Parallelization of module network structure learning and performance tuning on SMP

• Main Authors: Hongshan Jiang, Chunrong Lai, Wenguang Chen, Yurong Chen, Wei Hu, Weimin Zheng, Yimin Zhang
• Format: Conference Proceeding
• Language: English
• Subjects: Bayesian methods; Bioinformatics; Computer science; Multiprocessing systems; Partitioning algorithms; Scalability; Speech processing; Stochastic processes; Text mining; Yarn
• ISSN: 1530-2075
• DOI: 10.1109/IPDPS.2006.1639610

As an extension of Bayesian network, module network is an appropriate model for inferring causal network of a mass of variables from insufficient evidences. However learning such a model is still a time-consuming process. In this paper, we propose a parallel implementation of module network learning algorithm using OpenMP. We propose a static task partitioning strategy which distributes sub-search-spaces over worker threads to get the tradeoff between load-balance and software-cache-contention. To overcome performance penalties derived from shared-memory contention, we adopt several optimization techniques such as memory pre-allocation, memory alignment and static function usage. These optimizations have different patterns of influence on the sequential performance and the parallel speedup. Experiments validate the effectiveness of these optimizations. For a 2,200 nodes dataset, they enhance the parallel speedup up to 88%, together with a 2X sequential performance improvement. With resource contentions reduced, workload imbalance becomes the main hurdle to parallel scalability and the program behaviors more stable in various platforms.