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The enumeration of all maximal cliques in an undirected graph is a fundamental problem arising in several research areas. We consider maximal clique enumeration on sharedmemory, multi-core architectures and introduce an approach consisting entirely of data-parallel operations, in an effort to achieve efficient and portable performance across different architectures. We study the performance of the algorithm via experiments varying over benchmark graphs and architectures. Overall, we observe that our algorithm achieves up to a 33-time speedup and 9-time speedup over state-of-theart distributed and serial algorithms, respectively, for graphs with higher ratios of maximal cliques to total cliques. Further, we attain additional speedups on a GPU architecture, demonstrating the portable performance of our data-parallel design.

We present a new parallel algorithm for probabilistic graphical model optimization. The algorithm relies on data-parallel primitives (DPPs), which provide portable performance over hardware architecture. We evaluate results on CPUs and GPUs for an image segmentation problem. Compared to a serial baseline, we observe runtime speedups of up to 13X (CPU) and 44X (GPU). We also compare our performance to a reference, OpenMP-based algorithm, and find speedups of up to 7X (CPU). arXiv:1809.05018v1 [cs.DC] 13 Sep 2018 2 microct.lbl.gov 3 Cori configuration page: http://www.nersc.gov/users/ computational-systems/cori/configuration/ 4 Edison configuration page: http://www.nersc.gov/users/ computational-systems/edison/configuration/

In this paper, we use a Genetic Algorithm (GA) and MARLEDA—a modern Estimation of Distribution Algorithm (EDA)—to evolve and train several fuzzy rule-based classifiers (FRBCs) to detect patterns of financial statement fraud. We find that both GA and MARLEDA demonstrate a better ability to classify unseen corporate data observations than those of a traditional logistic regression model, and provide validity for detecting financial statement fraud with Evolutionary Algorithms (EAs) and FRBCs. Using ten-fold cross-validation, the GA and MARLEDA yield average training classification accuracy rates of 75.47 percent and 74.26 percent, respectively, and average validation accuracy rates of 63.75 percent and 64.46 percent, respectively.

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