341 citations · 1.1k across the 43 of their papers we have counts for
9 papers · 1 filter
A Data-Centric Approach to Extreme-Scale Ab initio Dissipative Quantum Transport Simulations
Alexandros Nikolaos Ziogas, Tal Ben-Nun, Guillermo Indalecio Fernández +3
The computational efficiency of a state of the art ab initio quantum transport (QT) solver, capable of revealing the coupled electro-thermal properties of atomically-resolved nano-…
Optimizing the Data Movement in Quantum Transport Simulations via Data-Centric Parallel Programming
Alexandros Nikolaos Ziogas, Tal Ben-Nun, Guillermo Indalecio Fernández +3
Designing efficient cooling systems for integrated circuits (ICs) relies on a deep understanding of the electro-thermal properties of transistors. To shed light on this issue in cu…
Predicting Weather Uncertainty with Deep Convnets
Peter Grönquist, Tal Ben-Nun, Nikoli Dryden +4
Modern weather forecast models perform uncertainty quantification using ensemble prediction systems, which collect nonparametric statistics based on multiple perturbed simulations.…
Mix & Match: training convnets with mixed image sizes for improved accuracy, speed and scale resiliency
Elad Hoffer, Berry Weinstein, Itay Hubara +3
Convolutional neural networks (CNNs) are commonly trained using a fixed spatial image size predetermined for a given model. Although trained on images of aspecific size, it is well…
Taming Unbalanced Training Workloads in Deep Learning with Partial Collective Operations
Shigang Li, Tal Ben-Nun, Salvatore Di Girolamo +2
Load imbalance pervasively exists in distributed deep learning training systems, either caused by the inherent imbalance in learned tasks or by the system itself. Traditional synch…
Graph Processing on FPGAs: Taxonomy, Survey, Challenges
Maciej Besta, Dimitri Stanojevic, Johannes De Fine Licht +2
Graph processing has become an important part of various areas, such as machine learning, computational sciences, medical applications, social network analysis, and many others. Va…
Stateful Dataflow Multigraphs: A Data-Centric Model for Performance Portability on Heterogeneous Architectures
Tal Ben-Nun, Johannes de Fine Licht, Alexandros Nikolaos Ziogas +2
The ubiquity of accelerators in high-performance computing has driven programming complexity beyond the skill-set of the average domain scientist. To maintain performance portabili…
Augment your batch: better training with larger batches
Elad Hoffer, Tal Ben-Nun, Itay Hubara +3
Large-batch SGD is important for scaling training of deep neural networks. However, without fine-tuning hyperparameter schedules, the generalization of the model may be hampered. W…
A Modular Benchmarking Infrastructure for High-Performance and Reproducible Deep Learning
Tal Ben-Nun, Maciej Besta, Simon Huber +3
We introduce Deep500: the first customizable benchmarking infrastructure that enables fair comparison of the plethora of deep learning frameworks, algorithms, libraries, and techni…