Siva Rajamanickam

Siva Rajamanickam

Researcher

Sandia National Laboratories

Siva Rajamanickam is a distinguished member of technical staff at Sandia National Laboratories. His interest is broadly in the areas of high performance computing, specifically in machine learning for science, performance portable algorithms, 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.

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

Kylee Santos, Stan Moore, Tomas Oppelstrup, Amirali Sharifian, Ilya Sharapov, Aidan Thompson, Delyan Z Kalchev, Danny Perez, Robert Schreiber, Scott Pakin, Edgar A Leon, James H Laros III, Michael James, Sivasankaran Rajamanickam
May 2024 arXiv preprint arXiv:2405.07898
Breaking the Molecular Dynamics Timescale Barrier Using a Wafer-Scale System
Lenz Fiedler, Normand Modine, Steve Scmerler, Dayton J Vogel, Gabriel Popoola, Aidan P Thompson, Sivasankaran Rajamanickam, Attila Cangi
June 2023 npj Computational Materials
Predicting electronic structures at any length scale with machine learning
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
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

Jon Roose, Miheer Vaidya, Ponnuswamy Sadayappan, Sivasankaran Rajamanickam (2023). TenSQL: An SQL Database Built on GraphBLAS. 2023 IEEE High Performance extreme Computing Conference (HPEC).

Cite

Michael Eydenberg, Mark Plagge, Sivasankaran Rajamanickam (2023). A Comparison of Spectral and Spatial Graph Convolutional Neural Network Kernels Using GraphSAGE-Sparse. 2023 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW).

Cite

Ichitaro Yamazaki, Alexander Heinlein, Sivasankaran Rajamanickam (2023). An Experimental Study of Two-level Schwarz Domain-Decomposition Preconditioners on GPUs. 2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS).

Cite

Kim Liegeois, Sivasankaran Rajamanickam, Luc Berger-Vergiat (2023). Performance Portable Batched Sparse Linear Solvers. IEEE Transactions on Parallel and Distributed Systems.

Cite

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).

Cite

See all publications

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