T
Tal Ben-Nun
Researcher at ETH Zurich
Publications - 87
Citations - 2690
Tal Ben-Nun is an academic researcher from ETH Zurich. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 20, co-authored 71 publications receiving 1657 citations. Previous affiliations of Tal Ben-Nun include École Polytechnique Fédérale de Lausanne & Hebrew University of Jerusalem.
Papers
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Journal ArticleDOI
Demystifying Parallel and Distributed Deep Learning: An In-depth Concurrency Analysis
Tal Ben-Nun,Torsten Hoefler +1 more
TL;DR: The problem of parallelization in DNNs is described from a theoretical perspective, followed by approaches for its parallelization, and potential directions for parallelism in deep learning are extrapolated.
Posted Content
Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis
Tal Ben-Nun,Torsten Hoefler +1 more
TL;DR: The problem of parallelization in DNNs is described from a theoretical perspective, followed by approaches for its parallelization, and potential directions for parallelism in deep learning are extrapolated.
Proceedings ArticleDOI
Augment Your Batch: Improving Generalization Through Instance Repetition
TL;DR: The results show that batch augmentation reduces the number of necessary SGD updates to achieve the same accuracy as the state-of-the-art, and enables faster training and better generalization by allowing more computational resources to be used concurrently.
Proceedings Article
Neural Code Comprehension: A Learnable Representation of Code Semantics
TL;DR: A novel processing technique to learn code semantics, and apply it to a variety of program analysis tasks, and shows that even without fine-tuning, a single RNN architecture and fixed inst2vec embeddings outperform specialized approaches for performance prediction and algorithm classification from raw code.
Posted ContentDOI
Productivity, Portability, Performance: Data-Centric Python
Alexandros Nikolaos Ziogas,Timo Schneider,Tal Ben-Nun,Alexandru Calotoiu,Tiziano De Matteis,Johannes de Fine Licht,Luca Lavarini,Torsten Hoefler +7 more
TL;DR: In this paper, the authors present a workflow that retains Python's high productivity while achieving portable performance across different architectures, including CPU, GPU, FPGA, and the Piz Daint supercomputer.