Publications

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

Filters

Filters

Publication Year

Research Areas

Events

2025

FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale
Boris Bonev, Thorsten Kurth, Ankur Mahesh, Mauro Bisson, Jean Kossaifi, Karthik Kashinath, Anima Anandkumar, William D. Collins, Mike Pritchard, Alex Keller
Task-Based Tensor Computations on Modern GPUs
Rohan Yadav, Michael Garland, Alex Aiken, Michael Bauer
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
Adaptive Algebraic Reuse of Reordering in Cholesky Factorizations with Dynamic Sparsity Patterns
Behrooz Zarebavani, Danny Kaufman, David Levin, Maryam Mehri Dehnavi
Composing Distributed Computations Through Task and Kernel Fusion
Rohan Yadav, Shiv Sundrum, Wonchan Lee, Michael Garland, Michael Bauer, Alex Aiken, Fredrik Kjolstad
Automatic Tracing in Task-Based Runtime Systems
Rohan Yadav, Michael Bauer, David Broman, Michael Garland, Alex Aiken, Fredrik Kjolstad

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
On the Trend of Resilience for GPU-Dense Systems
Kyushick Lee, Michael B. Sullivan, Siva Hari, Timothy Tsai, Steve Keckler, Mattan Erez
Best of SELSE (Workshop on Silicon Errors in Logic - System Effects)
NVGaze: An Anatomically-Informed Dataset for Low-Latency, Near-Eye Gaze Estimation
Joohwan Kim, Michael Stengel, Alexander Majercik, Shalini De Mello, David Dunn, Samuli Laine, Morgan McGuire, David Luebke
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
DeLTA: GPU Performance Model for Deep Learning Applications with In-depth Memory System Traffic Analysis
Sangkug Lym, Donghyuk Lee, Mike O'Connor, Niladrish Chatterjee, Mattan Erez
A Fast and Robust Method for Avoiding Self-Intersection
Carsten Wächter, Nikolaus Binder
Massively Parallel Path Space Filtering
Nikolaus Binder, Sascha Fricke, Alex Keller
Metaoptimization on a Distributed System for Deep Reinforcement Learning
Greg Heinrich, Iuri Frosio
Massively Parallel Construction of Radix Tree Forests for the Efficient Sampling of Discrete Probability Distributions
Nikolaus Binder, Alex Keller