Murali Emani

Computer Scientist

Artificial Intelligence & Machine Learning Group
Argonne Leadership Computing Facility (ALCF)
Argonne National Laboratory

memani [at] anl [dot] gov
Google Scholar LinkedIn ANL Profile AI Testbed MLCommons
Photo of Murali Emani
Background

About

I am a Computer Scientist in the Artificial Intelligence and Machine Learning (AIML) group with the Argonne Leadership Computing Facility (ALCF) at Argonne National Laboratory. Prior, I was a Postdoctoral Research Staff Member at Lawrence Livermore National Laboratory, US.

I obtained my PhD and worked as a Research Associate at the Institute for Computing Systems Architecture at the School of Informatics, University of Edinburgh, UK under the guidance of Prof. Michael O'Boyle.

At ALCF, I co-lead the AI Testbed where we explore the performance and efficiency of novel AI accelerators for scientific machine learning applications. I also served as co-chair for the MLPerf HPC group at MLCommons to benchmark large-scale ML on HPC systems.

My current projects focus on:

Developing performance models to identify and address bottlenecks while scaling ML/DL frameworks on emerging supercomputers for scientific applications; co-design of emerging hardware architectures to scale up machine learning algorithms; and benchmarking ML/DL frameworks and methods on HPC systems.

Parallel Programming Models High Performance Computing Scalable Machine Learning Runtime Systems AI Accelerators Benchmarking Online Adaptation LLM Inference

Positions

  • Computer Scientist, ALCF — Argonne National Lab
  • Co-Lead, AI Testbed @ ALCF
  • Former co-chair, MLPerf HPC, MLCommons

Education

  • PhD, University of Edinburgh, UK
  • Masters, IIIT Bangalore, India
Latest

News

June '26
ICMl'26Paper on LeXI: Layer-Adaptive Active Experts for Efficient MoE Model Inference selected at AdaptFM workshop at ICML'26.
Mar '26
ICLR'26Paper on ImageNet-Think-250K: A Large-Scale Synthetic Dataset for Multimodal Reasoning for Vision Language Models selected at DATA-FM workshop at ICLR'26.
Feb '26
PaperPaper on A Robust, Defensible, and Reproducible Methodology for Benchmarking Single-Turn Jailbreak Attacks on Large Language Models published for the India AI Impact Summit.
Jan '26
EACL'26Paper on PagedEviction: Structured Block-wise KV Cache Pruning for Efficient Large Language Model Inference selected at EACL'26.
Dec '25
ACL'25Paper on Mātṛkā: Multilingual Jailbreak Evaluation of Open-Source Large Language Models selected at the 1st Workshop on Benchmarks for Human-Centric AI in Indian Languages at ACL 2025.
Sep '25
Gordon BellAERIS: Argonne Earth Systems Model for Reliable and Skillful Predictions — finalist for the 2025 ACM Gordon Bell Prize in Climate Modeling.
Sep '25
SC'25Paper on MoE-Inference-Bench accepted at PMBS workshop at SC25; paper on sparse fine-tuning for wafer-scale engines accepted at ExHetAI workshop at SC25.
Jul '25
ICCV'25Paper on MoPEQ: Mixture of Mixed Precision Quantized Experts accepted at Workshop on Extreme Quantization for Computer Vision at ICCV 2025.
Jun '25
ICIP'25Paper on Langvision-Lora-NAS accepted at IEEE ICIP 2025. Co-organized two BoF sessions and a tutorial on programming novel AI accelerators at ISC25.
Feb '25
arXivPapers on AILuminate: v1.0 of the AI Risk and Reliability Benchmark from MLCommons and BaKlaVa — Budgeted Allocation of KV cache for Long-context Inference available on arXiv.
Research Output

Publications

2026
Preprint
Pluralis v0.1: Towards a Multicultural, Multimodal, Multilingual Benchmark for AI Risk and Reliability
Alicia Parrish, Rajat Shinde, Sanket Badhe, Xinyi Bai, Sree Bhargavi Balija, Hua-Rong Chu, Emilio Ferrara, Armstrong Foundjem, Rajat Ghosh, Aakash Gupta, Xuanli He, Ong Chen Hui, Minji Jung, Madhangi Karimanal, Faiza Khan Khattak, Boryoung Kim, Eugenia Kim, Liliya Lavitas, Seok Min Lim, Victor Lu, Jim Moirangthem, Dhivya Nagasubramanian, Deepak Pandita, Sita Rajagopal, Geetha Raju, Evgeniia Razumovskaia, Aravind Reddy, Federico Ricciuti, Nobin Sarwar, Sungpil Shin, Sunayana Sitaram, Snehal Thorat, Tharindu Cyril Weerasooriya, Jasmijn Bastings, Joachim Baumann, Kongtao Chen, Murali Emani, Mariya Hendriksen, Jiho Jin, Jun Seong Kim, Younghoon Ko, Alicja Kwasniewska, Minjae Lee, Tom Wei-cyuan Lin, Kashyap Manjusha, Junho Myung, Junyeong Park, Roma Patel, Shyam Ratan, Sudarsun Santhiappan, Priyanka Suresh, Ksheeraj Sai Vepuri, Laura Amortegui-Ordonez, Claire Dennis, Minsuk Kahng, Chris Knotz, Alice Oh, Balaraman Ravindran, Soojung Ryu, William Bartholomew, Hiwot Tesfaye, Lora Aroyo
AdaptFM
ICML'26
LExI: Layer-Adaptive Active Experts for Efficient MoE Model Inference
Krishna Teja Chitty-Venkata, Murali Emani
ISC'26r
PreLoRA: Hybrid Pre-training of Vision Transformers with Full Training and Low-Rank Adapters
Krishu K Thapa, Reet Barik, Krishna Teja Chitty-Venkata, Murali Emani, Venkatram Vishwanath
Data-FM
ICLR'26
ImageNet-Think-250K: A Large-Scale Synthetic Dataset for Multimodal Reasoning for Vision Language Models
Krishna Teja Chitty-Venkata, Murali Emani
EACL'26
PagedEviction: Structured Block-wise KV Cache Pruning for Efficient Large Language Model Inference
Krishna Teja Chitty-Venkata, Jie Ye, Xian-He Sun, Anthony Kougkas, Murali Emani, Venkatram Vishwanath, Bogdan Nicolae
Preprint
Agentic Product Maturity Ladder V0.1
Sean McGregor, Deepak Nathani, Lama Saouma, Fazl Barez, Armstrong Foundjem, Tuesday, Aakash Gupta, Jake Thomas, Vassil Tashev, Tianhao Li, Victor Lu, Saurav Sahay, Bennett Hillenbrand, Cynthia Keyser, Trupti Bavalatti, M Chockalingam, Dhivya Nagasubramanian, Faiza Khan Khattak, Medha Bankhwal, Murali Emani, Jacqueline Stetson, James Ezick, Jason Stanley, Joachim Baumann, Rokas Gipiškis, Ravishankar K. Iyer, Roman Eng, Kihyuk Nam, William Bartholomew, Alexandre Drouin, Benjamin Larsen, Chin Ze Shen, Daniel Herde, Arihant Chadda, Malek Ben Salem, Daniel Kang, Kurt Bollacker, Mark Watson, Peter Mattson
Tech
Report
A Robust, Defensible, and Reproducible Methodology for Benchmarking Single-Turn Jailbreak Attacks on LLMs
Carsten Maple, Riya Tapwal, Chris Knotz, James Ezick, Alicia Parrish, Murali Emani, et al.
ACL'25
Workshop
Mātṛkā: Multilingual Jailbreak Evaluation of Open-Source Large Language Models
Carsten Maple, Riya Tapwal, Chris Knotz, James Ezick, James Goel, Alicia Parrish, Federico Ricciuti, Jonathan Petit, Ong Chen Hui, Seok Min Lim, Armstrong Foundjem, Tuesday, Elie Alhajjar, Sean McGregor, Kashyap Ramanandula Manjusha, Andrew Gruen, Bennett Hillenbrand, Jacqueline Stetson, Kurt Bollacker, Abhishek Kumar, Kongtao Chen, Simone Tedeschi, Aakash Gupta, Vasilios Mavroudis, Victor Lu, Rajat Shinde, Satyapriya Krishna, Vincent Alessi, Jibin Varghese, Roman Eng, Virendra Mehta, Anita Khadka, Bo Li, Washington Mbonu, Saif Ul Islam, Cong Chen, Vassil Tashev, Manish Bhatt, Matteo Prandi, Serrhini Mohammed, Tianhao Li, Sarah Luger, Hua-Rong Chu, Leon Derczynski, Murali Emani, Saurav Sahay, Faiza Khan Khattak, Rajat Ghosh, Nobin Sarwar, Iris Johnson, Dhivya Nagasubramanian, Xuanli He, Eugenia Kim, Sujata Goswami, Fazl Barez, Jean-Philippe Monteuuis, Sanket Badhe, Foutse Khomh, Chee Seng Chan, Priyanka Tiwari, Ying-Jung Chen, Lora Aroyo, Kenneth Fricklas, Manil Maskey, Trupti Bavalatti, Bryan Tegomoh, Natan Vidra
2025
SC'25
AERIS: Argonne Earth Systems Model for Reliable and Skillful Predictions🏆 Gordon Bell Finalist
Väinö Hatanpää, Eugene Ku, Jason Stock, Murali Emani, Sam Foreman, Chunyong Jung, Sandeep Madireddy, Tung Nguyen, Varuni Sastry, Ray A. O. Sinurat, Sam Wheeler, Huihuo Zheng, Troy Arcomano, Venkatram Vishwanath, Rao Kotamarthi
PMBS'25
MoE-Inference-Bench: Performance Evaluation of Mixture of Expert Large Language and Vision Models
Krishna Teja Chitty-Venkata, Sylvia Howland, Golara Azar, Daria Soboleva, Natalia Vassilieva, Siddhisanket Raskar, Murali Emani, Venkatram Vishwanath
ICCV'25
Workshop
MoPEQ: Mixture of Mixed Precision Quantized Experts
Krishna Teja Chitty-Venkata, Jie Ye, Murali Emani, Venkatram Vishwanath
ICIP'25
Langvision-Lora-NAS: Neural Architecture Search for Variable LoRA Rank in Vision Language Models
Krishna Teja Chitty-Venkata, Murali Emani, Venkatram Vishwanath
arXiv
AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
Shaona Ghosh, Heather Frase, Adina Williams, Sarah Luger, Paul Röttger, Fazl Barez, Sean McGregor, Kenneth Fricklas, Mala Kumar, Quentin Feuillade--Montixi, Kurt Bollacker, Felix Friedrich, Ryan Tsang, Bertie Vidgen, Alicia Parrish, Chris Knotz, Eleonora Presani, Jonathan Bennion, Marisa Ferrara Boston, Mike Kuniavsky, Wiebke Hutiri, James Ezick, Malek Ben Salem, Rajat Sahay, Sujata Goswami, Usman Gohar, Ben Huang, Supheakmungkol Sarin, Elie Alhajjar, Canyu Chen, Roman Eng, Kashyap Ramanandula Manjusha, Virendra Mehta, Eileen Long, Murali Emani, Natan Vidra, Benjamin Rukundo, Abolfazl Shahbazi, Kongtao Chen, Rajat Ghosh, Vithursan Thangarasa, Pierre Peigné, Abhinav Singh, Max Bartolo, Satyapriya Krishna, Mubashara Akhtar, Rafael Gold, Cody Coleman, Luis Oala, Vassil Tashev, Joseph Marvin Imperial, Amy Russ, Sasidhar Kunapuli, Nicolas Miailhe, Julien Delaunay, Bhaktipriya Radharapu, Rajat Shinde, Tuesday, Debojyoti Dutta, Declan Grabb, Ananya Gangavarapu, Saurav Sahay, Agasthya Gangavarapu, Patrick Schramowski, Stephen Singam, Tom David, Xudong Han, Priyanka Mary Mammen, Tarunima Prabhakar, Venelin Kovatchev, Rebecca Weiss, Ahmed Ahmed, Kelvin N. Manyeki, Sandeep Madireddy, Foutse Khomh, Fedor Zhdanov, Joachim Baumann, Nina Vasan, Xianjun Yang, Carlos Mougn, Jibin Rajan Varghese, Hussain Chinoy, Seshakrishna Jitendar, Manil Maskey, Claire V. Hardgrove, Tianhao Li, Aakash Gupta, Emil Joswin, Yifan Mai, Shachi H Kumar, Cigdem Patlak, Kevin Lu, Vincent Alessi, Sree Bhargavi Balija, Chenhe Gu, Robert Sullivan, James Gealy, Matt Lavrisa, James Goel, Peter Mattson
arXiv
BaKlaVa — Budgeted Allocation of KV Cache for Long-context Inference
Ahmed Burak Gulhan, Krishna Teja Chitty-Venkata, Murali Emani, Mahmut Kandemir, Venkatram Vishwanath
arXiv
Aurora: Architecting Argonne's First Exascale Supercomputer for Accelerated Scientific Discovery
William E. Allcock, Benjamin S. Allen, James Anchell, Victor Anisimov, Thomas Applencourt, Abhishek Bagusetty, Ramesh Balakrishnan, Riccardo Balin, Solomon Bekele, Colleen Bertoni, Cyrus Blackworth, Renzo Bustamante, Kevin Canada, John Carrier, Christopher Chan-nui, Lance C. Cheney, Taylor Childers, Paul Coffman, Susan Coghlan, Tanima Dey, Michael D'Mello, Ashok Emani, Murali Emani, Kyle G. Felker, Sam Foreman, Olivier Franza, Longfei Gao, Marta García, María Garzarán, Balazs Gerofi, Yasaman Ghadar, Subrata Goswami, Neha Gupta, Kevin Harms, Väinö Hatanpää, Brian Holland, Carissa Holohan, Brian Homerding, Khalid Hossain, Xue Hu, Louise Huot, Huda Ibeid, Joseph A. Insley, Sai Jayanthi, Hong Jiang, Wei Jiang, Xiao-Yong Jin, Jeongnim Kim, Christopher Knight, Panagiotis Kourdis, Kalyan Kumaran, JaeHyuk Kwack, Janghaeng Lee, Ti Leggett, Ben Lenard, Chris Lewis, Nevin Liber, Johann Lombardi, Raymond M. Loy, Ye Luo, Bethany Lusch, Nilakantan Mahadevan, Beth Markey, Victor A. Mateevitsi, Gordon McPheeters, Ryan Milner, Jerome Mitchell, Vitali A. Morozov, Servesh Muralidharan, Tom Musta, Mrigendra Nagar, Vikram Narayana, Marieme Ngom, Anthony-Trung Nguyen, Nathan Nichols, Aditya Nishtala, James C. Osborn, Michael E. Papka, Scott Parker, Saumil S. Patel, Julia Piotrowska, Adrian C. Pope, Sucheta Raghunanda, Esteban Rangel, Paul M. Rich, Katherine M. Riley, Silvio Rizzi, Kris Rowe, Varuni Sastry, Adam Scovel, Filippo Simini, Haritha Siddabathuni Som, Patrick Steinbrecher, Rick Stevens, Xinmin Tian, Peter Upton, Thomas Uram, Archit K. Vasan, Álvaro Vázquez-Mayagoitia, Kaushik Velusamy , Brice Videau, Venkatram Vishwanath, Brian Whitney, Timothy J. Williams, Michael Woodacre, Sam Zeltner, Chuanjun Zhang, Gengbin Zheng, Huihuo Zheng
arXiv
LExI: Layer-Adaptive Active Experts for Efficient MoE Model Inference
Krishna Teja Chitty-Venkata, Sandeep Madireddy, Murali Emani, Venkatram Vishwanath
arXiv
AI Benchmark Democratization and Carpentry
Gregor von Laszewski, Wesley Brewer, Jeyan Thiyagalingam, Juri Papay, Armstrong Foundjem, Piotr Luszczek, Murali Emani, Shirley V. Moore, Vijay Janapa Reddi, Matthew D. Sinclair, Sebastian Lobentanzer, Sujata Goswami, Benjamin Hawks, Marco Colombo, Nhan Tran, Christine R. Kirkpatrick, Abdulkareem Alsudais, Gregg Barrett, Tianhao Li, Kirsten Morehouse, Shivaram Venkataraman, Rutwik Jain, Kartik Mathur, Victor Lu, Tejinder Singh, Khojasteh Z. Mirza, Kongtao Chen, Sasidhar Kunapuli, Gavin Farrell, Renato Umeton, Geoffrey C. Fox
2024
PMBS'24
LLM-Inference-Bench: Inference Benchmarking of Large Language Models on AI Accelerators
Krishna Teja Chitty-Venkata, Siddhisanket Raskar, Bharat Kale, Farah Ferdaus, Aditya Tanikanti, Ken Raffenetti, Valerie Taylor, Murali Emani, Venkatram Vishwanath
SC'24
MProt-DPO: Breaking the ExaFLOPS Barrier for Multimodal Protein Design Workflows with Direct Preference Optimization
Gautham Dharuman, Kyle Hippe, Alexander Brace, Sam Foreman, Väinö Hatanpää, Varuni K. Sastry, Huihuo Zheng, Logan Ward, Servesh Muralidharan, Archit Vasan, Bharat Kale, Carla M. Mann, Heng Ma, Yun-Hsuan Cheng, Yuliana Zamora, Shengchao Liu, Chaowei Xiao, Murali Emani, Tom Gibbs, Mahidhar Tatineni, Deepak Canchi, Jerome Mitchell, Koichi Yamada, Maria Garzaran, Michael E. Papka, Ian Foster, Rick Stevens, Anima Anandkumar, Venkatram Vishwanath, Arvind Ramanathan
HCW'24
IPDPS
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, Dun Ma, Kiran Ranganath, Rick Weisner, Jiunn-yeu Chen, Yuting Yang, Natalia Vassilieva, Bin C. Zhang, Sylvia Howland, Alexander Tsyplikhin
USENIX
ATC'24
Centimani: Enabling Fast AI Accelerator Selection for DNN Training with a Novel Performance Predictor
Zhen Xie, Murali Emani, Xiaodong Yu, Dingwen Tao, Xin He, Pengfei Su, Keren Zhou, Venkatram Vishwanath
Euro-Par'24
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
arXiv
AI-coupled HPC Workflow Applications, Middleware and Performance
Wes Brewer, Ana Gainaru, Frédéric Suter, Feiyi Wang, Murali Emani, Shantenu Jha
2022 – 2023
SC'22
GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics🏆 ACM Gordon Bell Special Prize
Maxim Zvyagin, Alexander Brace, Kyle Hippe, Yuntian Deng, Bin Zhang, Cindy Orozco Bohorquez, Austin Clyde, Bharat Kale, Danilo Perez-Rivera, Heng Ma, Carla M. Mann, Michael Irvin, Defne G. Ozgulbas, Natalia Vassilieva, James Gregory Pauloski, Logan Ward, Valerie Hayot-Sasson, Murali Emani, Sam Foreman, Zhen Xie, Diangen Lin, Maulik Shukla, Weili Nie, Josh Romero, Christian Dallago, Arash Vahdat, Chaowei Xiao, Thomas Gibbs, Ian Foster, James J. Davis, Michael E. Papka, Thomas Brettin, Rick Stevens, Anima Anandkumar, Venkatram Vishwanath, Arvind Ramanathan
PMBS'22
A Comprehensive Evaluation of Novel AI Accelerators for Deep Learning Workloads
Murali Emani, Zhen Xie, Siddhisanket Raskar, Varuni Sastry, William Arnold, Bruce Wilson, Rajeev Thakur, Venkatram Vishwanath, Zhengchun Liu, Michael E. Papka, Cindy Orozco Bohorquez, Rick Weisner, Karen Li, Yongning Sheng, Yun Du, Jian Zhang, Alexander Tsyplikhin, Gurdaman Khaira, Jeremy Fowers, Ramakrishnan Sivakumar, Victoria Godsoe, Adrian Macias, Chetan Tekur, Matthew Boyd
Nature
Sci. Data'23
FAIR for AI: An interdisciplinary, international, inclusive, and diverse community building perspective
E.A. Huerta, Ben Blaiszik, L. Catherine Brinson, Kristofer E. Bouchard, Daniel Diaz, Caterina Doglioni, Javier M. Duarte, Murali Emani, Ian Foster, Geoffrey Fox, Philip Harris, Lukas Heinrich, Shantenu Jha, Daniel S. Katz, Volodymyr Kindratenko, Christine R. Kirkpatrick, Kati Lassila-Perini, Ravi K. Madduri, Mark S. Neubauer, Fotis E. Psomopoulos, Avik Roy, Oliver Rübel, Zhizhen Zhao, Ruike Zhu
IEEE CSE'21
Accelerating Scientific Applications With SambaNova Reconfigurable Dataflow Architecture
Murali Emani, Venkatram Vishwanath, Corey Adams, Michael E. Papka, Rick Stevens, Laura Florescu, Sumti Jairath, William Liu, Tejas Nama, Arvind Sujeeth
MLHPC'21
MLPerf HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems
Steven Farrell, Murali Emani, Jacob Balma, Lukas Drescher, Aleksandr Drozd, Andreas Fink, Geoffrey Fox, David Kanter, Thorsten Kurth, Peter Mattson, Dawei Mu, Amit Ruhela, Kento Sato, Koichi Shirahata, Tsuguchika Tabaru, Aristeidis Tsaris, Jan Balewski, Ben Cumming, Takumi Danjo, Jens Domke, Takaaki Fukai, Naoto Fukumoto, Tatsuya Fukushi, Balazs Gerofi, Takumi Honda, Toshiyuki Imamura, Akihiko Kasagi, Kentaro Kawakami, Shuhei Kudo, Akiyoshi Kuroda, Maxime Martinasso, Satoshi Matsuoka, Henrique Mendonça, Kazuki Minami, Prabhat Ram, Takashi Sawada, Mallikarjun Shankar, Tom St. John, Akihiro Tabuchi, Venkatram Vishwanath, Mohamed Wahib, Masafumi Yamazaki, Junqi Yin
Selected Earlier Work
PLDI'15
Celebrating Diversity: A Mixture of Experts Approach for Runtime Mapping in Dynamic Environments
Murali Krishna Emani, Michael O'Boyle — ACM SIGPLAN PLDI 2015
CGO'13
Smart, Adaptive Mapping of Parallelism in the Presence of External Workload
Murali Krishna Emani, Zheng Wang, Michael O'Boyle — CGO 2013
Community

Service

Program Committee

SIGMETRICS'27, CGO'27, ACL'26, MICRO'26, CAIS'26, ICPP'26, SC'26, ISC'26, MLSys'26, SIGKDD'26, AAAI'26
HiPC'25, KDD'25, SC'25, IISWC'25, CCGRID'25, ISC'25, SC'24, CCGRID'24, Bench'23, ISC'23, Bench'22, ICCD'22, IPDPS'22, SC'21 (posters), IPDPS'21, AI4Science@SC'20, ICPP'19, CCGRID'19, PACT'18, CCGRID'18, ICPP'18

Artifact Evaluation Committee

  • ASPLOS'21
  • ASPLOS'20
  • MLSys'20

Journal Reviewer

Concurrency & Computation, ParCo, ACM TOPC, Elsevier TJOR, ACM TECS, NSF panels

Intellectual Property

Granted Patents

US 9,317,456 B2
Method and system for performing event-matching with a graphical processing unit
Murali Krishna Emani, Sudeep Mallick
US 8,869,125 B2
Systems and Methods for Demarcating Information Related to one or more Blocks in an Application
Murali Krishna Emani, Sudeep Mallick, Balkrishna Prasad
US 9,043,775
Method for Identifying Problematic Loops in an Application and Devices thereof
Murali Krishna Emani, Sudeep Mallick, Balkrishna Prasad