cwpearson/hugo-cwpearson
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

add ipdps 2018 paper

Browse Source
This commit is contained in:
Carl Pearson
2018-09-06 12:43:42 -04:00
parent 963364201b
commit d44f0ebc5a
1 changed files with 4 additions and 4 deletions
Download Patch File Download Diff File Expand all files Collapse all files

8
content/publication/20180521_hidayetoglu_ipdps.md
View File

@@ -1,6 +1,6 @@
+++
title = "A Fast and Massively-Parallel Solver for Nonlinear Tomographic Image Reconstruction"
date = 2018-05-18
date = 2018-05-21
draft = false
# Authors. Comma separated list, e.g. `["Bob Smith", "David Jones"]`.
@@ -18,11 +18,11 @@ authors = ["Mert Hidayetoglu", "Carl Pearson", "Izzat El Hajj", "Levent Gurel",
publication_types = ["1"]
# Publication name and optional abbreviated version.
publication = "32nd IEEE International Parallel and Distributed Processing"
publication = "2018 IEEE International Parallel and Distributed Processing Symposium"
publication_short = "IPDPS 2018"
# Abstract and optional shortened version.
abstract = ""
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."
abstract_short = ""
# Does this page contain LaTeX math? (true/false)
@@ -44,7 +44,7 @@ selected = true
projects = []
# Links (optional)
url_pdf = ""
url_pdf = "pdf/20180521_hidayetoglu_ipdps.pdf"
url_preprint = ""
url_code = ""
url_dataset = ""