Pratik Nayak

Computer Scientist, Mathematician, Engineer.

Currently

Doctoral Candidate in Applied Mathematics and Computer Science.

Research interests

High Performance Computing, Numerical Linear Algebra, GPU Computing, Asynchronous iterative methods.

Education

Oct 2017 -- present Karlsruhe Institute of Technology, Karlsruhe, Deutschland

  • Doctoral Candidate

August 2015 -- June 2017 Technical University of Delft, Delft, Netherlands

  • Masters (with Honors) in Mechanical Engineering, Specializing in Solid and Fluid Mechanics.

Awards

September 2015 Wind Energy Scholarship, Delft Wind Energy Institute, TU Delft. - Full scholarship for Masters studies

Teaching and Organizational Experience

November 2022 Lead student volunteer, SC22, Dallas Texas, USA.

November 2021 SCALE student (Lead volunteer): part of the Junior Technical Papers committee, SC21, Virtual and St. Louis, Missouri, USA.

April 2021 -- July 2021 Numerical Linear Algebra for High Performance Computing, Instructor and Assistant, Department of Mathematics, Karlsruhe Institute of Technology

October 2020 -- February 2021 Numerical Linear Algebra for High Performance Computing, Teaching Assistant, Department of Mathematics, Karlsruhe Institute of Technology

November 2020 Student volunteer, SC20, Virtual, World

October 2018 -- February 2019 Numerical Linear Algebra for High Performance Computing, Teaching Assistant, Department of Mathematics, Karlsruhe Institute of Technology

July 2018 Student volunteer, PASC18, Basel, Switzerland

January 2018 HPC for Tomorrow- Scientific Computing short course, Teaching Assistant, National Taiwan University, Taiwan

October 2017 -- February 2018 Numerical Linear Algebra for High Performance Computing, Teaching Assistant, Department of Mathematics, Karlsruhe Institute of Technology

Publications

2022

  1. Batched sparse iterative solvers on GPU for the collision operator for fusion plasma simulations, International Parallel and Distributed Processing Symposium (IPDPS), Lyon, May-June 2022 (accepted)

2021

  1. Ginkgo: A Modern Linear Operator Algebra Framework for High Per- formance Computing, December 2021, ACM Transactions on Mathematical Software (ACM-TOMS) doi:10.1145/3480935

  2. Advances in Mixed Precision Algorithms: 2021 Edition, August, 2021, Sandia National laboratory technical report. SAND2021-10227R

  3. Batched Sparse Iterative Solvers for Computational Chemistry Simulations on GPUs, 12th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA), November 2021, 10.1109/ScalA54577.2021.00010

  4. A collaborative peer review process for grading coding assignments in coursework, July 2021, ICCS 2021, [paper]

2020

  1. Two stage asynchronous iterative solvers for multi-GPU clusters , Nov 2020, ScalA Workshop, Supercomputing (SC20), [paper]

  2. Ginkgo: A high performance numerical linear algebra library, August 2020, The Journal of Open Source Software, doi: 10.21105/joss.02260

  3. Evaluating asynchronous Schwarz solvers for Exascale, Aug 2020, The International Journal of High Performance Computing [paper link]. Alternative: arXiv:2003.05361, cs.DC,

  4. A Survey of Numerical Methods Utilizing Mixed Precision Arithmetic, July 2020, arXiv:2007.06674 cs.MS

  5. Ginkgo: A Modern Linear Operator Algebra Framework for High Performance Computing, July 2020, arXiv:2006.16852, cs.MS

  6. Load-balancing Sparse Matrix Vector Product Kernels on GPUs, ACM-TOPS, March 2020, doi: 10.1145/3387354

2019

  1. Towards Continuous Benchmarking: An Automated Performance Evaluation Framework for High Performance Software, Platform for Advanced Scientific Computing (PASC) Conference, June 12-14, 2019, Zurich , doi: 10.1145/3324989.3325719

Talks

  1. A collaborative peer review process for grading coding assignments in coursework, July 2021, ICCS 2021, [slides]

  2. Two stage asynchronous iterative solvers for multi-GPU clusters , Nov 2020, ScalA Workshop, Supercomputing (SC20) , [slides]

  3. Evaluating asynchronous Schwarz solvers for Exascale, PACO19 conference, Max Planck Institute-Magdeburg, 5-6 November 2019. [slides]

  4. Using iterative methods for local solves in asynchronous Schwarz methods, University of Tennessee, Knoxville - Innovative Computing Laboratory, Lunch talks [slides].