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Benchmark Hypergraphs and Detailed Experimental Results of "k-way Hypergraph Partitioning via n-Level Recursive Bisection"

<p>This dataset contains hypergraphs derived&nbsp;from three benchmark sets: The<br /> ISPD98 VLSI Circuit Benchmark Suite [1], the University of Florida Sparse Matrix Collec-<br /> tion [2] and the international SAT Competition 2014 [3]. From the latter, we randomly selected<br /> 100 instances from the application track and converted them into hypergraphs as follows:<br /> Each boolean variable (and its complement) is mapped to one vertex and each clause constitutes<br /> a net [41]. The Sparse Matrix Collection is organized into 172 groups and each group contains<br /> matrices of different application areas. From each group, we choose one matrix for each appli-<br /> cation area that has between 10 000 and 10 000 000 columns. In case multiple matrices fulfill<br /> our criteria, we randomly select one. In total, we include 192 matrices, which are translated into<br /> hypergraphs using the row-net model, i.e. each row is treated as a net and each column as<br /> a vertex. Empty rows are discarded. Both vertices and nets have unit weight. Together with the<br /> 18 ISPD98 VLSI instances , a total of 310 hypergraphs constitute our benchmark set. 4 Each of<br /> these hypergraphs is partitioned into k &isin; {2, 4, 8, 16, 32, 64, 128} blocks with &epsilon; = 0.03. For each<br /> value of k, a k-way partition is considered to be one test instance, resulting in a total of 2170<br /> instances.</p> <p>See the README for further information on the different files contained in this dataset.</p> <p>[1 ]C. J. Alpert. The ISPD98 Circuit Benchmark Suite. In Proc. of the 1998 Int. Symp. on Physical Design,&nbsp;ISPD &rsquo;98, pages 80&ndash;85, New York, 1998. ACM.<br /> [2] T. A. Davis and Y. Hu. The University of Florida Sparse Matrix Collection. ACM Trans. Math. Softw.,38(1):1:1&ndash;1:25, 2011.<br /> [3] A. Belov, D. Diepold, M. Heule, and M. J&auml;rvisalo. The SAT Competition 2014. http://www.satcompetition.org/2014/, 2014.</p>

ShareScore

40/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship
8
Harmonization
4
Access
20
Reuse readiness
8
Engagement
0

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