Proceedings of the Workshop on Machine Learning in High-Performance Computing Environments最新文献

Optimizing deep learning hyper-parameters through an evolutionary algorithm 通过进化算法优化深度学习超参数

Proceedings of the Workshop on Machine Learning in High-Performance Computing Environments Pub Date : 2015-11-15 DOI: 10.1145/2834892.2834896

Steven R. Young, Derek C. Rose, T. Karnowski, Seung-Hwan Lim, R. Patton

引用次数: 339

LBANN: livermore big artificial neural network HPC toolkit LBANN: livermore大型人工神经网络HPC工具包

Proceedings of the Workshop on Machine Learning in High-Performance Computing Environments Pub Date : 2015-11-15 DOI: 10.1145/2834892.2834897

B. V. Essen, Hyojin Kim, R. Pearce, K. Boakye, Barry Y. Chen

引用次数: 68

HPDBSCAN: highly parallel DBSCAN HPDBSCAN:高度并行的DBSCAN

Proceedings of the Workshop on Machine Learning in High-Performance Computing Environments Pub Date : 2015-11-15 DOI: 10.1145/2834892.2834894

Markus Goetz, Christian Bodenstein, M. Riedel

{"title":"HPDBSCAN: highly parallel DBSCAN","authors":"Markus Goetz, Christian Bodenstein, M. Riedel","doi":"10.1145/2834892.2834894","DOIUrl":"https://doi.org/10.1145/2834892.2834894","url":null,"abstract":"Clustering algorithms in the field of data-mining are used to aggregate similar objects into common groups. One of the best-known of these algorithms is called DBSCAN. Its distinct design enables the search for an apriori unknown number of arbitrarily shaped clusters, and at the same time allows to filter out noise. Due to its sequential formulation, the parallelization of DBSCAN renders a challenge. In this paper we present a new parallel approach which we call HPDBSCAN. It employs three major techniques in order to break the sequentiality, empower workload-balancing as well as speed up neighborhood searches in distributed parallel processing environments i) a computation split heuristic for domain decomposition, ii) a data index preprocessing step and iii) a rule-based cluster merging scheme. As a proof-of-concept we implemented HPDBSCAN as an OpenMP/MPI hybrid application. Using real-world data sets, such as a point cloud from the old town of Bremen, Germany, we demonstrate that our implementation is able to achieve a significant speed-up and scale-up in common HPC setups. Moreover, we compare our approach with previous attempts to parallelize DBSCAN showing an order of magnitude improvement in terms of computation time and memory consumption.","PeriodicalId":307997,"journal":{"name":"Proceedings of the Workshop on Machine Learning in High-Performance Computing Environments","volume":"62 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116252715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 53

Dynamic adaptive neural network arrays: a neuromorphic architecture 动态自适应神经网络阵列:一种神经形态结构

Proceedings of the Workshop on Machine Learning in High-Performance Computing Environments Pub Date : 2015-11-15 DOI: 10.1145/2834892.2834895

Catherine D. Schuman, A. Disney, J. Reynolds

引用次数: 14

Asynchronous parallel stochastic gradient descent: a numeric core for scalable distributed machine learning algorithms 异步并行随机梯度下降:可扩展分布式机器学习算法的数值核心

Proceedings of the Workshop on Machine Learning in High-Performance Computing Environments Pub Date : 2015-05-19 DOI: 10.1145/2834892.2834893

J. Keuper, F. Pfreundt

引用次数: 38

Proceedings of the Workshop on Machine Learning in High-Performance Computing Environments 高性能计算环境中的机器学习研讨会论文集

Proceedings of the Workshop on Machine Learning in High-Performance Computing Environments Pub Date : 1900-01-01 DOI: 10.1145/2834892

引用次数: 20