The GAP Benchmark Suite

We present a graph processing benchmark suite with the goal of helping to standardize graph processing evaluations. Fewer differences between graph processing evaluations will make it easier to compare different research efforts and quantify improvements. The benchmark not only specifies graph kernels, input graphs, and evaluation methodologies, but it also provides optimized baseline implementations. These baseline implementations are representative of state-of-the-art performance, and thus new contributions should outperform them to demonstrate an improvement. The input graphs are sized appropriately for shared memory platforms, but any implementation on any platform that conforms to the benchmark's specifications could be compared. This benchmark suite can be used in a variety of settings. Graph framework developers can demonstrate the generality of their programming model by implementing all of the benchmark's kernels and delivering competitive performance on all of the benchmark's graphs. Algorithm designers can use the input graphs and the baseline implementations to demonstrate their contribution. Platform designers and performance analysts can use the suite as a workload representative of graph processing.

[1]  Guy E. Blelloch,et al.  Brief announcement: the problem based benchmark suite , 2012, SPAA '12.

[2]  David A. Patterson,et al.  Direction-optimizing Breadth-First Search , 2012, 2012 International Conference for High Performance Computing, Networking, Storage and Analysis.

[3]  Pradeep Dubey,et al.  GraphMat: High performance graph analytics made productive , 2015, Proc. VLDB Endow..

[4]  David A. Bader,et al.  An Experimental Study of A Parallel Shortest Path Algorithm for Solving Large-Scale Graph Instances , 2007, ALENEX.

[5]  Peter Sanders,et al.  [Delta]-stepping: a parallelizable shortest path algorithm , 2003, J. Algorithms.

[6]  Ralph Johnson,et al.  design patterns elements of reusable object oriented software , 2019 .

[7]  Michael Isard,et al.  Scalability! But at what COST? , 2015, HotOS.

[8]  Timothy A. Davis,et al.  The university of Florida sparse matrix collection , 2011, TOMS.

[9]  U. Brandes A faster algorithm for betweenness centrality , 2001 .

[10]  Scott Meyers,et al.  Effective C++: 55 Specific Ways to Improve Your Programs and Designs (3rd Edition) , 1991 .

[11]  Guy E. Blelloch,et al.  Ligra: a lightweight graph processing framework for shared memory , 2013, PPoPP '13.

[12]  David A. Patterson,et al.  Locality Exists in Graph Processing: Workload Characterization on an Ivy Bridge Server , 2015, 2015 IEEE International Symposium on Workload Characterization.

[13]  D. Patterson,et al.  Searching for a Parent Instead of Fighting Over Children : A Fast Breadth-First Search Implementation for Graph 500 , 2011 .

[14]  David A. Bader,et al.  On the architectural requirements for efficient execution of graph algorithms , 2005, 2005 International Conference on Parallel Processing (ICPP'05).

[15]  R. F. Boisvert,et al.  The Matrix Market Exchange Formats: Initial Design | NIST , 1996 .

[16]  David A. Bader,et al.  National Laboratory Lawrence Berkeley National Laboratory Title A Faster Parallel Algorithm and Efficient Multithreaded Implementations for Evaluating Betweenness Centrality on Massive Datasets Permalink , 2009 .

[17]  Keshav Pingali,et al.  A lightweight infrastructure for graph analytics , 2013, SOSP.

[18]  Hosung Park,et al.  What is Twitter, a social network or a news media? , 2010, WWW '10.

[19]  Andrew S. Grimshaw,et al.  Scalable GPU graph traversal , 2012, PPoPP '12.

[20]  Uzi Vishkin,et al.  An O(log n) Parallel Connectivity Algorithm , 1982, J. Algorithms.

[21]  Scott Beamer,et al.  Understanding and Improving Graph Algorithm Performance , 2016 .

[22]  Christos Faloutsos,et al.  Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication , 2005, PKDD.

[23]  Alan M. Frieze,et al.  Random graphs , 2006, SODA '06.

[24]  James Cheng,et al.  Triangle listing in massive networks and its applications , 2011, KDD.