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2020 – today
- 2022
[c27]Alexandru Calotoiu, Tal Ben-Nun, Grzegorz Kwasniewski, Johannes de Fine Licht, Timo Schneider, Philipp Schaad
, Torsten Hoefler:
Lifting C semantics for dataflow optimization. ICS 2022: 17:1-17:13- [c26]Oliver Rausch, Tal Ben-Nun, Nikoli Dryden, Andrei Ivanov, Shigang Li, Torsten Hoefler:
A data-centric optimization framework for machine learning. ICS 2022: 36:1-36:13 - [i35]Tal Ben-Nun, Linus Groner, Florian Deconinck, Tobias Wicky, Eddie Davis, Johann Dahm, Oliver Elbert, Rhea George, Jeremy McGibbon, Lukas Trümper, Elynn Wu, Oliver Fuhrer, Thomas C. Schulthess, Torsten Hoefler:
Productive Performance Engineering for Weather and Climate Modeling with Python. CoRR abs/2205.04148 (2022) - [i34]Lukas Gianinazzi, Tal Ben-Nun, Saleh Ashkboos, Yves Baumann, Piotr Luczynski, Torsten Hoefler:
The spatial computer: A model for energy-efficient parallel computation. CoRR abs/2205.04934 (2022) - [i33]Alexandros Nikolaos Ziogas, Grzegorz Kwasniewski, Tal Ben-Nun, Timo Schneider, Torsten Hoefler:
Deinsum: Practically I/O Optimal Multilinear Algebra. CoRR abs/2206.08301 (2022) - [i32]Saleh Ashkboos, Langwen Huang, Nikoli Dryden, Tal Ben-Nun, Peter Dueben, Lukas Gianinazzi, Luca Kummer, Torsten Hoefler:
ENS-10: A Dataset For Post-Processing Ensemble Weather Forecast. CoRR abs/2206.14786 (2022) - [i31]Philipp Schaad, Tal Ben-Nun, Torsten Hoefler:
Boosting Performance Optimization with Interactive Data Movement Visualization. CoRR abs/2207.07433 (2022) - 2021
- [j8]Torsten Hoefler, Dan Alistarh, Tal Ben-Nun, Nikoli Dryden, Alexandra Peste:
Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks. J. Mach. Learn. Res. 22: 241:1-241:124 (2021) - [j7]Shigang Li, Tal Ben-Nun, Giorgi Nadiradze, Salvatore Di Girolamo, Nikoli Dryden, Dan Alistarh, Torsten Hoefler:
Breaking (Global) Barriers in Parallel Stochastic Optimization With Wait-Avoiding Group Averaging. IEEE Trans. Parallel Distributed Syst. 32(7): 1725-1739 (2021) - [c25]Johannes de Fine Licht, Andreas Kuster, Tiziano De Matteis, Tal Ben-Nun, Dominic Hofer, Torsten Hoefler:
StencilFlow: Mapping Large Stencil Programs to Distributed Spatial Computing Systems. CGO 2021: 315-326 - [c24]Chris Cummins, Zacharias V. Fisches, Tal Ben-Nun, Torsten Hoefler, Michael F. P. O'Boyle, Hugh Leather:
ProGraML: A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations. ICML 2021: 2244-2253 - [c23]Alexandros Nikolaos Ziogas, Tal Ben-Nun, Timo Schneider, Torsten Hoefler:
NPBench: a benchmarking suite for high-performance NumPy. ICS 2021: 63-74 - [c22]Andrei Ivanov, Nikoli Dryden, Tal Ben-Nun, Shigang Li, Torsten Hoefler:
Data Movement Is All You Need: A Case Study on Optimizing Transformers. MLSys 2021 - [c21]Grzegorz Kwasniewski, Tal Ben-Nun, Alexandros Nikolaos Ziogas, Timo Schneider, Maciej Besta, Torsten Hoefler:
On the parallel I/O optimality of linear algebra kernels: near-optimal LU factorization. PPoPP 2021: 463-464 - [c20]Grzegorz Kwasniewski, Marko Kabic, Tal Ben-Nun, Alexandros Nikolaos Ziogas, Jens Eirik Saethre, André Gaillard, Timo Schneider, Maciej Besta, Anton Kozhevnikov, Joost VandeVondele, Torsten Hoefler:
On the parallel I/O optimality of linear algebra kernels: near-optimal matrix factorizations. SC 2021: 70:1-70:15 - [c19]Nikoli Dryden, Roman Böhringer, Tal Ben-Nun, Torsten Hoefler:
Clairvoyant prefetching for distributed machine learning I/O. SC 2021: 92:1-92:15 - [c18]Alexandros Nikolaos Ziogas, Timo Schneider, Tal Ben-Nun, Alexandru Calotoiu, Tiziano De Matteis, Johannes de Fine Licht, Luca Lavarini, Torsten Hoefler:
Productivity, portability, performance: data-centric Python. SC 2021: 95:1-95:13 - [c17]Grzegorz Kwasniewski, Tal Ben-Nun, Lukas Gianinazzi, Alexandru Calotoiu, Timo Schneider, Alexandros Nikolaos Ziogas, Maciej Besta, Torsten Hoefler:
Pebbles, Graphs, and a Pinch of Combinatorics: Towards Tight I/O Lower Bounds for Statically Analyzable Programs. SPAA 2021: 328-339 - [i30]Roman Böhringer, Nikoli Dryden, Tal Ben-Nun, Torsten Hoefler:
Clairvoyant Prefetching for Distributed Machine Learning I/O. CoRR abs/2101.08734 (2021) - [i29]Torsten Hoefler, Dan Alistarh, Tal Ben-Nun, Nikoli Dryden, Alexandra Peste:
Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks. CoRR abs/2102.00554 (2021) - [i28]Grzegorz Kwasniewski, Tal Ben-Nun, Lukas Gianinazzi, Alexandru Calotoiu, Timo Schneider, Alexandros Nikolaos Ziogas, Maciej Besta, Torsten Hoefler:
Pebbles, Graphs, and a Pinch of Combinatorics: Towards Tight I/O Lower Bounds for Statically Analyzable Programs. CoRR abs/2105.07203 (2021) - [i27]Lukas Gianinazzi, Maximilian Fries, Nikoli Dryden, Tal Ben-Nun, Maciej Besta, Torsten Hoefler:
Learning Combinatorial Node Labeling Algorithms. CoRR abs/2106.03594 (2021) - [i26]Alexandros Nikolaos Ziogas, Timo Schneider, Tal Ben-Nun, Alexandru Calotoiu, Tiziano De Matteis, Johannes de Fine Licht, Luca Lavarini, Torsten Hoefler:
Productivity, Portability, Performance: Data-Centric Python. CoRR abs/2107.00555 (2021) - [i25]Grzegorz Kwasniewski, Marko Kabic, Tal Ben-Nun, Alexandros Nikolaos Ziogas, Jens Eirik Saethre, André Gaillard, Timo Schneider, Maciej Besta, Anton Kozhevnikov, Joost VandeVondele, Torsten Hoefler:
On the Parallel I/O Optimality of Linear Algebra Kernels: Near-Optimal Matrix Factorizations. CoRR abs/2108.09337 (2021) - [i24]Oliver Rausch, Tal Ben-Nun, Nikoli Dryden, Andrei Ivanov, Shigang Li, Torsten Hoefler:
A Data-Centric Optimization Framework for Machine Learning. CoRR abs/2110.10802 (2021) - [i23]Alexandru Calotoiu, Tal Ben-Nun, Grzegorz Kwasniewski, Johannes de Fine Licht, Timo Schneider, Philipp Schaad, Torsten Hoefler:
Lifting C Semantics for Dataflow Optimization. CoRR abs/2112.11879 (2021) - 2020
- [j6]Tal Ben-Nun, Michael Sutton, Sreepathi Pai, Keshav Pingali:
Groute: Asynchronous Multi-GPU Programming Model with Applications to Large-scale Graph Processing. ACM Trans. Parallel Comput. 7(3): 18:1-18:27 (2020) - [j5]Maciej Besta, Marc Fischer, Tal Ben-Nun, Dimitri Stanojevic, Johannes de Fine Licht, Torsten Hoefler:
Substream-Centric Maximum Matchings on FPGA. ACM Trans. Reconfigurable Technol. Syst. 13(2): 8:1-8:33 (2020) - [c16]Elad Hoffer, Tal Ben-Nun, Itay Hubara, Niv Giladi, Torsten Hoefler, Daniel Soudry:
Augment Your Batch: Improving Generalization Through Instance Repetition. CVPR 2020: 8126-8135 - [c15]Shigang Li, Tal Ben-Nun, Salvatore Di Girolamo, Dan Alistarh, Torsten Hoefler:
Taming unbalanced training workloads in deep learning with partial collective operations. PPoPP 2020: 45-61 - [c14]Tal Ben-Nun, Todd Gamblin, Daisy S. Hollman, Hari Krishnan, Chris J. Newburn:
Workflows are the New Applications: Challenges in Performance, Portability, and Productivity. P3HPC@SC 2020: 57-69 - [i22]Chris Cummins, Zacharias V. Fisches, Tal Ben-Nun, Torsten Hoefler, Hugh Leather:
ProGraML: Graph-based Deep Learning for Program Optimization and Analysis. CoRR abs/2003.10536 (2020) - [i21]Shigang Li, Tal Ben-Nun, Dan Alistarh, Salvatore Di Girolamo, Nikoli Dryden, Torsten Hoefler:
Breaking (Global) Barriers in Parallel Stochastic Optimization with Wait-Avoiding Group Averaging. CoRR abs/2005.00124 (2020) - [i20]Peter Grönquist, Chengyuan Yao, Tal Ben-Nun, Nikoli Dryden, Peter Dueben, Shigang Li, Torsten Hoefler:
Deep Learning for Post-Processing Ensemble Weather Forecasts. CoRR abs/2005.08748 (2020) - [i19]Andrei Ivanov, Nikoli Dryden, Tal Ben-Nun, Shigang Li, Torsten Hoefler:
Data Movement Is All You Need: A Case Study on Optimizing Transformers. CoRR abs/2007.00072 (2020) - [i18]Grzegorz Kwasniewski, Tal Ben-Nun, Alexandros Nikolaos Ziogas, Timo Schneider, Maciej Besta, Torsten Hoefler:
On the Parallel I/O Optimality of Linear Algebra Kernels: Near-Optimal LU Factorization. CoRR abs/2010.05975 (2020) - [i17]Maciej Besta, Marc Fischer, Tal Ben-Nun, Dimitri Stanojevic, Johannes de Fine Licht, Torsten Hoefler:
Substream-Centric Maximum Matchings on FPGA. CoRR abs/2010.14684 (2020) - [i16]Johannes de Fine Licht, Andreas Kuster, Tiziano De Matteis, Tal Ben-Nun, Dominic Hofer, Torsten Hoefler:
StencilFlow: Mapping Large Stencil Programs to Distributed Spatial Computing Systems. CoRR abs/2010.15218 (2020) - [i15]Tal Ben-Nun, Lukas Gianinazzi, Torsten Hoefler, Yishai Oltchik:
Parametric Graph Templates: Properties and Algorithms. CoRR abs/2011.07001 (2020) - [i14]Chris Cummins, Hugh Leather, Zacharias V. Fisches, Tal Ben-Nun, Torsten Hoefler, Michael F. P. O'Boyle:
Deep Data Flow Analysis. CoRR abs/2012.01470 (2020)
2010 – 2019
- 2019
- [j4]Tal Ben-Nun, Torsten Hoefler:
Demystifying Parallel and Distributed Deep Learning: An In-depth Concurrency Analysis. ACM Comput. Surv. 52(4): 65:1-65:43 (2019) - [c13]Maciej Besta, Marc Fischer, Tal Ben-Nun, Johannes de Fine Licht, Torsten Hoefler:
Substream-Centric Maximum Matchings on FPGA. FPGA 2019: 152-161 - [c12]Tal Ben-Nun, Maciej Besta, Simon Huber, Alexandros Nikolaos Ziogas, Daniel Peter, Torsten Hoefler:
A Modular Benchmarking Infrastructure for High-Performance and Reproducible Deep Learning. IPDPS 2019: 66-77 - [c11]Alexandros Nikolaos Ziogas, Tal Ben-Nun, Guillermo Indalecio Fernández, Timo Schneider, Mathieu Luisier, Torsten Hoefler:
A data-centric approach to extreme-scale ab initio dissipative quantum transport simulations. SC 2019: 1:1-1:13 - [c10]Alexandros Nikolaos Ziogas, Tal Ben-Nun, Guillermo Indalecio Fernández, Timo Schneider, Mathieu Luisier, Torsten Hoefler:
Optimizing the data movement in quantum transport simulations via data-centric parallel programming. SC 2019: 78:1-78:17 - [c9]Tal Ben-Nun, Johannes de Fine Licht, Alexandros Nikolaos Ziogas, Timo Schneider, Torsten Hoefler:
Stateful dataflow multigraphs: a data-centric model for performance portability on heterogeneous architectures. SC 2019: 81:1-81:14 - [i13]Elad Hoffer, Tal Ben-Nun, Itay Hubara, Niv Giladi, Torsten Hoefler, Daniel Soudry:
Augment your batch: better training with larger batches. CoRR abs/1901.09335 (2019) - [i12]Tal Ben-Nun, Maciej Besta, Simon Huber, Alexandros Nikolaos Ziogas, Daniel Peter, Torsten Hoefler:
A Modular Benchmarking Infrastructure for High-Performance and Reproducible Deep Learning. CoRR abs/1901.10183 (2019) - [i11]Tal Ben-Nun, Johannes de Fine Licht, Alexandros Nikolaos Ziogas, Timo Schneider, Torsten Hoefler:
Stateful Dataflow Multigraphs: A Data-Centric Model for High-Performance Parallel Programs. CoRR abs/1902.10345 (2019) - [i10]Maciej Besta, Dimitri Stanojevic, Johannes de Fine Licht, Tal Ben-Nun, Torsten Hoefler:
Graph Processing on FPGAs: Taxonomy, Survey, Challenges. CoRR abs/1903.06697 (2019) - [i9]Shigang Li, Tal Ben-Nun, Salvatore Di Girolamo, Dan Alistarh, Torsten Hoefler:
Taming Unbalanced Training Workloads in Deep Learning with Partial Collective Operations. CoRR abs/1908.04207 (2019) - [i8]Elad Hoffer, Berry Weinstein, Itay Hubara, Tal Ben-Nun, Torsten Hoefler, Daniel Soudry:
Mix & Match: training convnets with mixed image sizes for improved accuracy, speed and scale resiliency. CoRR abs/1908.08986 (2019) - [i7]Peter Grönquist, Tal Ben-Nun, Nikoli Dryden, Peter Dueben, Luca Lavarini, Shigang Li, Torsten Hoefler:
Predicting Weather Uncertainty with Deep Convnets. CoRR abs/1911.00630 (2019) - [i6]Alexandros Nikolaos Ziogas, Tal Ben-Nun, Guillermo Indalecio Fernández, Timo Schneider, Mathieu Luisier, Torsten Hoefler:
Optimizing the Data Movement in Quantum Transport Simulations via Data-Centric Parallel Programming. CoRR abs/1912.08810 (2019) - [i5]Alexandros Nikolaos Ziogas, Tal Ben-Nun, Guillermo Indalecio Fernández, Timo Schneider, Mathieu Luisier, Torsten Hoefler:
A Data-Centric Approach to Extreme-Scale Ab initio Dissipative Quantum Transport Simulations. CoRR abs/1912.10024 (2019) - 2018
- [c8]Yosuke Oyama, Tal Ben-Nun, Torsten Hoefler, Satoshi Matsuoka:
Accelerating Deep Learning Frameworks with Micro-Batches. CLUSTER 2018: 402-412 - [c7]Michael Sutton, Tal Ben-Nun, Amnon Barak:
Optimizing Parallel Graph Connectivity Computation via Subgraph Sampling. IPDPS 2018: 12-21 - [c6]Tal Ben-Nun, Alice Shoshana Jakobovits, Torsten Hoefler:
Neural Code Comprehension: A Learnable Representation of Code Semantics. NeurIPS 2018: 3589-3601 - [i4]Tal Ben-Nun, Torsten Hoefler:
Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis. CoRR abs/1802.09941 (2018) - [i3]Yosuke Oyama, Tal Ben-Nun, Torsten Hoefler, Satoshi Matsuoka:
μ-cuDNN: Accelerating Deep Learning Frameworks with Micro-Batching. CoRR abs/1804.04806 (2018) - [i2]Tal Ben-Nun, Alice Shoshana Jakobovits, Torsten Hoefler:
Neural Code Comprehension: A Learnable Representation of Code Semantics. CoRR abs/1806.07336 (2018) - 2017
- [c5]Tomas Karnagel, Tal Ben-Nun, Matthias Werner, Dirk Habich, Wolfgang Lehner:
Big data causing big (TLB) problems: taming random memory accesses on the GPU. DaMoN 2017: 6:1-6:10 - [c4]Tal Ben-Nun, Michael Sutton, Sreepathi Pai, Keshav Pingali:
Groute: An Asynchronous Multi-GPU Programming Model for Irregular Computations. PPoPP 2017: 235-248 - 2016
- [j3]Avi Ginsburg, Tal Ben-Nun, Roi Asor, Asaf Shemesh, Israel Ringel, Uri Raviv:
Reciprocal Grids: A Hierarchical Algorithm for Computing Solution X-ray Scattering Curves from Supramolecular Complexes at High Resolution. J. Chem. Inf. Model. 56(8): 1518-1527 (2016) - [j2]Tal Ben-Nun, Amnon Barak, Uri Raviv:
Spline-based parallel nonlinear optimization of function sequences. J. Parallel Distributed Comput. 93-94: 132-145 (2016) - [p1]Carsten Weinhold, Adam Lackorzynski, Jan Bierbaum, Martin Küttler, Maksym Planeta, Hermann Härtig, Amnon Shiloh, Ely Levy, Tal Ben-Nun, Amnon Barak, Thomas Steinke, Thorsten Schütt, Jan Fajerski, Alexander Reinefeld, Matthias Lieber, Wolfgang E. Nagel:
FFMK: A Fast and Fault-Tolerant Microkernel-Based System for Exascale Computing. Software for Exascale Computing 2016: 405-426 - [i1]Michael Sutton, Tal Ben-Nun, Amnon Barak, Sreepathi Pai, Keshav Pingali:
Adaptive Work-Efficient Connected Components on the GPU. CoRR abs/1612.01178 (2016) - 2015
- [c3]Tal Ben-Nun, Ely Levy, Amnon Barak, Eri Rubin:
Memory access patterns: the missing piece of the multi-GPU puzzle. SC 2015: 19:1-19:12 - 2014
- [j1]Eri Rubin, Ely Levy, Amnon Barak, Tal Ben-Nun:
MAPS: Optimizing Massively Parallel Applications Using Device-Level Memory Abstraction. ACM Trans. Archit. Code Optim. 11(4): 44:1-44:22 (2014) - 2010
- [c2]Tal Ben-Nun, Yoav Etsion, Dror G. Feitelson:
Design and implementation of a generic resource sharing virtual time dispatcher. SYSTOR 2010
2000 – 2009
- 2009
- [c1]Yoav Etsion, Tal Ben-Nun, Dror G. Feitelson:
A global scheduling framework for virtualization environments. IPDPS 2009: 1-8
Coauthor Index
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