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

2022

Towards Precision-Aware Fault Tolerance Approaches for Mixed-Precision Applications
Bo Fang, Siva Hari, Timothy Tsai, Xinyi Li, Ganesh Gopalakrishnan, Ignacio Laguna, Kevin Barker, Ang Li
Variable Bitrate Neural Fields
Towaki Takikawa, Alex Evans, Jonathan Tremblay, Thomas Müller, Morgan McGuire, Alec Jacobson, Sanja Fidler
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
Thomas Müller, Alex Evans, Christoph Schied, Alex Keller
Best Technical Paper, SIGGRAPH 2022
THE BEST INVENTIONS OF 2022, TIME
Ray/Ribbon Intersections
Alexander Reshetov
GATSPI: GPU Accelerated Gate-Level Simulation for Power Improvement
Yanqing Zhang, Haoxing (Mark) Ren, Akshay Sridharan, Brucek Khailany

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

2020

Accelerating Reinforcement Learning through GPU Atari Emulation
Iuri Frosio, Steven Dalton
Locality-Centric Data and Threadblock Management for Massive GPUs
Mahmoud Khairy, Vadim Nikiforov, David Nellans, Timothy G. Rogers
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
Buddy Compression: Enabling Larger Memory for Deep Learning and HPC Workloads on GPUs
Esha Chouske, Michael B. Sullivan, Mike O'Connor, Mattan Erez, Jeff Pool, David Nellans, Steve Keckler
An In-Network Architecture for Accelerating Shared-Memory Multiprocessor Collectives
Benjamin Klenk, Ted Jiang, Greg Thorson, Larry Dennison
NWChem: Past, Present, and Future
Edoardo Aprà, Many others, Oreste Villa, Many others

2019

Near-Memory Data Transformation for Efficient Sparse Matrix Multi-Vector Multiplication
Daichi Fujiki, Niladrish Chatterjee, Donghyuk Lee, Mike O'Connor
Highly-scalable, Physics-informed GANs for Learning Solutions of Stochastic PDEs
Liu Yang, Sean Treichler, Thorsten Kurth, Keno Fischer, David Barajas-Solano, Josh Romero, Valentin Churavy, Alexandre Tartakovsky, Michael Houston, Prabhat, George Karniadakis
Exascale Deep Learning for Scientific Inverse Problems
Nouamane Laanait, Joshua Romero, Junqi Yin, M. Todd Young, Sean Treichler, Vitalii Starchenko, Albina Borisevich, Alex Sergeev, Michael Matheson
Task Bench: A Parameterized Benchmark for Evaluating Parallel Runtime Performance
Elliott Slaughter, Wei Wu, Yuankun Fu, Legend Brandenburg, Nicolai Garcia, Wilhem Kautz, Emily Marx, Kaleb S. Morris, Qinglei Cao, George Bosilca, Seema Mirchandaney, Wonchan Lee, Sean Treichler, Patrick McCormick, Alex Aiken
Optimizing Multi-GPU Parallelization Strategies for Deep Learning Training
Saptadeep Pal, Eiman Ebrahimi, Arslan Zulfiqar, Yaosheng Fu, Victor Zhang, Szymon Migacz, David Nellans, Puneet Gupta
GPU-Accelerated Atari Emulation for Reinforcement Learning
Steven Dalton, Iuri Frosio, Michael Garland
GPU Snapshot: Checkpoint Offloading for GPU-Dense Systems
Kyushick Lee, Michael B. Sullivan, Siva Hari, Timothy Tsai, Steve Keckler, Mattan Erez