publications, experiment with experience list
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title = "Update on k-truss Decomposition on GPU"
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date = 2019-08-22T00:00:00 # Schedule page publish date.
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title = "[HPEC] Update on k-truss Decomposition on GPU"
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date = 2019-08-22 # Schedule page publish date.
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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 = ["Mohammad Almasri", "Omer Anjum", "Carl Pearson", "Vikram S. Mailthody", "Zaid Qureshi", "Rakesh Nagi", "Jinjun Xiong", "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 paper
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# 2 = Journal article
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# 3 = Manuscript
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# 4 = Report
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# 5 = Book
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# 6 = Book section
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publication_types = ["1"]
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# Publication name and optional abbreviated version.
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publication = "2019 IEEE High Performance Extreme Computing Conference"
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publication_short = "In *HPEC'19*"
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math = false
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highlight = false
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# Featured image thumbnail (optional)
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image_preview = ""
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# Is this a selected publication? (true/false)
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selected = false
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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 = []
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# Links (optional)
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url_pdf = "pdf/2019_almasri_hpec.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 = "pdf/2019_almasri_hpec_slides.pdf"
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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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caption = ""
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focal_point = ""
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+++
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**Mohammad Almasri, Omer Anjum, Carl Pearson, Vikram S. Mailthody, Zaid Qureshi, Rakesh Nagi, Jinjun Xiong, Wen-Mei Hwu**
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In *2019 IEEE High Performance Extreme Computing Conference*
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In this paper, we present an update to our previous submission on k-truss decomposition from Graph Challenge 2018.
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For single GPU k-truss implementation, we propose multiple algorithmic optimizations that significantly improve performance by up to 35.2x (6.9x on average) compared to our previous GPU implementation. In addition, we present a scalable multi-GPU implementation in which each GPU handles a different 'k' value.
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Compared to our prior multi-GPU implementation,the proposed approach is faster by up to 151.3x (78.8x on average). In case when the edges with only maximal k-truss are sought, incrementing the 'k' value in each iteration is inefficient particularly for graphs with large maximum k-truss.
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Thus, we propose binary search for the 'k' value to find the maximal k-truss. The binary search approach on a single GPU is up to 101.5 (24.3x on average) faster than our 2018 $k$-truss submission.
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Lastly, we show that the proposed binary search finds the maximum k-truss for "Twitter" graph dataset having 2.8 billion bidirectional edges in just 16 minutes on a single V100 GPU.
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Lastly, we show that the proposed binary search finds the maximum k-truss for "Twitter" graph dataset having 2.8 billion bidirectional edges in just 16 minutes on a single V100 GPU.
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* [pdf](/pdf/2019_almasri_hpec.pdf)
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* [slides](/pdf/2019_almasri_hpec_slides.pdf)
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