71 lines
2.8 KiB
Markdown
71 lines
2.8 KiB
Markdown
+++
|
|
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. |