Synonyms
Glossary
- Benchmarking:
-
Performance evaluation for comparison to the state of the art
- Benchmark Suite:
-
Set of instances used for benchmarking
Definition
Benchmarking refers to a repeatable performance evaluation as a means to compare somebody’s work to the state of the art in the respective field. As an example, benchmarking can compare the computing performance of new and old hardware.
In the context of computing, many different benchmarks of various sorts have been used. A prominent example is the Linpack benchmark of the TOP500 list of the fastest computers in the world, which measures the performance of the hardware by solving a dense linear algebra problem. Different categories of benchmarks include sequential vs. parallel, microbenchmark vs. application, or fixed code vs. informal problem description. See, e.g., Weicker (2002) for a more detailed treatment of hardware evaluation.
When it comes to benchmarking...
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References
Aloise D, Caporossi G, Perron S, Hansen P, Liberti L, Ruiz M (2012) Modularity maximization in networks by variable neighborhood search. In: 10th DIMACS implementation challenge workshop. Georgia Institute of Technology, Atlanta
Arenas A (2009) Network data sets. http://deim.urv.cat/aarenas/data/welcome.htm. Online; accessed 28 Sept 2012
Bader DA, Berry J, Kahan S, Murphy R, Jason Riedy E, Willcock J (2010) Graph 500 benchmark 1 (“search”), version 1.1. Technical report, Graph 500
Bader D, Meyerhenke H, Sanders P, Wagner D (2012) 10th DIMACS implementation challenge. http://www.cc.gatech.edu/dimacs10/. Online; accessed 28 Sept 2012
Barabási A-L, Albert R (1999) Emergence of scaling in random networks. Science 286:509–512
Bauer R, Delling D, Sanders P, Schieferdecker D, Schultes D, Wagner D (2010) Combining hierarchical and goal-directed speed-up techniques for Dijkstra’s algorithm. ACM J Exp Algorithmics 15: 2.3:2.1–2.3:2.31
Berry JW, Hendrickson B, LaViolette RA, Phillips CA (2011) Tolerating the community detection resolution limit with edge weighting. Phys Rev E 83:056119
Bollobás B (1985) Random graphs. Academic, London
Çatalyürek ÜV, Aykanat C (1996) Decomposing irregularly sparse matrices for parallel matrix-vector multiplication. In: Ferreira A, Rolim J, Saad Y, Yang T (eds) Parallel algorithms for irregularly structured problems. Volume 1117 of lecture notes in computer science. Springer, Berlin/Heidelberg, pp 75–86. doi:10.1007/BFb0030098
Çatalyürek ÜV, Kaya K, Langguth J, Uçar B (2012) A divisive clustering technique for maximizing the modularity. In: 10th DIMACS implementation challenge workshop. Georgia Institute of Technology, Atlanta
Chakrabarti D, Zhan Y, Faloutsos C (2004) R-MAT: a recursive model for graph mining. In: Proceedings of the 4th SIAM international conference on data mining (SDM), Orlando, April 2004. SIAM
Davis T (2008) The University of Florida Sparse Matrix Collection. http://www.cise.ufl.edu/research/sparse/matrices. Online; accessed 28 Sept 2012
Fagginger Auer BO, Bisseling RH (2012) Graph coarsening and clustering on the GPU. In: 10th DIMACS implementation challenge workshop. Georgia Institute of Technology, Atlanta
Fortunato S, Barthelemy M (2007) Resolution limit in community detection. Proc Natl Acad Sci 104:36–41
Gilbert H (1959) Random graphs. Ann Math Stat 30(4):1141–1144
Good BH, de Montjoye Y-A, Clauset A (2010) Performance of modularity maximization in practical contexts. Phys Rev E 81:046106
Kannan R, Vempala S, Vetta A (2004) On clusterings: good, bad, spectral. J ACM 51(3):497–515
Karypis G, Kumar V (1999) A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J Sci Comput 20:359–392
Lancichinetti A, Fortunato S (2009) Community detection algorithms: a comparative analysis. Phys Rev E 80(5):056117
Lancichinetti A, Fortunato S (2011) Limits of modularity maximization in community detection. Phys Rev E 84:066122
Lescovec J. Stanford Network Analysis Package (SNAP). http://snap.stanford.edu/index.html. Online; accessed 28 Sept 2012
Newman M () Network data. http://www-personal.umich.edu/mejn/netdata/. Online; accessed 28 Sept 2012
Ovelgönne M, Geyer-Schulz A (2012) An ensemble learning strategy for graph clustering. In: 10th DIMACS implementation challenge workshop. Georgia Institute of Technology, Atlanta
Riedy EJ, Meyerhenke H, Ediger D, Bader DA (2012) Parallel community detection for massive graphs. In: 10th DIMACS implementation challenge workshop. Georgia Institute of Technology, Atlanta
Seshadhri C, Kolda TG, Pinar A (2012) Community structure and scale-free collections of Erdős-Rényi graphs. Phys Rev E 85(5):066122
Soper AJ, Walshaw C, Cross M (2004) A combined evolutionary search and multilevel optimisation approach to graph-partitioning. J Glob Optim 29(2): 225–241
van Dongen SM (2000) Graph clustering by flow simulation. PhD thesis, University of Utrecht
Watts DJ, Strogatz SH (1998) Collective dynamics of “Small-World” networks. Nature 393: 440–442
Weicker R (2002) Benchmarking. In: Calzarossa M, Tucci S (eds) Performance evaluation of complex systems: techniques and tools. Volume 2459 of lecture notes in computer science. Springer, Berlin/Heidelberg, pp 231–242
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Bader, D.A., Meyerhenke, H., Sanders, P., Schulz, C., Kappes, A., Wagner, D. (2014). Benchmarking for Graph Clustering and Partitioning. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6170-8_23
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