Siva Rajamanickam

Siva Rajamanickam


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.

Download my resumé.

  • High Performance Computing
  • Machine Learning for Science
  • Performance Portable Algorithms
  • Codesign of Algorithms and Architectures
  • Combinatorial Scientific Computing
  • PhD in Computer Science and Engineering, 2009

    University of Florida

  • BE in Computer Science and Engineering, 1999

    Madurai Kamaraj University

Recent Publications

(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).


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


(2022). Parallel graph coloring algorithms for distributed GPU environments. Parallel Computing.


(2022). FROSch Preconditioners for Land Ice Simulations of Greenland and Antarctica. SIAM Journal on Scientific Computing.


(2022). Enabling Flexibility for Sparse Tensor Acceleration via Heterogeneity. arXiv preprint arXiv:2201.08916.



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.


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