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ORCIDSaurav Muralidharan
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Books and Theses
- 2016
[b1]Saurav Muralidharan:
Abstractions and Strategies for Adaptive Programming. University of Utah, USA, 2016
Journal Articles
- 2020
- [j3]Vinu Joseph, Ganesh Gopalakrishnan, Saurav Muralidharan, Michael Garland, Animesh Garg:
A Programmable Approach to Neural Network Compression. IEEE Micro 40(5): 17-25 (2020) - 2016
- [j2]Saurav Muralidharan, Michael Garland, Albert Sidelnik, Mary W. Hall
:
Designing a Tunable Nested Data-Parallel Programming System. ACM Trans. Archit. Code Optim. 13(4): 47:1-47:24 (2016) - 2013
- [j1]Protonu Basu, Mary W. Hall, Malik Murtaza Khan, Suchit Maindola, Saurav Muralidharan, Shreyas Ramalingam, Axel Rivera, Manu Shantharam, Anand Venkat:
Towards making autotuning mainstream. Int. J. High Perform. Comput. Appl. 27(4): 379-393 (2013)
Conference and Workshop Papers
- 2024
- [c7]Ruisi Cai, Saurav Muralidharan, Greg Heinrich, Hongxu Yin, Zhangyang Wang, Jan Kautz, Pavlo Molchanov:
Flextron: Many-in-One Flexible Large Language Model. ICML 2024 - 2023
- [c6]Yannan Nellie Wu, Po-An Tsai, Saurav Muralidharan, Angshuman Parashar, Vivienne Sze, Joel S. Emer:
HighLight: Efficient and Flexible DNN Acceleration with Hierarchical Structured Sparsity. MICRO 2023: 1106-1120 - [c5]Saurav Muralidharan:
Uniform Sparsity in Deep Neural Networks. MLSys 2023 - 2016
- [c4]Saurav Muralidharan, Amit Roy, Mary W. Hall, Michael Garland, Piyush Rai:
Architecture-Adaptive Code Variant Tuning. ASPLOS 2016: 325-338 - 2015
- [c3]Saurav Muralidharan, Michael Garland, Bryan Catanzaro, Albert Sidelnik, Mary W. Hall:
A collection-oriented programming model for performance portability. PPoPP 2015: 263-264 - 2014
- [c2]Saurav Muralidharan, Manu Shantharam, Mary W. Hall, Michael Garland, Bryan Catanzaro:
Nitro: A Framework for Adaptive Code Variant Tuning. IPDPS 2014: 501-512 - 2009
- [c1]Saurav Muralidharan, Sajin Koroth, Nevil Anto, Rafeeque Pandarachalil:
Galaxia: A Semi-decentralized System for Implementing Secure-Group P2P Networks. NetCoM 2009: 289-294
Informal and Other Publications
- 2024
- [i10]Ruisi Cai, Saurav Muralidharan, Greg Heinrich, Hongxu Yin, Zhangyang Wang, Jan Kautz, Pavlo Molchanov:
Flextron: Many-in-One Flexible Large Language Model. CoRR abs/2406.10260 (2024) - [i9]Saurav Muralidharan, Sharath Turuvekere Sreenivas, Raviraj Joshi, Marcin Chochowski, Mostofa Patwary, Mohammad Shoeybi, Bryan Catanzaro, Jan Kautz, Pavlo Molchanov:
Compact Language Models via Pruning and Knowledge Distillation. CoRR abs/2407.14679 (2024) - [i8]Sharath Turuvekere Sreenivas, Saurav Muralidharan, Raviraj Joshi, Marcin Chochowski, Mostofa Patwary, Mohammad Shoeybi, Bryan Catanzaro, Jan Kautz, Pavlo Molchanov:
LLM Pruning and Distillation in Practice: The Minitron Approach. CoRR abs/2408.11796 (2024) - [i7]Gongfan Fang, Hongxu Yin, Saurav Muralidharan, Greg Heinrich, Jeff Pool, Jan Kautz, Pavlo Molchanov, Xinchao Wang:
MaskLLM: Learnable Semi-Structured Sparsity for Large Language Models. CoRR abs/2409.17481 (2024) - 2023
- [i6]Yannan Nellie Wu, Po-An Tsai, Saurav Muralidharan, Angshuman Parashar, Vivienne Sze, Joel S. Emer:
HighLight: Efficient and Flexible DNN Acceleration with Hierarchical Structured Sparsity. CoRR abs/2305.12718 (2023) - [i5]Harvey Dam, Vinu Joseph, Aditya Bhaskara, Ganesh Gopalakrishnan, Saurav Muralidharan, Michael Garland:
Understanding the Effect of the Long Tail on Neural Network Compression. CoRR abs/2306.06238 (2023) - [i4]Cameron Shinn, Collin McCarthy, Saurav Muralidharan, Muhammad Osama, John D. Owens:
The Sparsity Roofline: Understanding the Hardware Limits of Sparse Neural Networks. CoRR abs/2310.00496 (2023) - 2022
- [i3]Salar Latifi, Saurav Muralidharan, Michael Garland:
Efficient Sparsely Activated Transformers. CoRR abs/2208.14580 (2022) - 2020
- [i2]Vinu Joseph, Shoaib Ahmed Siddiqui, Aditya Bhaskara, Ganesh Gopalakrishnan, Saurav Muralidharan, Michael Garland, Sheraz Ahmed, Andreas Dengel:
Reliable Model Compression via Label-Preservation-Aware Loss Functions. CoRR abs/2012.01604 (2020) - 2019
- [i1]Vinu Joseph, Saurav Muralidharan, Animesh Garg, Michael Garland, Ganesh Gopalakrishnan:
A Programmable Approach to Model Compression. CoRR abs/1911.02497 (2019)
Coauthor Index
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