Publications | Research

19 results found
Artificial Intelligence and Machine Learning
High Performance Computing

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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

CVPR

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

SIGGRAPH

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