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Research Results

This page will contain presentations, publications, and reports created by the Deep Learning MRI.



  • Volodymyr Kindratenko, Dawei Mu, Yan Zhan, John Maloney, Sayed Hadi Hashemi, Benjamin Rabe, Ke Xu, Roy Campbell, Jian Peng, and William Gropp. 2020. HAL: Computer System for Scalable Deep Learning. InPractice and Experience in Advanced Research Computing (PEARC ’20), July 26–30, 2020, Portland, OR, USA. ACM, New York, NY, USA, 15 pages.
  • Venkatakrishnan, Ramshankar, Ashish Misra, and Volodymyr Kindratenko. “High-Level Synthesis-Based Approach for Accelerating Scientific Codes on FPGAs.” Computing in Science & Engineering 22.4 (2020): 104-109.
  • Misra, Ashish, and Volodymyr Kindratenko. “HLS-Based Acceleration Framework for Deep Convolutional Neural Networks.” International Symposium on Applied Reconfigurable Computing. Springer, Cham, 2020.
  • S. Hashemi, P. Rausch, B. Rabe, K. Chou, S. Liu, V. Kindratenko, R. Campbell, “tensorflow-tracing: A Performance Tuning Framework for Production,” In Proc. 2019 USENIX Conference on Operational Machine Learning (OpML’19), 2019.
  • S. H. Hashemi, S. Abdu Jyothi, R. H. Campbell, “TicTac: Improving Distributed Deep Learning With Communication Scheduling,” SysML Conference 2019.
  • S. H. Hashemi, S. Abdu Jyothi, R. H. Campbell, “On Importance of Execution Ordering in Graph-Based Distributed Machine Learning Systems,” SysML Conference 2018.


  • V. Kindratenko, “POWER9 AI Cluster at NCSA” University Power Systems HPC/AI User Meeting, December 21, 2019, SC19 – Denver, CO.
  • S. H. Hashemi, S. Abdu Jyothi, and R. H. Campbell, “Network Efficiency through Model-Awareness in Distributed Machine Learning Systems,” NSDI ’18, Seattle, WA.
  • S. H. Hashemi and R. H. Campbell, “Making a Case for Timed RPCs in Iterative Systems,” OSDI ’18, San Diego, CA.
  • S. H. Hashemi, B. Rabe, V. Kindratenko, “Building a Scalable Deep Learning Platform,” University Power Systems HPC/AI User Meeting, December 15, 2018, SC18 – Dallas, TX.