News :

7/2017 : Our paper on What does fault tolerant Deep Learning need from MPI? is accepted for publication at EuroMPI/USA'17.!!

5/2017 : I am appointed as Team Lead for Scalable Machine Learning at PNNL.

4/2017 : Our open source release of MaTEx-TensorFlow is now available at MaTEx github page. Kudos to the MaTEx team members!!

3/2017 : Our paper on ScalaFSM: Enabling Scalability-Sensitive Speculative Parallelization for FSM Computations is accepted by ICS'17!!.

2/2017 : Our paper on Deep Learning on Computational Chemistry is accepted by JCC'17!!.

2/2017 : Our paper on Comparing NVIDIA DGX-1/Pascal and Intel Knights Landing on Deep Learning Workloads is accepted by ParLearning'17!!.

1/2017 : Our paper on Generating Performance Models for Irregular Applications is accepted by IPDPS'17!!.

11/2016 : Our proposal on xGA: Global Arrays on Extreme Scale Architectures is accepted by Exascale Computing Program (ECP).

10/2016 : Our paper on Adaptive Neuron Apoptosis for Accelerating Deep Learning on Large Scale Systems is accepted at IEEE Conference on BigData'16.

9/2016 : Our research on Convergence of Machine Learning and Deep Learning for HPC Modeling and Simulation is funded by Advanced Scientific Computing Research (ASCR)!!.

9/2016 : Our paper on Fault Tolerant Frequent Pattern Mining is accepted at HiPC'16.

9/2016 : Our proposal on Learning Control on Building Systems is accepted at Control of Complex Systems Initiative (CCSI).

7/2016 : We have received Oak Ridge Director's Discretionary Award for conducting research on Extreme Scale Deep Learning algorithms with MaTEx.

5/2016 : Our paper on Fault Tolerant Support Vector Machines is accepted at ICPP'16.

4/2016 : I am serving as a PC member for NAS Conference and reviewer for Computer and TPDS Jouranals.

3/2016 : We released our MaTEx with Distributed TensorFlow using MPI and a paper -- Distributed TensorFlow with MPI.

1/2016 : I am serving as a co-editor on a Parallel Computing (ParCo) special issue

12/2015 : A paper on Application Fault Modeling using Machine Learning accepted in IPDPS'16.

11/2015: Featured Presentation on Extreme Scale Machine Learning Research at DOE Booth @ SC'15.

11/2015: Invited Presentation on role of Interconnects in Machine Learning at Mellanox Booth @ SC'15.

11/2015: Akshay Venkatesh (Summer student - 2014) presented best student paper nominee @ SC'15. Kudos!

10/2015: Invited Presentation on Global Arrays at Japan LENS workshop

10/2015: Presented a PNNL wide talk on What can Large Scale Machine Learning do for you?

9/2015: Paper Presentation in Cluster'15 on Extreme Scale Support Vector Machines

9/2015: Paper Presentation in Cluster'15 on Work Stealing based Frequent Pattern Mining

8/2015: Invited Presentation in MUG'15 on role of MPI in Large Scale Machine Learning

7/2015: Joint work with OSU on Machine Learning accepted for publication in OpenSHMEM Workshop

7/2015: Our SC'15 paper is nominated for best student paper!!

Contact :

  • abhinav (DOT) vishnu (AT) pnnl (DOT) gov
  • Phone: 509-372-4794
  • Mailing address:
  • PO Box 999, MSIN J4-30
  • Richland, WA, 99352
Last update: 10/2016