Experimental evaluation of NISQ quantum computers: error measurement, characterization, and implications. - Tirthak Patel; Abhay Potharaju; Baolin Li 001; Rohan Basu Roy; Devesh Tiwari (SC, 2020)

UREQA: Leveraging Operation-Aware Error Rates for Effective Quantum Circuit Mapping on NISQ-Era Quantum Computers. - Tirthak Patel; Baolin Li 001; Rohan Basu Roy; Devesh Tiwari (USENIX ATC, 2020)

Serving Machine Learning Inference Using Heterogeneous Hardware. - Baolin Li 001; Vijay Gadepally; Siddharth Samsi; Mark S. Veillette; Devesh Tiwari (HPEC, 2021)

The MIT Supercloud Dataset. - Siddharth Samsi; Matthew L. Weiss; David Bestor; Baolin Li 001; Michael Jones 001; Albert Reuther; Daniel Edelman; William Arcand; Chansup Byun; John Holodnack; Matthew Hubbell; Jeremy Kepner; et al. (HPEC, 2021)

RIBBON: cost-effective and qos-aware deep learning model inference using a diverse pool of cloud computing instances. - Baolin Li 001; Rohan Basu Roy; Tirthak Patel; Vijay Gadepally; Karen Gettings; Devesh Tiwari (SC, 2021)

MISO: exploiting multi-instance GPU capability on multi-tenant GPU clusters. - Baolin Li 001; Tirthak Patel; Siddharth Samsi; Vijay Gadepally; Devesh Tiwari (SoCC, 2022)

Do Temperature and Humidity Exposures Hurt or Benefit Your SSDs? - Adnan Maruf; Sashri Brahmakshatriya; Baolin Li 001; Devesh Tiwari; Gang Quan; Janki Bhimani (DATE, 2022)

AI-Enabling Workloads on Large-Scale GPU-Accelerated System: Characterization, Opportunities, and Implications. - Baolin Li 001; Rohin Arora; Siddharth Samsi; Tirthak Patel; William Arcand; David Bestor; Chansup Byun; Rohan Basu Roy; Bill Bergeron; John T. Holodnak; Michael Houle 001; Matthew Hubbell; et al. (HPCA, 2022)

Benchmarking Resource Usage for Efficient Distributed Deep Learning. - Nathan C. Frey; Baolin Li 001; Joseph McDonald; Dan Zhao 007; Michael Jones 001; David Bestor; Devesh Tiwari; Vijay Gadepally; Siddharth Samsi (HPEC, 2022)

DASH: Scheduling Deep Learning Workloads on Multi-Generational GPU-Accelerated Clusters. - Baolin Li 001; Tirthak Patel; Vijay Gadepally; Karen Gettings; Siddharth Samsi; Devesh Tiwari (HPEC, 2022)

Characterizing Multi-Instance GPU for Machine Learning Workloads. - Baolin Li 001; Vijay Gadepally; Siddharth Samsi; Devesh Tiwari (IPDPS Workshops, 2022)

The MIT Supercloud Workload Classification Challenge. - Benny J. Tang; Qiqi Chen; Matthew L. Weiss; Nathan C. Frey; Joseph McDonald; David Bestor; Charles Yee; William Arcand; William Bergeron; Chansup Byun; Daniel Edelman; Michael Houle 001; et al. (IPDPS Workshops, 2022)

Great Power, Great Responsibility: Recommendations for Reducing Energy for Training Language Models. - Joseph McDonald; Baolin Li 001; Nathan C. Frey; Devesh Tiwari; Vijay Gadepally; Siddharth Samsi (NAACL-HLT (Findings), 2022)

The MIT Supercloud Workload Classification Challenge. - Benny J. Tang; Qiqi Chen; Matthew L. Weiss; Nathan C. Frey; Joseph McDonald; David Bestor; Charles Yee; William Arcand; Chansup Byun; Daniel Edelman; Matthew Hubbell; Michael Jones 001; et al. (CoRR, 2022)

Using Multi-Instance GPU for Efficient Operation of Multi-Tenant GPU Clusters. - Baolin Li 001; Tirthak Patel; Siddharth Samsi; Vijay Gadepally; Devesh Tiwari (CoRR, 2022)

Building Heterogeneous Cloud System for Machine Learning Inference. - Baolin Li 001; Siddharth Samsi; Vijay Gadepally; Devesh Tiwari (CoRR, 2022)

Sustainable Supercomputing for AI: GPU Power Capping at HPC Scale. - Dan Zhao 007; Siddharth Samsi; Joseph McDonald; Baolin Li 001; David Bestor; Michael Jones 001; Devesh Tiwari; Vijay Gadepally (SoCC, 2023)

Kairos: Building Cost-Efficient Machine Learning Inference Systems with Heterogeneous Cloud Resources. - Baolin Li 001; Siddharth Samsi; Vijay Gadepally; Devesh Tiwari (HPDC, 2023)

From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inference. - Siddharth Samsi; Dan Zhao 007; Joseph McDonald; Baolin Li 001; Adam Michaleas; Michael Jones 001; William Bergeron; Jeremy Kepner; Devesh Tiwari; Vijay Gadepally (HPEC, 2023)

Toward Sustainable HPC: Carbon Footprint Estimation and Environmental Implications of HPC Systems. - Baolin Li 001; Rohan Basu Roy; Daniel Wang; Siddharth Samsi; Vijay Gadepally; Devesh Tiwari (SC, 2023)

Clover: Toward Sustainable AI with Carbon-Aware Machine Learning Inference Service. - Baolin Li 001; Siddharth Samsi; Vijay Gadepally; Devesh Tiwari (SC, 2023)

Green Carbon Footprint for Model Inference Serving via Exploiting Mixed-Quality Models and GPU Partitioning. - Baolin Li 001; Siddharth Samsi; Vijay Gadepally; Devesh Tiwari (CoRR, 2023)

Sustainable HPC: Modeling, Characterization, and Implications of Carbon Footprint in Modern HPC Systems. - Baolin Li 001; Siddharth Samsi; Vijay Gadepally; Devesh Tiwari (CoRR, 2023)

Sprout: Green Generative AI with Carbon-Efficient LLM Inference. - Baolin Li 001; Yankai Jiang 002; Vijay Gadepally; Devesh Tiwari (EMNLP, 2024)

LLM Inference Serving: Survey of Recent Advances and Opportunities. - Baolin Li 001; Yankai Jiang 002; Vijay Gadepally; Devesh Tiwari (HPEC, 2024)

Interpretable Analysis of Production GPU Clusters Monitoring Data via Association Rule Mining. - Baolin Li 001; Siddharth Samsi; Vijay Gadepally; Devesh Tiwari (IPDPS, 2024)

EcoLife: Carbon-Aware Serverless Function Scheduling for Sustainable Computing. - Yankai Jiang 002; Rohan Basu Roy; Baolin Li 001; Devesh Tiwari (SC, 2024)

Toward Sustainable GenAI using Generation Directives for Carbon-Friendly Large Language Model Inference. - Baolin Li 001; Yankai Jiang 002; Vijay Gadepally; Devesh Tiwari (CoRR, 2024)