url | https://arxiv.org/abs/2511.01893 |