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update anatole, work on publications, add talks list

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Carl Pearson
2021-01-27 17:40:20 -07:00
parent 163a470f3f
commit 3a685bf1a6
28 changed files with 204 additions and 780 deletions
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content/publication/20190926_almasri_hpec/index.md
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@@ -21,16 +21,6 @@ publication_types = ["1"]
publication = "2019 IEEE High Performance Extreme Computing Conference"
publication_short = "In *HPEC'19*"
# Abstract and optional shortened version.
abstract = """
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.
"""
abstract_short = ""
# Does this page contain LaTeX math? (true/false)
math = false
@@ -73,3 +63,9 @@ url_source = ""
# 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.