Publications

Our publications provide insight into some of our leading-edge research.

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2023

Legate Sparse: Distributed Sparse Computing in Python
Rohan Yadav, Wonchan Lee, Melih Elibol, Taylor Patti, Manolis Papadakis, Michael Garland, Alex Aiken, Fredrik Kjolstad, Michael Bauer
Neuralangelo: High-Fidelity Neural Surface Reconstruction
Max Zhaoshuo Li, Thomas Müller, Alex Evans, Russell H. Taylor, Mathias Unberath, Ming-Yu Liu, Chen-Hsuan Lin
The Best Inventions of 2023, TIME Magazine
Visibility Algorithms for Dynamic Dependence Analysis and Distributed Coherence
Michael Bauer, Elliott Slaughter, Sean Treichler, Wonchan Lee, Michael Garland, Alex Aiken
Parsimony: Enabling SIMD/Vector Programming in Standard Compiler Flows
Vijay Kandiah, Daniel Lustig, Oreste Villa, David Nellans, Nikos Hardavellas
Implementing Reinforcement Learning Datacenter Congestion Control in NVIDIA NICs
Benjamin Fuhrer, Yuval Shpigelman, Chen Tessler, Shie Mannor, Gal Chechik, Eitan Zahavy, Gal Dalal

2021

GPS: A Global Publish-Subscribe Model for Multi-GPU Memory Management
Harini Muthukrishnan, Daniel Lustig, David Nellans, Thomas Wenisch
Best Paper nominee
IEEE Micro Top Picks in Computer Architecture (Honorable Mention)
EMOGI: Efficient Memory-access for Out-of-memory Graph-traversal in GPUs
Seung Won Min, Vikram Sharma Mailthody, Zaid Qureshi, Jinjun Xiong, Eiman Ebrahimi, Wen-mei Hwu
Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture
Seung Won Min, Kun Wu, Sitao Huang, Mert Hidayetoglu, Jinjun Xiong, Eiman Ebrahimi, Deming Chen, Wen-mei Hwu
Suraksha: A Quantitative AV Safety Evaluation Framework to Analyze Safety Implications of Perception Design Choices
Hengyu Zhao, Siva Hari, Timothy Tsai, Michael B. Sullivan, Steve Keckler, Jishen Zhao
Efficient Multi-GPU Shared Memory via Automatic Optimization of Fine-Grained Transfers
Harini Muthukrishnan, David Nellans, Daniel Lustig, Jeffrey Fessler, Thomas Wenisch
Demystifying GPU Reliability: Comparing and Combining Beam Experiments, Fault Simulation, and Profiling
Fernando Fernandes dos Santos, Siva Hari, Pedro Martins Basso, Luigi Carro, Paolo Rech
Learning Sparse Matrix Row Permutations for Efficient SpMM on GPU Architectures
Atefeh Mehrabi, Donghyuk Lee, Niladrish Chatterjee, Danial J. Sorin, Benjamin C. Lee, Mike O'Connor
Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture
Seung Won Min, Kun Wu, Sitao Huang, Mert Hidayetoglu, Jinjun Xiong, Eiman Ebrahimi, Deming Chen, Wen-mei Hwu
Scaling Implicit Parallelism via Dynamic Control Replication
Michael Bauer, Wonchan Lee, Elliott Slaughter, Zhihao Jia, Mario Di Renzo, Manolis Papadakis, Galen Shipman, Patrick McCormick, Michael Garland, Alex Aiken