2.8 KiB
+++ title = "Update on k-truss Decomposition on GPU" date = 2019-08-22T00:00:00 # Schedule page publish date. draft = false
Authors. Comma separated list, e.g. ["Bob Smith", "David Jones"].
authors = ["Mohammad Almasri", "Omer Anjum", "Carl Pearson", "Vikram S. Mailthody", "Zaid Qureshi", "Rakesh Nagi", "Jinjun Xiong", "Wen-Mei Hwu"]
Publication type.
Legend:
0 = Uncategorized
1 = Conference paper
2 = Journal article
3 = Manuscript
4 = Report
5 = Book
6 = Book section
publication_types = ["1"]
Publication name and optional abbreviated version.
publication = "2019 IEEE High Performance Extreme Computing Conference" publication_short = "In HPEC'19"
Does this page contain LaTeX math? (true/false)
math = false
Does this page require source code highlighting? (true/false)
highlight = false
Featured image thumbnail (optional)
image_preview = ""
Is this a selected publication? (true/false)
selected = false
Projects (optional).
Associate this publication with one or more of your projects.
Simply enter your project's folder or file name without extension.
E.g. projects = ["deep-learning"] references
content/project/deep-learning/index.md.
Otherwise, set projects = [].
projects = []
Links (optional)
url_pdf = "pdf/2019_almasri_hpec.pdf" url_preprint = "" url_code = "" url_dataset = "" url_project = "" url_slides = "pdf/2019_almasri_hpec_slides.pdf" url_video = "" url_poster = "" url_source = ""
Featured image
To use, add an image named featured.jpg/png to your page's folder.
[image]
Caption (optional)
caption = ""
Focal point (optional)
Options: Smart, Center, TopLeft, Top, TopRight, Left, Right, BottomLeft, Bottom, BottomRight
focal_point = "" +++
In this paper, we present an update to our previous submission on k-truss decomposition from Graph Challenge 2018. 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. 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. 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. 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.