70 lines
2.8 KiB
Markdown
70 lines
2.8 KiB
Markdown
+++
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title = "A Fast and Massively-Parallel Solver for Multiple-Scattering Tomographic Image Reconstruction"
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date = 2018-05-21
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draft = false
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# Authors. Comma separated list, e.g. `["Bob Smith", "David Jones"]`.
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authors = ["Mert Hidayetoglu", "Carl Pearson", "Izzat El Hajj", "Levent Gurel", "Weng Cho Chew", "Wen-Mei Hwu"]
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# Publication type.
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# Legend:
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# 0 = Uncategorized
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# 1 = Conference proceedings
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# 2 = Journal
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# 3 = Work in progress
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# 4 = Technical report
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# 5 = Book
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# 6 = Book chapter
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publication_types = ["1"]
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# Publication name and optional abbreviated version.
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publication = "In *2018 IEEE International Parallel and Distributed Processing Symposium*"
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publication_short = "In *IPDPS*"
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# Abstract and optional shortened version.
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abstract = "We present a massively-parallel solver for large Helmholtz-type inverse scattering problems. The solver employs the distorted Born iterative method for capturing the multiple-scattering phenomena in image reconstructions. This method requires many full-wave forward-scattering solutions in each iteration, constituting the main performance bottleneck with its high computational complexity. As a remedy, we use the multilevel fast multipole algorithm (MLFMA). The solver scales among computing nodes using a two-dimensional parallelization strategy that distributes illuminations in one dimension, and MLFMA sub-trees in the other dimension. Multi-core CPUs and GPUs are used to provide per-node speedup. We demonstrate a 76% efficiency when scaling from 64 GPUs to 4,096 GPUs. The paper provides reconstruction of a 204.8λ×204.8λ image (4M unknowns) executed on 4,096 GPUs in near-real time (almost 2 minutes). To the best of our knowledge, this is the largest full-wave inverse scattering solution to date, in terms of both image size and computational resources."
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abstract_short = ""
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highlight = true
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# Featured image thumbnail (optional)
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image_preview = ""
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selected = true
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# Projects (optional).
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# Associate this publication with one or more of your projects.
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# Simply enter your project's folder or file name without extension.
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# E.g. `projects = ["deep-learning"]` references
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# `content/project/deep-learning/index.md`.
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# Otherwise, set `projects = []`.
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projects = ["app_studies"]
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# Links (optional)
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url_pdf = "pdf/20180521_hidayetoglu_ipdps.pdf"
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url_preprint = ""
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url_code = ""
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url_dataset = ""
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url_project = ""
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url_slides = ""
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url_video = ""
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url_poster = ""
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url_source = ""
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# Featured image
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[image]
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caption = ""
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