Tal Ben-Nun

Publication Activity (10 Years)

Years Active: 2009-2024
Publications (10 Years): 92

Top Topics

Weather Forecast

Top Venues

Publications

Lukas Gianinazzi, Alexandros Nikolaos Ziogas, Langwen Huang, Piotr Luczynski, Saleh Ashkboosh, Florian Scheidl, Armon Carigiet, Chio Ge, Nabil Abubaker, Maciej Besta, Tal Ben-Nun, Torsten Hoefler
Arrow Matrix Decomposition: A Novel Approach for Communication-Efficient Sparse Matrix Multiplication. PPoPP (2024)
Yves Baumann, Tal Ben-Nun, Maciej Besta, Lukas Gianinazzi, Torsten Hoefler, Piotr Luczynski
Low-Depth Spatial Tree Algorithms. IPDPS (2024)
Yves Baumann, Tal Ben-Nun, Maciej Besta, Lukas Gianinazzi, Torsten Hoefler, Piotr Luczynski
Low-Depth Spatial Tree Algorithms. CoRR (2024)
Lukas Gianinazzi, Alexandros Nikolaos Ziogas, Langwen Huang, Piotr Luczynski, Saleh Ashkboos, Florian Scheidl, Armon Carigiet, Chio Ge, Nabil Abubaker, Maciej Besta, Tal Ben-Nun, Torsten Hoefler
Arrow Matrix Decomposition: A Novel Approach for Communication-Efficient Sparse Matrix Multiplication. CoRR (2024)
Aiden Grossman, Ludger Paehler, Konstantinos Parasyris, Tal Ben-Nun, Jacob Hegna, William S. Moses, Jose Manuel Monsalve Diaz, Mircea Trofin, Johannes Doerfert
ComPile: A Large IR Dataset from Production Sources. CoRR (2023)
Philipp Schaad, Timo Schneider, Tal Ben-Nun, Alexandru Calotoiu, Alexandros Nikolaos Ziogas, Torsten Hoefler
FuzzyFlow: Leveraging Dataflow To Find and Squash Program Optimization Bugs. SC (2023)
Tal Ben-Nun, Berke Ates, Alexandru Calotoiu, Torsten Hoefler
Bridging Control-Centric and Data-Centric Optimization. CoRR (2023)
Tal Ben-Nun, Lukas Gianinazzi, Torsten Hoefler, Yishai Oltchik
Maximum Flows in Parametric Graph Templates. CoRR (2023)
Roberto L. Castro, Andrei Ivanov, Diego Andrade, Tal Ben-Nun, Basilio B. Fraguela, Torsten Hoefler
VENOM: A Vectorized N: M Format for Unleashing the Power of Sparse Tensor Cores. SC (2023)
Lukas Trümper, Tal Ben-Nun, Philipp Schaad, Alexandru Calotoiu, Torsten Hoefler
Performance Embeddings: A Similarity-Based Transfer Tuning Approach to Performance Optimization. ICS (2023)
Tal Ben-Nun, Lukas Gianinazzi, Torsten Hoefler, Yishai Oltchik
Maximum Flows in Parametric Graph Templates. CIAC (2023)
Anshu Dubey, Tal Ben-Nun, Bradford L. Chamberlain, Bronis R. de Supinski, Damian W. I. Rouson
Performance on HPC Platforms Is Possible Without C++. Comput. Sci. Eng. 25 (5) (2023)
Tal Ben-Nun, Berke Ates, Alexandru Calotoiu, Torsten Hoefler
Bridging Control-Centric and Data-Centric Optimization. CGO (2023)
Niels Gleinig, Tal Ben-Nun, Torsten Hoefler
A Theory of I/O-Efficient Sparse Neural Network Inference. CoRR (2023)
Andrei Ivanov, Nikoli Dryden, Tal Ben-Nun, Saleh Ashkboos, Torsten Hoefler
STen: Productive and Efficient Sparsity in PyTorch. CoRR (2023)
Philipp Schaad, Timo Schneider, Tal Ben-Nun, Alexandru Calotoiu, Alexandros Nikolaos Ziogas, Torsten Hoefler
FuzzyFlow: Leveraging Dataflow To Find and Squash Program Optimization Bugs. CoRR (2023)
Julia Bazinska, Andrei Ivanov, Tal Ben-Nun, Nikoli Dryden, Maciej Besta, Siyuan Shen, Torsten Hoefler
Cached Operator Reordering: A Unified View for Fast GNN Training. CoRR (2023)
Lukas Trümper, Tal Ben-Nun, Philipp Schaad, Alexandru Calotoiu, Torsten Hoefler
Performance Embeddings: A Similarity-based Approach to Automatic Performance Optimization. CoRR (2023)
Roberto L. Castro, Andrei Ivanov, Diego Andrade, Tal Ben-Nun, Basilio B. Fraguela, Torsten Hoefler
VENOM: A Vectorized N: M Format for Unleashing the Power of Sparse Tensor Cores. CoRR (2023)
Alexandros Nikolaos Ziogas, Grzegorz Kwasniewski, Tal Ben-Nun, Timo Schneider, Torsten Hoefler
Deinsum: Practically I/O Optimal Multi-Linear Algebra. SC (2022)
Carl-Johannes Johnsen, Tiziano De Matteis, Tal Ben-Nun, Johannes de Fine Licht, Torsten Hoefler
Temporal Vectorization: A Compiler Approach to Automatic Multi-Pumping. CoRR (2022)
Lukas Gianinazzi, Tal Ben-Nun, Saleh Ashkboos, Yves Baumann, Piotr Luczynski, Torsten Hoefler
The spatial computer: A model for energy-efficient parallel computation. CoRR (2022)
Philipp Schaad, Tal Ben-Nun, Torsten Hoefler
Boosting Performance Optimization with Interactive Data Movement Visualization. SC (2022)
Alexandros Nikolaos Ziogas, Grzegorz Kwasniewski, Tal Ben-Nun, Timo Schneider, Torsten Hoefler
Deinsum: Practically I/O Optimal Multilinear Algebra. CoRR (2022)
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 (2022)
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 Forecasts. NeurIPS (2022)
Carl-Johannes Johnsen, Tiziano De Matteis, Tal Ben-Nun, Johannes de Fine Licht, Torsten Hoefler
Temporal Vectorization: A Compiler Approach to Automatic Multi-Pumping. ICCAD (2022)
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)
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 (2022)
Johannes de Fine Licht, Tiziano De Matteis, Tal Ben-Nun, Andreas Kuster, Oliver Rausch, Manuel Burger, Carl-Johannes Johnsen, Torsten Hoefler
Python FPGA Programming with Data-Centric Multi-Level Design. CoRR (2022)
Philipp Schaad, Tal Ben-Nun, Torsten Hoefler
Boosting Performance Optimization with Interactive Data Movement Visualization. CoRR (2022)
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. SC (2022)
Oliver Rausch, Tal Ben-Nun, Nikoli Dryden, Andrei Ivanov, Shigang Li, Torsten Hoefler
A data-centric optimization framework for machine learning. ICS (2022)
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 (2021)
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)
Roman Böhringer, Nikoli Dryden, Tal Ben-Nun, Torsten Hoefler
Clairvoyant Prefetching for Distributed Machine Learning I/O. CoRR (2021)
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)
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 (2021)
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) (2021)
Oliver Rausch, Tal Ben-Nun, Nikoli Dryden, Andrei Ivanov, Shigang Li, Torsten Hoefler
A Data-Centric Optimization Framework for Machine Learning. CoRR (2021)
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 (2021)
Alexandru Calotoiu, Tal Ben-Nun, Grzegorz Kwasniewski, Johannes de Fine Licht, Timo Schneider, Philipp Schaad, Torsten Hoefler
Lifting C Semantics for Dataflow Optimization. CoRR (2021)
Alexandros Nikolaos Ziogas, Tal Ben-Nun, Timo Schneider, Torsten Hoefler
NPBench: a benchmarking suite for high-performance NumPy. ICS (2021)
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)
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)
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 (2021)
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)
Nikoli Dryden, Roman Böhringer, Tal Ben-Nun, Torsten Hoefler
Clairvoyant prefetching for distributed machine learning I/O. SC (2021)
Lukas Gianinazzi, Maximilian Fries, Nikoli Dryden, Tal Ben-Nun, Maciej Besta, Torsten Hoefler
Learning Combinatorial Node Labeling Algorithms. CoRR (2021)
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)
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)
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 (2021)
Peter Grönquist, Chengyuan Yao, Tal Ben-Nun, Nikoli Dryden, Peter Dueben, Shigang Li, Torsten Hoefler
Deep Learning for Post-Processing Ensemble Weather Forecasts. CoRR (2020)
Maciej Besta, Marc Fischer, Tal Ben-Nun, Dimitri Stanojevic, Johannes de Fine Licht, Torsten Hoefler
Substream-Centric Maximum Matchings on FPGA. CoRR (2020)
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) (2020)
Chris Cummins, Hugh Leather, Zacharias V. Fisches, Tal Ben-Nun, Torsten Hoefler, Michael F. P. O'Boyle
Deep Data Flow Analysis. CoRR (2020)
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 (2020)
Andrei Ivanov, Nikoli Dryden, Tal Ben-Nun, Shigang Li, Torsten Hoefler
Data Movement Is All You Need: A Case Study on Optimizing Transformers. CoRR (2020)
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)
Elad Hoffer, Tal Ben-Nun, Itay Hubara, Niv Giladi, Torsten Hoefler, Daniel Soudry
Augment Your Batch: Improving Generalization Through Instance Repetition. CVPR (2020)
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)
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 (2020)
Tal Ben-Nun, Lukas Gianinazzi, Torsten Hoefler, Yishai Oltchik
Parametric Graph Templates: Properties and Algorithms. CoRR (2020)
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) (2020)
Chris Cummins, Zacharias V. Fisches, Tal Ben-Nun, Torsten Hoefler, Hugh Leather
ProGraML: Graph-based Deep Learning for Program Optimization and Analysis. CoRR (2020)
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 (2020)
Elad Hoffer, Tal Ben-Nun, Itay Hubara, Niv Giladi, Torsten Hoefler, Daniel Soudry
Augment your batch: better training with larger batches. CoRR (2019)
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 (2019)
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)
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 (2019)
Peter Grönquist, Tal Ben-Nun, Nikoli Dryden, Peter Dueben, Luca Lavarini, Shigang Li, Torsten Hoefler
Predicting Weather Uncertainty with Deep Convnets. CoRR (2019)
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 (2019)
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)
Tal Ben-Nun, Torsten Hoefler
Demystifying Parallel and Distributed Deep Learning: An In-depth Concurrency Analysis. ACM Comput. Surv. 52 (4) (2019)
Maciej Besta, Dimitri Stanojevic, Johannes de Fine Licht, Tal Ben-Nun, Torsten Hoefler
Graph Processing on FPGAs: Taxonomy, Survey, Challenges. CoRR (2019)
Alexandros Nikolaos Ziogas, Tal Ben-Nun, Guillermo Indalecio Fernández, Timo Schneider, Mathieu Luisier, Torsten Hoefler
dissipative quantum transport simulations. SC (2019)
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 (2019)
Shigang Li, Tal Ben-Nun, Salvatore Di Girolamo, Dan Alistarh, Torsten Hoefler
Taming Unbalanced Training Workloads in Deep Learning with Partial Collective Operations. CoRR (2019)
Maciej Besta, Marc Fischer, Tal Ben-Nun, Johannes de Fine Licht, Torsten Hoefler
Substream-Centric Maximum Matchings on FPGA. FPGA (2019)
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)
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 (2019)
Tal Ben-Nun, Alice Shoshana Jakobovits, Torsten Hoefler
Neural Code Comprehension: A Learnable Representation of Code Semantics. CoRR (2018)
Michael Sutton, Tal Ben-Nun, Amnon Barak
Optimizing Parallel Graph Connectivity Computation via Subgraph Sampling. IPDPS (2018)
Tal Ben-Nun, Alice Shoshana Jakobovits, Torsten Hoefler
Neural Code Comprehension: A Learnable Representation of Code Semantics. NeurIPS (2018)
Yosuke Oyama, Tal Ben-Nun, Torsten Hoefler, Satoshi Matsuoka
Accelerating Deep Learning Frameworks with Micro-Batches. CLUSTER (2018)
Tal Ben-Nun, Torsten Hoefler
Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis. CoRR (2018)
Yosuke Oyama, Tal Ben-Nun, Torsten Hoefler, Satoshi Matsuoka
μ-cuDNN: Accelerating Deep Learning Frameworks with Micro-Batching. CoRR (2018)
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)
Tal Ben-Nun, Michael Sutton, Sreepathi Pai, Keshav Pingali
Groute: An Asynchronous Multi-GPU Programming Model for Irregular Computations. PPOPP (2017)
Tal Ben-Nun, Amnon Barak, Uri Raviv
Spline-based parallel nonlinear optimization of function sequences. J. Parallel Distributed Comput. (2016)
Michael Sutton, Tal Ben-Nun, Amnon Barak, Sreepathi Pai, Keshav Pingali
Adaptive Work-Efficient Connected Components on the GPU. CoRR (2016)
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) (2016)
Tal Ben-Nun, Ely Levy, Amnon Barak, Eri Rubin
Memory access patterns: the missing piece of the multi-GPU puzzle. SC (2015)
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) (2014)
Tal Ben-Nun, Yoav Etsion, Dror G. Feitelson
Design and implementation of a generic resource sharing virtual time dispatcher. SYSTOR (2010)
Yoav Etsion, Tal Ben-Nun, Dror G. Feitelson
A global scheduling framework for virtualization environments. IPDPS (2009)