Sotabase

Career

· Daisytuner
· Post-doctoral researcher, ETH Zurich
· Staff Scientist, Lawrence Livermore National Laboratory
· LLNL
· Doctorate in Computer Science, The Hebrew University of Jerusalem
· Postdoctoral Researcher, The Hebrew University of Jerusalem

Publications (89)

Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks
Journal of machine learning research · 2021
887
cited
Demystifying Parallel and Distributed Deep Learning
ACM Computing Surveys · 2018
772
cited
Neural Code Comprehension: A Learnable Representation of Code Semantics
Neural Information Processing Systems · 2018
271
cited
Augment Your Batch: Improving Generalization Through Instance Repetition
Computer Vision and Pattern Recognition · 2020
250
cited
Deep learning for post-processing ensemble weather forecasts
Philosophical Transactions of the Royal Society A · 2020
189
cited
Data Movement Is All You Need: A Case Study on Optimizing Transformers
Conference on Machine Learning and Systems · 2020
169
cited
ProGraML: A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations
International Conference on Machine Learning · 2021
133
cited
Stateful Dataflow Multigraphs: A Data-Centric Model for Performance Portability on Heterogeneous Architectures
International Conference for High Performance Computing, Networking, Storage and Analysis · 2019
127
cited
Groute: An Asynchronous Multi-GPU Programming Model for Irregular Computations
ACM SIGPLAN Symposium on Principles & Practice of Parallel Programming · 2017
121
cited
A package for OpenCL based heterogeneous computing on clusters with many GPU devices
2010 IEEE International Conference On Cluster Computing Workshops and Posters (CLUSTER WORKSHOPS) · 2010
107
cited
A Modular Benchmarking Infrastructure for High-Performance and Reproducible Deep Learning
IEEE International Parallel and Distributed Processing Symposium · 2019
79
cited
Augment your batch: better training with larger batches
arXiv.org · 2019
78
cited
Clairvoyant Prefetching for Distributed Machine Learning I/O
International Conference for High Performance Computing, Networking, Storage and Analysis · 2021
69
cited
ProGraML: Graph-based Deep Learning for Program Optimization and Analysis
arXiv.org · 2020
67
cited
Taming unbalanced training workloads in deep learning with partial collective operations
ACM SIGPLAN Symposium on Principles & Practice of Parallel Programming · 2019
64
cited
Solution X-ray scattering form factors of supramolecular self-assembled structures.
Langmuir · 2010
59
cited
X+: a comprehensive computationally accelerated structure analysis tool for solution X‐ray scattering from supramolecular self‐assemblies
2010
56
cited
Graph Processing on FPGAs: Taxonomy, Survey, Challenges
arXiv.org · 2019
53
cited
A Data-Centric Approach to Extreme-Scale Ab Initio Dissipative Quantum Transport Simulations
International Conference for High Performance Computing, Networking, Storage and Analysis · 2019
49
cited
Memory access patterns: the missing piece of the multi-GPU puzzle
International Conference for High Performance Computing, Networking, Storage and Analysis · 2015
48
cited
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