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Reference | Publication | Mathematics and Computer Science Division

Machine Learning Publications

Publications

  • S. Sreepathi, J. Kumar, R. T. Mills, Forrest M. Hoffman, V. Sripathi, W. W. Hargrove, 2017. "Parallel Multivariate Spatio-Temporal Clustering of Large Ecological Datasets on Hybrid Supercomputers" https://www.climatemodeling.org/~rmills/pubs/Sreepathi_IEEECluster_2017…. Proceedings of IEEE Cluster 2017. doi: 10.1109/CLUSTER.2017.88.
  • R. T. Mills, V. Sripathi, J. Kumar, S. Sreepathi, F. M. Hargrove, W. W. Hargrove, 2018. "Parallel k-means Clustering of Geospatial Data Sets Using Manycore CPU Architectures" https://www.climatemodeling.org/~rmills/pubs/Mills_DMESS2018_20181117.p…. Eighth Workshop on Data Mining in Earth System Sciences (DMESS 2018), Proceedings of the IEEE International Conference on Data Mining (ICDM 2018) Workshops.
  • A. Sanaullah, M. Herbordt, C. Yang, Y. Alexeev, K. Yoshii, "Real-Time Data Analysis for Medical Diagnosis using FPGA Accelerated Neural Networks", BMC special issue
  • A. Sanaullah, C. Yang, M. Herbordt, Y. Alexeev, K. Yoshii, "Application Aware Tuning of Reconfigurable Multi-Layer Perceptiron Architectures", 2018 IEEE High Performance Extreme Computing Conference (HPEC)
  • Y. Luo, X. Wang, S. Ogrenci-Memik, G. Memik, K. Yoshii, P. Beckman, "Minimizing Thermal Variation in Heterogeneous HPC Systems with FPGA Nodes", 2018 IEEE International Conference on Computer Design (ICCD), Florida, Oct 10
  • A. Sanaullah, M. Herbordt, C. Yang, Y. Alexeev, K. Yoshi, "Boosting Curative Surgery Success Rates with FPGAs", Computational Approaches for Cancer (CAFCW17) at SC17
  • A. Sanaullah, C. Yang, Y. Alexeev, K. Yoshii, M.C. Herbordt. "TRIP: An Ultra-Low Latency TeraOps/s  Reconfigureable Inference Processor for Multi-Layer Perceptrons." Poster presented at 2017 International Conference for High Performance Computing, Networking, Storage and Analysis, Denver, CO., US, November 12, 2017-November 17, 2017
  • Sunwoo Lee, Ankit Agrawal, Prasanna Balaprakash, Alok Choudhary, Wei-keng Liao, Communication-efficient parallelization strategy for deep convolutional neural network training, Workshop: 4th Workshop on Machine Learning in HPC Environments)- SC18
  • Xin Liang, Sheng Di, Sihuan Li, Dingwen Tao, Zizhong Chen, Franck Cappello, Exploring Best Lossy Compression Strategy by Combining SZ with Spatiotemporal Decimation, Workshop: Machine Learning in HPC Environments - SC18
  • Xin-Chuan Wu, Sheng Di, Franck Cappello, Hal Finkel, Yuri Alexeev, Frederic T. Chong, Amplitude-Aware Lossy Compression for Quantum Circuit Simulation, Workshop: 4th International Workshop on Data Reduction for Big Scientific Data, DRBSD-4 - SC18
  • Preeti Malakar, Prasanna Balaprakash, Venkatram Vishwanath, Vitali Morozov, Kalyan Kumaran, Benchmarking Machine Learning Methods for Performance Modeling of Scientific Application – (Workshop: 9th IEEE International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems. PMBS’18)
  • Z Liu, P Balaprakash, R Kettimuthu, I Foster, Wide Area Data Transfer,in 26th ACM International Symposium on High-Performance Parallel and Distributed Computing, Washington, DC, July 2017
  • S Sahoo, TA Russo, J Elliott, I Foster, Machine learning algorithms for modeling groundwater level changes in agricultural regions of the US, Water Resources Research, 2017
  • K Zhang, A Guliani, S Ogrenci-Memik, G Memik, K Yoshii, R Sankaran, P. Beckman, Machine Learning-Based Temperature Prediction for Runtime Thermal Management across System Components, IEEE Transactions on Parallel and Distributed Systems, 2017
  • P-L Guhur, H Zhang, T Peterka, E Constantinescu, F Cappello, Lightweight and Accurate Silent Data Corruption Detection in Ordinary Differential Equation Solvers, in EuroPar 2016
  • P Balaprakash, A Tiwari, SM Wild, L Carrington, PD Hovland, AutoMOMML: Automatic Multi-objective Modeling with Machine Learning, International Conference on High Performance Computing, 2016,  pp. 219-239
  • F Isaila, P Balaprakash, SM Wild, D Kimpe, R Latham, R Ross, P Hovland, Collective I/O tuning using analytical and machine learning models, in IEEE International Conference on Cluster Computing (CLUSTER), 2015, pp. 128-137
  • P Balaprakash, Y Alexeev, SA Mickelson, S Leyffer, R Jacob, A Craig, Machine-learning-based load balancing for Community Ice CodE component in CESM, in International Conference on High Performance Computing for Computational Science, 2014, pp. 79-91
  • P Balaprakash, Y Alexeev, SA Mickelson, S Leyffer, RL Jacob, AP Craig, Machine learning based load-balancing for the cesm climate modeling package, in Proc. VECPAR 2014
  • O Subasi, S Di, P Balaprakash, O Unsai, J Labaarta, A Cristal, S Krishnamoorthy, F. Cappello,  MACORD: Online Adaptive Machine Learning Framework for Silent Error Detection, in CLUSTER 2017
  • A. Guliani, K. Zhang, S. Ogrenci-Mernik, G. Mernik, K. Yoshii, R. Sakaran, and P. Beckman, Machine learning-based temperature prediction for runtime thermal management across system components, IEEE Transactions on Parallel and Distributed Systems, July 2017, DOI Bookmark: http://doi.ieeecomputersociety.org/

Presentations 

  • Justin M. Wozniak, Rajeev Jain, Prasanna Balaprakash, Jonathan Ozik, Nicholson Collier, John Bauer, Fangfang Xia, Thomas Brettin, Rick Stevens, Jamaludin Mohd-Yusof, Cristina, Garcia Cardona, Brian Van Essen, and Matthew Baughman, “CANDLE/Supervisor: A workflow framework for machine learning applied to cancer research,” SC17 Workshop: Computational Approaches for Cancer

Posters

Preprints

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