Exploring the Use of Dataflow Architectures for Graph Neural Network Workloads. - Ryien Hosseini; Filippo Simini; Venkatram Vishwanath; Ramakrishnan Sivakumar; Sanjif Shanmugavelu; Zhengyu Chen; Lev Zlotnik; Mingran Wang; Philip Colangelo; Andrew Deng; Philip Lassen; Shukur Pathan (ISC Workshops, 2023)

WActiGrad: Structured Pruning for Efficient Finetuning and Inference of Large Language Models on AI Accelerators. - Krishna Teja Chitty-Venkata; Varuni Katti Sastry; Murali Emani; Venkatram Vishwanath; Sanjif Shanmugavelu; Sylvia Howland (Euro-Par (2), 2024)

Toward a Holistic Performance Evaluation of Large Language Models Across Diverse AI Accelerators. - Murali Emani; Sam Foreman; Varuni Sastry; Zhen Xie; Siddhisanket Raskar; William Arnold; Rajeev Thakur; Venkatram Vishwanath; Michael E. Papka; Sanjif Shanmugavelu; Darshan Gandhi; Hengyu Zhao; et al. (IPDPS (Workshops), 2024)

Impacts of floating-point non-associativity on reproducibility for HPC and deep learning applications. - Sanjif Shanmugavelu; Mathieu Taillefumier; Christopher Culver; Oscar R. Hernandez; Mark Coletti; Ada Sedova (SC Workshops, 2024)

Scientific Computing with Large Language Models. - Christopher Culver; Peter Hicks; Mihailo Milenkovic; Sanjif Shanmugavelu; Tobias Becker (CoRR, 2024)

Robustness of Deep Learning Classification to Adversarial Input on GPUs: Asynchronous Parallel Accumulation Is a Source of Vulnerability. - Sanjif Shanmugavelu; Mathieu Taillefumier; Christopher Culver; Vijay Ganesh 001; Oscar R. Hernandez; Ada Sedova (Euro-Par (2), 2025)

Pruning as a Defense: Reducing Memorization in Large Language Models. - Mansi Gupta; Nikhar Waghela; Sarthak Gupta; Shourya Goel; Sanjif Shanmugavelu (CoRR, 2025)