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ACM Transactions on Mathematical Software (TOMS)
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A guide to simulation (2nd ed.)
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Communications of the ACM
Randomized algorithms
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
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Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
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OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
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GH '06 Proceedings of the 21st ACM SIGGRAPH/EUROGRAPHICS symposium on Graphics hardware
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ACM Transactions on Knowledge Discovery from Data (TKDD)
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ACM Transactions on Algorithms (TALG)
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Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
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COCOON'99 Proceedings of the 5th annual international conference on Computing and combinatorics
A many threaded CUDA interpreter for genetic programming
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
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DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I
Scalable parallel minimum spanning forest computation
Proceedings of the 17th ACM SIGPLAN symposium on Principles and Practice of Parallel Programming
Sampling connected induced subgraphs uniformly at random
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
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Today, several database applications call for the generation of random graphs. A fundamental, versatile random graph model adopted for that purpose is the Erdős-Rényi Γv,p model. This model can be used for directed, undirected, and multipartite graphs, with and without self-loops; it induces algorithms for both graph generation and sampling, hence is useful not only in applications necessitating the generation of random structures but also for simulation, sampling and in randomized algorithms. However, the commonly advocated algorithm for random graph generation under this model performs poorly when generating large graphs, and fails to make use of the parallel processing capabilities of modern hardware. In this paper, we propose PPreZER, an alternative, data parallel algorithm for random graph generation under the Erdős-Rényi model, designed and implemented in a graphics processing unit (GPU). We are led to this chief contribution of ours via a succession of seven intermediary algorithms, both sequential and parallel. Our extensive experimental study shows an average speedup of 19 for PPreZER with respect to the baseline algorithm.