Sudharshan Srinivasan

Research

  • Machine Learning Systems and GNN Infrastructure

    This project focuses on designing high-performance computing (HPC) infrastructure and developing a parallel Graph Neural Network (GNN) architecture to efficiently scale and execute scientific GNN workloads.

  • Framework for Recommending Parallel Graph Processing Packages

    Developed a framework that predicts the execution time of parallel graph processing packages for specific graphs based on hardware configurations. Using machine learning models to analyze graph metadata, the framework achieved 97% accuracy. It is now integrated into the easy-parallel-graph system, available here

  • Ranking for Sparse Linear Solvers

    Developed a ranking framework that suggests the best performing solver for a specific sparse linear system.

  • Application Aware Heterogeneous Many-Core Processors

    Implemented a design for Heterogeneous Many-Core Processors (HMCP) that are fine-tuned and customized for specific applications to optimize power, performance, and area.

  • Computational Model of an Analytical Simulator

    A full-scale analytical simulator was designed to give a proof of concept for the proposed Parallel Architecture for application-aware HMCP using C++ and Python.

Publications

  • Sudharshan Srinivasan, A. Khanda, et al. "A Distributed Algorithm for Identifying Strongly Connected Components on Incremental Graphs," IEEE 35th Int'l Symposium on Computer Architecture and High-Performance Computing (SBAC-PAD), 2023.

  • Dhanasekar Sundararaman and Sudharshan Srinivasan. "Twigraph: Discovering and Visualizing Influential Words Between Twitter Profiles." International Conference on Social Informatics. Springer, 2017.

  • Samuel D. Pollard, Sudharshan Srinivasan, and Boyana Norris. "A performance and recommendation system for parallel graph processing implementations: Work-in-progress." ACM/SPEC International Conference on Performance Engineering Companion, Mumbai, India, April 2019. ACM.

  • Sudharshan Srinivasan and Boyana Norris. "A Tiered GNN Architecture for Improved Training of Multi-Agent Reinforcement Learning Systems" (under submission) IEEE RLC 2025.

  • Sudharshan Srinivasan and Boyana Norris. "Thinking Asynchronously for GNN communications on multi-node systems" (under submission) CYBI 2025.