Interests

Approximate Computing on Combinatorial Algorithms

• Designed approximate sorting algorithms with constraints and provide quality metrics to measure the tolerance in the output. Used in packet sorting protocols in software defined networks for faster classification.

• Modeled parallel approximate sorting algorithms for optimized performance.

• Optimized binary and range search algorithms to be performed in an almost sorted array with complexity comparable to normal search.

• Pattern recognition and sequence analysis algorithms design for a given array that aids the approximate sorting algorithm. Optimizing distributed sorting algorithms by aiding to load balancing.

• Design and analysis of distributed and external sorting algorithms for approximate computing.

• Investigating on graph algorithms (minimum spanning tree, shortest path) for approximate processing.

Graph algorithms on Compressed Structures

• Formulating multiplication and factorization algorithms to process large graphs (matrices/tensors) in its compressed format without decompression. The CSR/CBT structure used here stores extremely large social media graphs.

• Performing these algorithms in a multilevel parallel paradigm on GPGPUs.

Publications

• A. Narasimhan, S. Radhakrishnan, and C. R. Subramanian, Approximate Sorting and Sequence Analysis, 18th International Conference on Fundamentals of Computer Science, 2022 Accepted, Presented in Las Vegas, USA. 

• A. Narasimhan, S. Radhakrishnan, and C. R. Subramanian, On Searching an Approximately Sorted Array, 21st International Conference on Information and Knowledge Engineering, 2022 Accepted, Presented in Las Vegas, USA. 

• A. Narasimhan, S. Radhakrishnan, M. Atiquzzaman and C. R. Subramanian, "High-Speed Packet Classification: A Case for Approximate Sorting," GLOBECOM 2022 - 2022 IEEE Global Communications Conference, Rio de Janeiro, Brazil, 2022, pp. 5765-5770, doi: 10.1109/GLOBECOM48099.2022.10001397.

• S. G. Krishna, Sridhar Radhakrishnan, A. Narasimhan, and R. Veras, On Tensor - Tensor Multiplication and Compression, 28th International Conference on Parallel and Distributed Processing Techniques and Applications, 2022 Accepted.

• S. G. Krishna, A. Narasimhan, S. Radhakrishnan and R. Veras, On Large-Scale Matrix-Matrix Multiplication On Compressed Structures, 2021 IEEE International Conference on Big Data (Big Data), 2021, pp. 2976-2985, doi:10.1109/BigData52589.2021.9671829.