Smart, adaptive mapping of parallelism in the presence of external workload. - Murali Krishna Emani; Zheng Wang 001; Michael F. P. O'Boyle (CGO, 2013)

Change Detection Based Parallelism Mapping: Exploiting Offline Models and Online Adaptation. - Murali Krishna Emani; Michael F. P. O'Boyle (LCPC, 2014)

Celebrating diversity: a mixture of experts approach for runtime mapping in dynamic environments. - Murali Krishna Emani; Michael F. P. O'Boyle (PLDI, 2015)

Adaptive parallelism mapping in dynamic environments using machine learning. - Murali Krishna Emani (2015)

Integrating Algorithmic Parameters into Benchmarking and Design Space Exploration in 3D Scene Understanding. - Bruno Bodin; Luigi Nardi; M. Zeeshan Zia; Harry Wagstaff; Govind Sreekar Shenoy; Murali Krishna Emani; John Mawer; Christos Kotselidis; Andy Nisbet; Mikel Luján; Björn Franke; Paul H. J. Kelly; et al. (PACT, 2016)

Mapping Medley: Adaptive Parallelism Mapping with Varying Optimization Goals. - Murali Krishna Emani (LCPC, 2016)

Bootstrapping Parameter Space Exploration for Fast Tuning. - Jayaraman J. Thiagarajan; Nikhil Jain; Rushil Anirudh; Alfredo Giménez; Rahul Sridhar; Aniruddha Marathe; Tao Wang 077; Murali Emani; Abhinav Bhatele; Todd Gamblin (ICS, 2018)

MPI Stages: Checkpointing MPI State for Bulk Synchronous Applications. - Nawrin Sultana; Anthony Skjellum; Ignacio Laguna; Matthew Shane Farmer; Kathryn M. Mohror; Murali Emani (EuroMPI, 2018)

Is Data Placement Optimization Still Relevant on Newer GPUs? - Md Abdullah Shahneous Bari; Larisa Stoltzfus; Pei-Hung Lin; Chunhua Liao; Murali Emani; Barbara M. Chapman (PMBS@SC, 2018)

Data Placement Optimization in GPU Memory Hierarchy using Predictive Modeling. - Larisa Stoltzfus; Murali Emani; Pei-Hung Lin; Chunhua Liao (MCHPC@SC, 2018)

MELA: A Visual Analytics Tool for Studying Multifidelity HPC System Logs. - Shilpika; Bethany Lusch; Murali Emani; Venkatram Vishwanath; Michael E. Papka; Kwan-Liu Ma (DAAC@SC, 2019)

Machine Learning Guided Optimal Use of GPU Unified Memory. - Hailu Xu; Murali Emani; Pei-Hung Lin; Liting Hu; Chunhua Liao (MCHPC@SC, 2019)

EReinit: Scalable and efficient fault-tolerance for bulk-synchronous MPI applications. - Sourav Chakraborty 003; Ignacio Laguna; Murali Emani; Kathryn M. Mohror; Dhabaleswar K. Panda 001; Martin Schulz 001; Hari Subramoni (Concurr. Comput. Pract. Exp., 2020)

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 C. Fox; David Kanter; Thorsten Kurth; Peter Mattson; Dawei Mu; Amit Ruhela; et al. (MLHPC@SC, 2021)

HPC Ontology: Towards a Unified Ontology for Managing Training Datasets and AI Models for High-Performance Computing. - Chunhua Liao; Pei-Hung Lin; Gaurav Verma; Tristan Vanderbruggen; Murali Emani; Zifan Nan; Xipeng Shen (MLHPC@SC, 2021)

HPCFAIR: Enabling FAIR AI for HPC Applications. - Gaurav Verma; Murali Emani; Chunhua Liao; Pei-Hung Lin; Tristan Vanderbruggen; Xipeng Shen; Barbara M. Chapman (MLHPC@SC, 2021)

Stream-AI-MD: streaming AI-driven adaptive molecular simulations for heterogeneous computing platforms. - Alexander Brace; Michael Salim; Vishal Subbiah; Heng Ma; Murali Emani; Anda Trifan; Austin R. Clyde; Corey Adams; Thomas D. Uram; Hyun Seung Yoo; Andew Hock; Jessica Liu; et al. (PASC, 2021)

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 C. Fox; David Kanter; Thorsten Kurth; Peter Mattson; Dawei Mu; Amit Ruhela; et al. (CoRR, 2021)

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; Volodymyr V. Kindratenko; Anne C. Elster (Comput. Sci. Eng., 2021)

Toward an In-Depth Analysis of Multifidelity High Performance Computing Systems. - Shilpika; Bethany Lusch; Murali Emani; Filippo Simini; Venkatram Vishwanath; Michael E. Papka; Kwan-Liu Ma (CCGRID, 2022)

Towards neural architecture-aware exploration of compiler optimizations in a deep learning {graph} compiler. - Gaurav Verma; Swetang Finviya; Abid M. Malik; Murali Emani; Barbara M. Chapman (CF, 2022)

Early Experience with Transformer-Based Similarity Analysis for DataRaceBench. - Winson Chen; Tristan Vanderbruggen; Pei-Hung Lin; Chunhua Liao; Murali Emani (Correctness@SC, 2022)

Finding Reusable Machine Learning Components to Build Programming Language Processing Pipelines. - Patrick J. Flynn; Tristan Vanderbruggen; Chunhua Liao; Pei-Hung Lin; Murali Emani; Xipeng Shen (ECSA (Tracks and Workshops), 2022)

Efficient Design Space Exploration for Sparse Mixed Precision Neural Architectures. - Krishna Teja Chitty-Venkata; Murali Emani; Venkatram Vishwanath; Arun K. Somani (HPDC, 2022)

Interactive NLU-Powered Ontology-Based Workflow Synthesis for FAIR Support of HPC. - Zifan Nan; Mithil Dave; Xipeng Shen; Chunhua Liao; Tristan Vanderbruggen; Pei-Hung Lin; Murali Emani (HUST@SC, 2022)

Throughput-oriented and Accuracy-aware DNN Training with BFloat16 on GPU. - Zhen Xie; Siddhisanket Raskar; Murali Emani (IPDPS Workshops, 2022)

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; et al. (PMBS@SC, 2022)

AI Benchmarking for Science: Efforts from the MLCommons Science Working Group. - Jeyan Thiyagalingam; Gregor von Laszewski; Junqi Yin; Murali Emani; Juri Papay; Gregg Barrett; Piotr Luszczek; Aristeidis Tsaris; Christine R. Kirkpatrick; Feiyi Wang; Tom Gibbs; Venkatram Vishwanath; et al. (ISC Workshops, 2022)

Making Machine Learning Datasets and Models FAIR for HPC: A Methodology and Case Study. - Pei-Hung Lin; Chunhua Liao; Winson Chen; Tristan Vanderbruggen; Murali Emani; Hailu Xu (TransAI, 2022)

Neural Architecture Search for Transformers: A Survey. - Krishna Teja Chitty-Venkata; Murali Emani; Venkatram Vishwanath; Arun K. Somani (IEEE Access, 2022)

XUnified: A Framework for Guiding Optimal Use of GPU Unified Memory. - Hailu Xu; Pei-Hung Lin; Murali Emani; Liting Hu; Chunhua Liao (IEEE Access, 2022)

FAIR for AI: An interdisciplinary, international, inclusive, and diverse community building perspective. - Eliu A. Huerta; Ben Blaiszik; L. Catherine Brinson; Kristofer E. Bouchard; Daniel Diaz 003; Caterina Doglioni; Javier M. Duarte; Murali Emani; Ian T. Foster; Geoffrey C. Fox; Philip C. Harris; Lukas Heinrich; et al. (CoRR, 2022)

Towards Seamless Management of AI Models in High-Performance Computing. - Sixing Yu; Murali Emani; Chunhua Liao; Pei-Hung Lin; Tristan Vanderbruggen; Xipeng Shen; Ali Jannesari (CoRR, 2022)

Intelligent resolution: Integrating Cryo-EM with AI-driven multi-resolution simulations to observe the severe acute respiratory syndrome coronavirus-2 replication-transcription machinery in action. - Anda Trifan; Defne Gorgun; Michael Salim; Zongyi Li; Alexander Brace; Maxim Zvyagin; Heng Ma; Austin Clyde; David Clark; David J. Hardy; Tom Burnley; Lei Huang 019; et al. (Int. J. High Perform. Comput. Appl., 2022)

TrainBF: High-Performance DNN Training Engine Using BFloat16 on AI Accelerators. - Zhen Xie; Siddhisanket Raskar; Murali Emani; Venkatram Vishwanath (Euro-Par, 2023)

LM4HPC: Towards Effective Language Model Application in High-Performance Computing. - Le Chen; Pei-Hung Lin; Tristan Vanderbruggen; Chunhua Liao; Murali Emani; Bronis R. de Supinski (IWOMP, 2023)

Transfer Learning Across Heterogeneous Features For Efficient Tensor Program Generation. - Gaurav Verma; Siddhisanket Raskar; Zhen Xie; Abid M. Malik; Murali Emani; Barbara M. Chapman (ExHET@PPoPP, 2023)

Characterizing the Performance of Triangle Counting on Graphcore's IPU Architecture. - Reet Barik; Siddhisanket Raskar; Murali Emani; Venkatram Vishwanath (SC Workshops, 2023)

Data Race Detection Using Large Language Models. - Le Chen; Xianzhong Ding; Murali Emani; Tristan Vanderbruggen; Pei-Hung Lin; Chunhua Liao (SC Workshops, 2023)

HPC-GPT: Integrating Large Language Model for High-Performance Computing. - Xianzhong Ding; Le Chen; Murali Emani; Chunhua Liao; Pei-Hung Lin; Tristan Vanderbruggen; Zhen Xie; Alberto Cerpa; Wan Du (SC Workshops, 2023)

Differentiable Neural Architecture, Mixed Precision and Accelerator Co-Search. - Krishna Teja Chitty-Venkata; Yiming Bian; Murali Emani; Venkatram Vishwanath; Arun K. Somani (IEEE Access, 2023)

Neural Architecture Search Benchmarks: Insights and Survey. - Krishna Teja Chitty-Venkata; Murali Emani; Venkatram Vishwanath; Arun K. Somani (IEEE Access, 2023)

A Comprehensive Performance Study of Large Language Models on Novel AI Accelerators. - Murali Emani; Sam Foreman; Varuni Sastry; Zhen Xie; Siddhisanket Raskar; William Arnold; Rajeev Thakur; Venkatram Vishwanath; Michael E. Papka (CoRR, 2023)

DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies. - Shuaiwen Leon Song; Bonnie Kruft; Minjia Zhang; Conglong Li; Shiyang Chen 004; Chengming Zhang 006; Masahiro Tanaka; Xiaoxia Wu; Jeff Rasley; Ammar Ahmad Awan; Connor Holmes; Martin Cai; et al. (CoRR, 2023)

GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics. - 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 W. Irvin; et al. (Int. J. High Perform. Comput. Appl., 2023)

A survey of techniques for optimizing transformer inference. - Krishna Teja Chitty-Venkata; Sparsh Mittal; Murali Emani; Venkatram Vishwanath; Arun K. Somani (J. Syst. Archit., 2023)

A Multi-Level, Multi-Scale Visual Analytics Approach to Assessment of Multifidelity HPC Systems. - Shilpika; Bethany Lusch; Murali Emani; Filippo Simini; Venkatram Vishwanath; Michael E. Papka; Kwan-Liu Ma (CCGrid, 2024)

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)

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 001; Murali Emani; Venkatram Vishwanath (SC Workshops, 2024)

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 Katti Sastry; Huihuo Zheng; Logan T. Ward; Servesh Muralidharan; Archit Vasan; Bharat Kale; Carla M. Mann; et al. (SC, 2024)

Centimani: Enabling Fast AI Accelerator Selection for DNN Training with a Novel Performance Predictor. - Zhen Xie; Murali Emani; Xiaodong Yu 001; Dingwen Tao; Xin He; Pengfei Su 001; Keren Zhou 001; Venkatram Vishwanath (USENIX ATC, 2024)

AI-coupled HPC Workflow Applications, Middleware and Performance. - Wes Brewer; Ana Gainaru; Frédéric Suter; Feiyi Wang; Murali Emani; Shantenu Jha (CoRR, 2024)

Thorough Characterization and Analysis of Large Transformer Model Training At-Scale. - Scott Cheng; Jun-Liang Lin; Murali Emani; Siddhisanket Raskar; Sam Foreman; Zhen Xie; Venkatram Vishwanath; Mahmut Taylan Kandemir (Proc. ACM Meas. Anal. Comput. Syst., 2024)

BaKlaVa - Budgeted Allocation of KV cache for Long-context Inference. - Ahmed Burak Gulhan; Krishna Teja Chitty-Venkata; Murali Emani; Mahmut T. Kandemir; Venkatram Vishwanath (CoRR, 2025)

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; et al. (CoRR, 2025)

LangVision-LoRA-NAS: Neural Architecture Search for Variable LoRA Rank in Vision Language Models. - Krishna Teja Chitty-Venkata; Murali Emani; Venkatram Vishwanath (CoRR, 2025)