Publications

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

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2025

Beyond the Buzz: A Pragmatic Take on Inference Disaggregation
Tiyasa Mitra, Ritika Borkar, Nidhi Bhatia, Ramon Matas, Shivam Raj, Dheevatsa Mudigere, Ritchie Zhao, Maximilian Golub, Arpan Dutta, Sailaja Madduri, Dharmesh Jani, Brian Pharris, Bita Darvish Rouhani
SLIM: One-shot Quantization and Sparsity with Low-rank Approximation for LLM Weight Compression
Mohammad Mozaffari , Amir Yazdanbakhsh, Maryam Mehri Dehnavi

2024

Differentiable GPU-Parallelized Task and Motion Planning
William Shen, Caelan Garrett, Ankit Goyal, Tucker Hermans, Fabio Ramos

2023

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

2022

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

2021

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

2020

Accelerating Reinforcement Learning through GPU Atari Emulation
Iuri Frosio, Steven Dalton
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

2019

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
GPU-Accelerated Atari Emulation for Reinforcement Learning
Steven Dalton, Iuri Frosio, Michael Garland
Buddy Compression: Enabling Larger Memory for Deep Learning and HPC Workloads on GPUs
Esha Choukse, Michael B. Sullivan, Mike O'Connor, Mattan Erez, Jeff Pool, David Nellans, Stephen W. Keckler
Metaoptimization on a Distributed System for Deep Reinforcement Learning
Greg Heinrich, Iuri Frosio

2018

Exascale Deep Learning for Climate Analytics
Thorsten Kurth, Sean Treichler, Joshua Romero, Mayur Mudigonda, Nathan Luehr, Everett Phillips, Ankur Mahesh, Michael Matheson, Jack Deslippe, Massimiliano Fatica, Prabhat, Michael Houston
Structurally Sparsified Backward Propagation for Faster Long Short-Term Memory Training
Maohua Zhu, Jason Clemons, Jeff Pool, Minsoo Rhu, Steve Keckler, Yuan Xie

2017

AdaBatch: Adaptive Batch Sizes for Training Deep Neural Networks
Aditya Devarakonda, Maxim Naumov, Michael Garland
Parallel Jaccard and Related Graph Clustering Techniques
Alexandre Fender, Nahid Emad, Serge Petiton, Joe Eaton, Maxim Naumov
Feedforward and Recurrent Neural Networks Backward Propagation and Hessian in Matrix Form
Maxim Naumov
Parallel Modularity Clustering
Alexandre Fender, Nahid Emad, Serge Petiton, Maxim Naumov

2016

Tensor Contractions with Extended BLAS Kernels on CPU and GPU
Yang Shi, U. N. Niranjan, Animashree Anandkumar, Cris Cecka
vDNN: Virtualized Deep Neural Networks for Scalable, Memory-Efficient Neural Network Design.
Minsoo Rhu, Natalia Gimelshein, Jason Clemons, Arslan Zulfiqar, Steve Keckler
Parallel Spectral Graph Partitioning
Maxim Naumov, Timothy Moon