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CuPC: CUDA-Based parallel PC algorithm for causal structure learning on GPU

Zarebavani, B ; Sharif University of Technology | 2020

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  1. Type of Document: Article
  2. DOI: 10.1109/TPDS.2019.2939126
  3. Publisher: IEEE Computer Society , 2020
  4. Abstract:
  5. The main goal in many fields in the empirical sciences is to discover causal relationships among a set of variables from observational data. PC algorithm is one of the promising solutions to learn underlying causal structure by performing a number of conditional independence tests. In this paper, we propose a novel GPU-based parallel algorithm, called cuPC, to execute an order-independent version of PC. The proposed solution has two variants, cuPC-E and cuPC-S, which parallelize PC in two different ways for multivariate normal distribution. Experimental results show the scalability of the proposed algorithms with respect to the number of variables, the number of samples, and different graph densities. For instance, in one of the most challenging datasets, the runtime is reduced from more than 11 hours to about 4 seconds. On average, cuPC-E and cuPC-S achieve 500X and 1300X speedup, respectively, compared to serial implementation on CPU. © 1990-2012 IEEE
  6. Keywords:
  7. Bayesian networks ; Causal discovery ; CUDA ; GPU ; Machine learning ; Parallel processing ; PC algorithm ; Copper compounds ; Graphics processing unit ; Learning algorithms ; Learning systems ; Normal distribution ; Causal relationships ; Causal structure learning ; Conditional independence tests ; Multi-variate normal distributions ; Observational data
  8. Source: IEEE Transactions on Parallel and Distributed Systems ; Volume 31, Issue 3 , 2020 , Pages 530-542
  9. URL: https://ieeexplore.ieee.org/abstract/document/8823064