Galaxia: A Semi-decentralized System for Implementing Secure-Group P2P Networks. - Saurav Muralidharan; Sajin Koroth; Nevil Anto; Rafeeque Pandarachalil (NetCoM, 2009)

Towards making autotuning mainstream. - Protonu Basu; Mary W. Hall; Malik Murtaza Khan 001; Suchit Maindola; Saurav Muralidharan; Shreyas Ramalingam; Axel Rivera; Manu Shantharam; Anand Venkat (Int. J. High Perform. Comput. Appl., 2013)

Nitro: A Framework for Adaptive Code Variant Tuning. - Saurav Muralidharan; Manu Shantharam; Mary W. Hall; Michael Garland; Bryan Catanzaro (IPDPS, 2014)

A collection-oriented programming model for performance portability. - Saurav Muralidharan; Michael Garland; Bryan Catanzaro; Albert Sidelnik; Mary W. Hall (PPoPP, 2015)

Architecture-Adaptive Code Variant Tuning. - Saurav Muralidharan; Amit Roy; Mary W. Hall; Michael Garland; Piyush Rai (ASPLOS, 2016)

Designing a Tunable Nested Data-Parallel Programming System. - Saurav Muralidharan; Michael Garland; Albert Sidelnik; Mary W. Hall (ACM Trans. Archit. Code Optim., 2016)

Abstractions and Strategies for Adaptive Programming. - Saurav Muralidharan (2016)

A Programmable Approach to Model Compression. - Vinu Joseph; Saurav Muralidharan; Animesh Garg; Michael Garland; Ganesh Gopalakrishnan (CoRR, 2019)

Reliable Model Compression via Label-Preservation-Aware Loss Functions. - Vinu Joseph; Shoaib Ahmed Siddiqui; Aditya Bhaskara; Ganesh Gopalakrishnan; Saurav Muralidharan; Michael Garland; Sheraz Ahmed; Andreas Dengel 001 (CoRR, 2020)

A Programmable Approach to Neural Network Compression. - Vinu Joseph; Ganesh Gopalakrishnan; Saurav Muralidharan; Michael Garland; Animesh Garg (IEEE Micro, 2020)

Efficient Sparsely Activated Transformers. - Salar Latifi; Saurav Muralidharan; Michael Garland (CoRR, 2022)

HighLight: Efficient and Flexible DNN Acceleration with Hierarchical Structured Sparsity. - Yannan Nellie Wu; Po-An Tsai; Saurav Muralidharan; Angshuman Parashar; Vivienne Sze; Joel S. Emer (MICRO, 2023)

Uniform Sparsity in Deep Neural Networks. - Saurav Muralidharan (MLSys, 2023)

Understanding the Effect of the Long Tail on Neural Network Compression. - Harvey Dam; Vinu Joseph; Aditya Bhaskara; Ganesh Gopalakrishnan; Saurav Muralidharan; Michael Garland (CoRR, 2023)

The Sparsity Roofline: Understanding the Hardware Limits of Sparse Neural Networks. - Cameron Shinn; Collin McCarthy; Saurav Muralidharan; Muhammad Osama 002; John D. Owens (CoRR, 2023)

Flextron: Many-in-One Flexible Large Language Model. - Ruisi Cai; Saurav Muralidharan; Greg Heinrich; Hongxu Yin; Zhangyang Wang; Jan Kautz; Pavlo Molchanov 001 (ICML, 2024)

MaskLLM: Learnable Semi-Structured Sparsity for Large Language Models. - Gongfan Fang; Hongxu Yin; Saurav Muralidharan; Greg Heinrich; Jeff Pool; Jan Kautz; Pavlo Molchanov 001; Xinchao Wang (NeurIPS, 2024)

Compact Language Models via Pruning and Knowledge Distillation. - Saurav Muralidharan; Sharath Turuvekere Sreenivas; Raviraj Joshi; Marcin Chochowski; Mostofa Patwary; Mohammad Shoeybi; Bryan Catanzaro; Jan Kautz; Pavlo Molchanov 001 (NeurIPS, 2024)

LLM Pruning and Distillation in Practice: The Minitron Approach. - Sharath Turuvekere Sreenivas; Saurav Muralidharan; Raviraj Joshi; Marcin Chochowski; Mostofa Patwary; Mohammad Shoeybi; Bryan Catanzaro; Jan Kautz; Pavlo Molchanov 001 (CoRR, 2024)

EoRA: Training-free Compensation for Compressed LLM with Eigenspace Low-Rank Approximation. - Shih-Yang Liu; Huck Yang; Chein-Yi Wang; Nai Chit Fung; Hongxu Yin; Charbel Sakr; Saurav Muralidharan; Kwang-Ting Cheng; Jan Kautz; Yu-Chiang Frank Wang; Pavlo Molchanov 001; Min-Hung Chen (CoRR, 2024)

LLaMaFlex: Many-in-one LLMs via Generalized Pruning and Weight Sharing. - Ruisi Cai; Saurav Muralidharan; Hongxu Yin; Zhangyang Wang; Jan Kautz; Pavlo Molchanov 001 (ICLR, 2025)

Efficient Hybrid Language Model Compression through Group-Aware SSM Pruning. - Ali Taghibakhshi; Sharath Turuvekere Sreenivas; Saurav Muralidharan; Marcin Chochowski; Yashaswi Karnati; Raviraj Joshi; Ameya Sunil Mahabaleshwarkar; Zijia Chen; Yoshi Suhara; Oluwatobi Olabiyi; Daniel Korzekwa; Mostofa Patwary; et al. (CoRR, 2025)

Small Language Models are the Future of Agentic AI. - Peter Belcak; Greg Heinrich; Shizhe Diao; Yonggan Fu; Xin Dong 009; Saurav Muralidharan; Yingyan Celine Lin; Pavlo Molchanov 001 (CoRR, 2025)