Graph Library project
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---
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title: Multi-GPU Graph Library
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summary: Accelerating Static and Dynamic Graph Operations
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title: Graph Library
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summary: Accelerating Static Graph Operations
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tags:
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- impact
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- c3sr
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links:
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- icon: github
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icon_pack: fab
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name: Graph Challenge Code
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name: Pangolin Graph Library
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url: https://github.com/c3sr/pangolin
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- icon: link
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icon_pack: fa
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name: Pangolin C++ API Documentation
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url: https://pangolin-docs.netlify.app/
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- icon: github
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icon_pack: fab
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name: Graph Challenge
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url: https://github.com/c3sr/graph_challenge
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- icon: github
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icon_pack: fab
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name: Graph Library Code
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url: https://github.com/c3sr/pangolin
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- icon: github
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icon_pack: fab
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name: Graph Dataset Tools
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url: https://github.com/cwpearson/graph-datasets
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url: https://github.com/cwpearson/graph-datasets2
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- icon: link
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icon_pack: fa
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name: Graph Dataset Statistics
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url: https://graph-datasets-stats.netlify.com
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url_code: ""
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url_pdf: ""
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url_slides: ""
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@@ -41,4 +46,9 @@ url_video: ""
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# E.g. `slides = "example-slides"` references `content/slides/example-slides.md`.
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# Otherwise, set `slides = ""`.
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slides: ""
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---
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---
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This project grew out of a 2018 graph challenge submission that I was tangentially involved in.
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In 2019 I took charge of the triangle counting submission, where I implemented a variety of GPU triangle counting approaches.
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This library contains those implementations, as well as k-truss decompositions written by my colleague Mohammad Almasri.
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It also contains a fair amount of utility code, including sparse and dense data structures, system topology discovery code and search/load-balancing algorithms.
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