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  1. Proceedings of the 5th Workshop on Irregular Applications: Architectures and Algorithms (IA3 '15)
  2. GAIL: the graph algorithm iron law
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How to match in parallel: approximation algorithms and multicore machines
A scalable architecture for ordered irregular parallelism
Data-centric GPU-based adaptive mesh refinement
Scalable task-based algorithm for multiplication of block-rank-sparse matrices
Tensor-matrix products with a compressed sparse tensor
Generalised vectorisation for sparse matrix: vector multiplication
Hybrid memory cube performance characterization on data-centric workloads
PL2AP: fast parallel cosine similarity search
PathFinder: a signature-search miniapp and its runtime characteristics
A GPU-parallel construction of volumetric tree
Dynamic parallelism for simple and efficient GPU graph algorithms
Betweenness centrality on Multi-GPU systems
GAIL: the graph algorithm iron law
Improving graph partitioning for modern graphs and architectures

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GAIL: the graph algorithm iron law

Content Provider ACM Digital Library
Author Beamer, Scott Patterson, David Asanović, Krste
Abstract As new applications for graph algorithms emerge, there has been a great deal of research interest in improving graph processing. However, it is often difficult to understand how these new contributions improve performance. Execution time, the most commonly reported metric, distinguishes which alternative is the fastest but does not give any insight as to why. A new contribution may have an algorithmic innovation that allows it to examine fewer graph edges. It could also have an implementation optimization that reduces communication. It could even have optimizations that allow it to increase its memory bandwidth utilization. More interestingly, a new innovation may simultaneously affect all three of these factors (algorithmic work, communication volume, and memory bandwidth utilization). We present the Graph Algorithm Iron Law (GAIL) to quantify these tradeoffs to help understand graph algorithm performance.
Starting Page 1
Ending Page 4
Page Count 4
File Format PDF
ISBN 9781450340014
DOI 10.1145/2833179.2833187
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2015-11-15
Publisher Place New York
Access Restriction Subscribed
Content Type Text
Resource Type Article
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