Siva Rajamanickam

Siva Rajamanickam

Researcher

Sandia National Laboratories

Siva Rajamanickam is a principal member of technical staff at Sandia National Laboratories. His interest is broadly in the areas of high performance computing, specifically in performance portable algorithms, machine learning for science, codesign of algorithms and architectures and combinatorial scientific computing. Most of his works are in the intersection of these areas where interesting opportunities lie to solve problems that are of importance to computational science use cases.

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Interests
  • High Performance Computing
  • Machine Learning for Science
  • Performance Portable Algorithms
  • Codesign of Algorithms and Architectures
  • Combinatorial Scientific Computing
Education

  • PhD in Computer Science and Engineering, 2009

    University of Florida

  • BE in Computer Science and Engineering, 1999

    Madurai Kamaraj University

Featured Publications

Lenz Fiedler, Nils Hoffmann, Parvez Mohammed, Gabriel A. Popoola, Tamar Yovell, Vladyslav Oles, J. Austin Ellis, Sivasankaran Rajamanickam, Attila Cangi
October 2022 Machine Learning: Science and Technology
Training-free hyperparameter optimization of neural networks for electronic structures in matter
Raveesh Garg, Eric Qin, Francisco Munoz-Martinez, Robert Guirado, Akshay Jain, Sergi Abadal, Jose Abellan, Manuel Acacio, Eduard Alarcon, Sivasankaran Rajamanickam, Tushar Krishna
June 2022 2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
Understanding the design-space of sparse/dense multiphase GNN dataflows on spatial accelerators
James Fox, Bo Zhao, Beatriz Gonzalez del Rio, Sivasankaran Rajamanickam, Rampi Ramprasad, Le Song
July 2022 2022 International Joint Conference on Neural Networks (IJCNN), 2022
Concentric Spherical Neural Network for 3D Representation Learning
J Austin Ellis, Lenz Fiedler, Gabriel A Popoola, Normand A Modine, J Adam Stephens, Aidan P Thompson, Attila Cangi, Sivasankaran Rajamanickam
January 2021 Physical Review B
Accelerating finite-temperature Kohn-Sham density functional theory with deep neural networks
Christian R Trott, Damien Lebrun-Grandie, Daniel Arndt, Jan Ciesko, Vinh Dang, Nathan Ellingwood, Rahulkumar Gayatri, Evan Harvey, Daisy S Hollman, Dan Ibanez, others
January 2021 IEEE Transactions on Parallel and Distributed Systems
Kokkos 3: Programming model extensions for the exascale era
Christian R Trott, Luc Berger-Vergiat, David Poliakoff, Sivasankaran Rajamanickam, Damien Lebrun-Grandie, Jonathan Madsen, Nader Al Awar, Milos Gligoric, Galen Shipman, Geoff Womeldorff
January 2021 Computing in Science and Engineering
The Kokkos EcoSystem: Comprehensive Performance Portability For High Performance Computing

Recent Publications

Ichitaro Yamazaki, Christian Glusa, Jennifer Loe, Piotr Luszczek, Sivasankaran Rajamanickam, Jack Dongarra (2022). High-Performance GMRES Multi-Precision Benchmark: Design, Performance, and Challenges. 2022 IEEE/ACM International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS).

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Brian Kelley, Sivasankaran Rajamanickam (2022). Parallel, Portable Algorithms for Distance-2 Maximal Independent Set and Graph Coarsening. 2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS).

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Ian Bogle, George M Slota, Erik G Boman, Karen Devine, Sivasankaran Rajamanickam (2022). Parallel graph coloring algorithms for distributed GPU environments. Parallel Computing.

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Alexander Heinlein, Mauro Perego, Sivasankaran Rajamanickam (2022). FROSch Preconditioners for Land Ice Simulations of Greenland and Antarctica. SIAM Journal on Scientific Computing.

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Eric Qin, Raveesh Garg, Abhimanyu Bambhaniya, Michael Pellauer, Angshuman Parashar, Sivasankaran Rajamanickam, Cong Hao, Tushar Krishna (2022). Enabling Flexibility for Sparse Tensor Acceleration via Heterogeneity. arXiv preprint arXiv:2201.08916.

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Recent & Upcoming Talks

Batched Linear Solvers in Kokkos Kernels
Mar 2, 2023 12:00 AM Amsterdam, Netherlands.
Scientific Machine Learning and Data flow acceleration: ASCR HQ Update
Mar 1, 2023 12:00 AM Albuquerque
Recent Experiences on Accelerating Machine Learning Workloads on SambaNova Systems
Feb 3, 2023 12:00 AM Atlanta
Kokkos Kernels: Then and Now
Jan 19, 2023 12:00 AM Houston
Accelerating Selected DOE Machine Learning Workloads on SambaNova Systems
Nov 15, 2022 12:00 AM Dallas
See all events

Projects

Instead of listing all the funded projects that I lead, this is a summary of all the areas I am interested in. All my projects fall into one or more of these areas.

Codesign of Algorithms and Architectures
Combinatorial Scientific Computing
Performance Portability
Machine Learning for Science

Software

Instead of listing all the software projects that I am involved with, this is a summary of large frameworks that I contribute to.

Kokkos Kernels
Mala
Trilinos

Contact