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Reference | Publication | Mathematics and Computer Science Division

Machine Learning 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"…. 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"…. 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:


  • 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




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