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content/publication/20170621_hidayetoglu.md

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+draft = false
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+date = "2017-06-21"
+title = "Scalable Parallel DBIM Solutions of Inverse-Scattering Problems"
+authors = ["Mert Hidayetoglu", "Carl Pearson",  "Levent Gurel", "Wen-mei Hwu", "Weng Cho Chew"]
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+abstract = 'We report scalable solutions of inverse-scattering problems with the distorted Born iterative method (DBIM) on large number of computing nodes. Distributing forward solutions does not scale well when the number of illuminations is not greater than the number of computing nodes. As a remedy, we distribute both forward solutions and the corresponding forward solvers to improve granularity of DBIM solutions. This paper provides a set of solutions demonstrating good scaling of the proposed parallelization strategy up to 1,024 computing nodes, employing 16,394 processing cores in total.'
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+image = ""
+image_preview = ""
+math = false
+publication = "Computing and Electromagnetics International Workshop (CEM), 2017"
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+url_code = ""
+url_dataset = ""
+url_pdf = "pdf/20170621_hidayetoglu_cem.pdf"
+url_project = ""
+url_slides = ""
+url_video = ""
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+selected = true
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content/publication/20170621_hwu_cem.md

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+draft = false
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+date = "2017-06-21"
+title = "Thoughts on Massively-Parallel Heterogeneous Computing for Solving Large Problems"
+authors = ["Wen-mei Hwu", "Mert Hidayetoglu", "Weng Cho Chew", "Carl Pearson", "Simon Garcia de Gonzalo", "Sitao Huang", "Abdul Dakkak"]
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+abstract = 'In this paper, we present our view of massively-parallel heterogeneous computing for solving large scientific problems. We start by observing that computing has been the primary driver of major innovations since the beginning of the 21st century. We argue that this is the fruit of decades of progress in computing methods, technology, and systems. A high-level analysis on out-scaling and up-scaling on large supercomputers is given through a time-domain wave-scattering simulation example. The importance of heterogeneous node architectures for good up-scaling is highlighted. A case for low-complexity algorithms is made for continued scale-out towards exascale systems.'
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+image = ""
+image_preview = ""
+math = false
+publication = "*Computing and Electromagnetics International Workshop.* IEEE, 2017."
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+url_code = ""
+url_dataset = ""
+url_pdf = "pdf/20170621_hwu_cem.pdf"
+url_project = ""
+url_slides = ""
+url_video = ""
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+selected = true
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content/publication/20170621_pearson_cem.md

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+draft = false
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+date = "2017-06-21"
+title = "Comparative Performance Evaluation of Multi-GPU MLFMM Implementation for 2-D VIE Problems"
+authors = ["Carl Pearson", "Mert Hidayetoglu", "Wei Ren", "Weng Cho Chew", "Wen-Mei Hwu"]
+
+abstract = 'We compare multi-GPU performance of the multilevel fast multipole method (MLFMM) on two different systems: A shared-memory IBM S822LC workstation with four NVIDIA P100 GPUs, and 16 XK nodes (each is employed with a single NVIDIA K20X GPU) of the Blue Waters supercomputer. MLFMM is implemented for solving scattering problems involving two-dimensional inhomogeneous bodies. Results show that the multi-GPU implementation provides 794 and 969 times speedups on the IBM and Blue Waters systems over their corresponding sequential CPU executions, respectively, where the sequential execution on the IBM system is 1.17 times faster than on the Blue Waters System.'
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+image = ""
+image_preview = ""
+math = false
+publication = "*Computing and Electromagnetics International Workshop.* IEEE, 2017."
+
+url_code = ""
+url_dataset = ""
+url_pdf = "pdf/20170621_pearson_cem.pdf"
+url_project = ""
+url_slides = ""
+url_video = ""
+
+selected = true
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