A global scheduling framework for virtualization environments. - Yoav Etsion; Tal Ben-Nun; Dror G. Feitelson (IPDPS, 2009)

Design and implementation of a generic resource sharing virtual time dispatcher. - Tal Ben-Nun; Yoav Etsion; Dror G. Feitelson (SYSTOR, 2010)

MAPS: Optimizing Massively Parallel Applications Using Device-Level Memory Abstraction. - Eri Rubin; Ely Levy; Amnon Barak; Tal Ben-Nun (ACM Trans. Archit. Code Optim., 2014)

Memory access patterns: the missing piece of the multi-GPU puzzle. - Tal Ben-Nun; Ely Levy; Amnon Barak; Eri Rubin (SC, 2015)

Adaptive Work-Efficient Connected Components on the GPU. - Michael Sutton 001; Tal Ben-Nun; Amnon Barak; Sreepathi Pai; Keshav Pingali (CoRR, 2016)

Reciprocal Grids: A Hierarchical Algorithm for Computing Solution X-ray Scattering Curves from Supramolecular Complexes at High Resolution. - Avi Ginsburg; Tal Ben-Nun; Roi Asor; Asaf Shemesh; Israel Ringel; Uri Raviv (J. Chem. Inf. Model., 2016)

Spline-based parallel nonlinear optimization of function sequences. - Tal Ben-Nun; Amnon Barak; Uri Raviv (J. Parallel Distributed Comput., 2016)

Memory-Oriented Programming : A Data-Centric Programming Model for Systems with Multiple Parallel Accelerators (שער נוסף בעברית: תכנות מונחה זיכרון : מודל תכנות עבור מערכות מרובות מאיצים מקביליים.). - Tal Ben-Nun (2016)

FFMK: A Fast and Fault-Tolerant Microkernel-Based System for Exascale Computing. - Carsten Weinhold; Adam Lackorzynski; Jan Bierbaum; Martin Küttler; Maksym Planeta; Hermann Härtig; Amnon Shiloh; Ely Levy; Tal Ben-Nun; Amnon Barak; Thomas Steinke 001; Thorsten Schütt; et al. (Software for Exascale Computing, 2016)

Big data causing big (TLB) problems: taming random memory accesses on the GPU. - Tomas Karnagel; Tal Ben-Nun; Matthias Werner 004; Dirk Habich; Wolfgang Lehner (DaMoN, 2017)

Groute: An Asynchronous Multi-GPU Programming Model for Irregular Computations. - Tal Ben-Nun; Michael Sutton 001; Sreepathi Pai; Keshav Pingali (PPoPP, 2017)

Accelerating Deep Learning Frameworks with Micro-Batches. - Yosuke Oyama; Tal Ben-Nun; Torsten Hoefler; Satoshi Matsuoka (CLUSTER, 2018)

Optimizing Parallel Graph Connectivity Computation via Subgraph Sampling. - Michael Sutton 001; Tal Ben-Nun; Amnon Barak (IPDPS, 2018)

Neural Code Comprehension: A Learnable Representation of Code Semantics. - Tal Ben-Nun; Alice Shoshana Jakobovits; Torsten Hoefler (NeurIPS, 2018)

μ-cuDNN: Accelerating Deep Learning Frameworks with Micro-Batching. - Yosuke Oyama; Tal Ben-Nun; Torsten Hoefler; Satoshi Matsuoka (CoRR, 2018)

Substream-Centric Maximum Matchings on FPGA. - Maciej Besta; Marc Fischer; Tal Ben-Nun; Johannes de Fine Licht; Torsten Hoefler (FPGA, 2019)

A Modular Benchmarking Infrastructure for High-Performance and Reproducible Deep Learning. - Tal Ben-Nun; Maciej Besta; Simon Huber; Alexandros Nikolaos Ziogas; Daniel Peter; Torsten Hoefler (IPDPS, 2019)

Stateful dataflow multigraphs: a data-centric model for performance portability on heterogeneous architectures. - Tal Ben-Nun; Johannes de Fine Licht; Alexandros Nikolaos Ziogas; Timo Schneider; Torsten Hoefler (SC, 2019)

A data-centric approach to extreme-scale ab initio dissipative quantum transport simulations. - Alexandros Nikolaos Ziogas; Tal Ben-Nun; Guillermo Indalecio Fernández; Timo Schneider; Mathieu Luisier; Torsten Hoefler (SC, 2019)

Optimizing the data movement in quantum transport simulations via data-centric parallel programming. - Alexandros Nikolaos Ziogas; Tal Ben-Nun; Guillermo Indalecio Fernández; Timo Schneider; Mathieu Luisier; Torsten Hoefler (SC, 2019)

Augment your batch: better training with larger batches. - Elad Hoffer; Tal Ben-Nun; Itay Hubara; Niv Giladi; Torsten Hoefler; Daniel Soudry (CoRR, 2019)

Stateful Dataflow Multigraphs: A Data-Centric Model for High-Performance Parallel Programs. - Tal Ben-Nun; Johannes de Fine Licht; Alexandros Nikolaos Ziogas; Timo Schneider; Torsten Hoefler (CoRR, 2019)

Graph Processing on FPGAs: Taxonomy, Survey, Challenges. - Maciej Besta; Dimitri Stanojevic; Johannes de Fine Licht; Tal Ben-Nun; Torsten Hoefler (CoRR, 2019)

Mix & Match: training convnets with mixed image sizes for improved accuracy, speed and scale resiliency. - Elad Hoffer; Berry Weinstein; Itay Hubara; Tal Ben-Nun; Torsten Hoefler; Daniel Soudry (CoRR, 2019)

Predicting Weather Uncertainty with Deep Convnets. - Peter Grönquist; Tal Ben-Nun; Nikoli Dryden; Peter Dueben; Luca Lavarini; Shigang Li 002; Torsten Hoefler (CoRR, 2019)

A Data-Centric Approach to Extreme-Scale Ab initio Dissipative Quantum Transport Simulations. - Alexandros Nikolaos Ziogas; Tal Ben-Nun; Guillermo Indalecio Fernández; Timo Schneider; Mathieu Luisier; Torsten Hoefler (CoRR, 2019)

Demystifying Parallel and Distributed Deep Learning: An In-depth Concurrency Analysis. - Tal Ben-Nun; Torsten Hoefler (ACM Comput. Surv., 2019)

Augment Your Batch: Improving Generalization Through Instance Repetition. - Elad Hoffer; Tal Ben-Nun; Itay Hubara; Niv Giladi; Torsten Hoefler; Daniel Soudry (CVPR, 2020)

Taming unbalanced training workloads in deep learning with partial collective operations. - Shigang Li 002; Tal Ben-Nun; Salvatore Di Girolamo; Dan Alistarh; Torsten Hoefler (PPoPP, 2020)

Workflows are the New Applications: Challenges in Performance, Portability, and Productivity. - Tal Ben-Nun; Todd Gamblin; Daisy S. Hollman; Hari Krishnan; Chris J. Newburn (P3HPC@SC, 2020)

ProGraML: Graph-based Deep Learning for Program Optimization and Analysis. - Chris Cummins; Zacharias V. Fisches; Tal Ben-Nun; Torsten Hoefler; Hugh Leather (CoRR, 2020)

Breaking (Global) Barriers in Parallel Stochastic Optimization with Wait-Avoiding Group Averaging. - Shigang Li 002; Tal Ben-Nun; Dan Alistarh; Salvatore Di Girolamo; Nikoli Dryden; Torsten Hoefler (CoRR, 2020)

Deep Learning for Post-Processing Ensemble Weather Forecasts. - Peter Grönquist; Chengyuan Yao; Tal Ben-Nun; Nikoli Dryden; Peter Dueben; Shigang Li 002; Torsten Hoefler (CoRR, 2020)

Parametric Graph Templates: Properties and Algorithms. - Tal Ben-Nun; Lukas Gianinazzi; Torsten Hoefler; Yishai Oltchik (CoRR, 2020)

Deep Data Flow Analysis. - Chris Cummins; Hugh Leather; Zacharias V. Fisches; Tal Ben-Nun; Torsten Hoefler; Michael F. P. O'Boyle (CoRR, 2020)

Groute: Asynchronous Multi-GPU Programming Model with Applications to Large-scale Graph Processing. - Tal Ben-Nun; Michael Sutton 001; Sreepathi Pai; Keshav Pingali (ACM Trans. Parallel Comput., 2020)

Substream-Centric Maximum Matchings on FPGA. - Maciej Besta; Marc Fischer; Tal Ben-Nun; Dimitri Stanojevic; Johannes de Fine Licht; Torsten Hoefler (ACM Trans. Reconfigurable Technol. Syst., 2020)

StencilFlow: Mapping Large Stencil Programs to Distributed Spatial Computing Systems. - Johannes de Fine Licht; Andreas Kuster; Tiziano De Matteis; Tal Ben-Nun; Dominic Hofer; Torsten Hoefler (CGO, 2021)

ProGraML: A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations. - Chris Cummins; Zacharias V. Fisches; Tal Ben-Nun; Torsten Hoefler; Michael F. P. O'Boyle; Hugh Leather (ICML, 2021)

NPBench: a benchmarking suite for high-performance NumPy. - Alexandros Nikolaos Ziogas; Tal Ben-Nun; Timo Schneider; Torsten Hoefler (ICS, 2021)

Data Movement Is All You Need: A Case Study on Optimizing Transformers. - Andrei Ivanov; Nikoli Dryden; Tal Ben-Nun; Shigang Li 002; Torsten Hoefler (MLSys, 2021)

On the parallel I/O optimality of linear algebra kernels: near-optimal LU factorization. - Grzegorz Kwasniewski; Tal Ben-Nun; Alexandros Nikolaos Ziogas; Timo Schneider; Maciej Besta; Torsten Hoefler (PPoPP, 2021)

Clairvoyant prefetching for distributed machine learning I/O. - Nikoli Dryden; Roman Böhringer; Tal Ben-Nun; Torsten Hoefler (SC, 2021)

On the parallel I/O optimality of linear algebra kernels: near-optimal matrix factorizations. - 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 (SC, 2021)

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 (SC, 2021)

Pebbles, Graphs, and a Pinch of Combinatorics: Towards Tight I/O Lower Bounds for Statically Analyzable Programs. - Grzegorz Kwasniewski; Tal Ben-Nun; Lukas Gianinazzi; Alexandru Calotoiu; Timo Schneider; Alexandros Nikolaos Ziogas; Maciej Besta; Torsten Hoefler (SPAA, 2021)

Learning Combinatorial Node Labeling Algorithms. - Lukas Gianinazzi; Maximilian Fries; Nikoli Dryden; Tal Ben-Nun; Maciej Besta; Torsten Hoefler (CoRR, 2021)

Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks. - Torsten Hoefler; Dan Alistarh; Tal Ben-Nun; Nikoli Dryden; Alexandra Peste (J. Mach. Learn. Res., 2021)

Breaking (Global) Barriers in Parallel Stochastic Optimization With Wait-Avoiding Group Averaging. - Shigang Li 002; Tal Ben-Nun; Giorgi Nadiradze; Salvatore Di Girolamo; Nikoli Dryden; Dan Alistarh; Torsten Hoefler (IEEE Trans. Parallel Distributed Syst., 2021)

Temporal Vectorization: A Compiler Approach to Automatic Multi-Pumping. - Carl-Johannes Johnsen; Tiziano De Matteis; Tal Ben-Nun; Johannes de Fine Licht; Torsten Hoefler (ICCAD, 2022)

Lifting C semantics for dataflow optimization. - Alexandru Calotoiu; Tal Ben-Nun; Grzegorz Kwasniewski; Johannes de Fine Licht; Timo Schneider; Philipp Schaad; Torsten Hoefler (ICS, 2022)

A data-centric optimization framework for machine learning. - Oliver Rausch; Tal Ben-Nun; Nikoli Dryden; Andrei Ivanov; Shigang Li 002; Torsten Hoefler (ICS, 2022)

ENS-10: A Dataset For Post-Processing Ensemble Weather Forecasts. - Saleh Ashkboos; Langwen Huang; Nikoli Dryden; Tal Ben-Nun; Peter Dueben; Lukas Gianinazzi; Luca Kummer; Torsten Hoefler (NeurIPS, 2022)

Productive Performance Engineering for Weather and Climate Modeling with Python. - 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; et al. (SC, 2022)

Boosting Performance Optimization with Interactive Data Movement Visualization. - Philipp Schaad; Tal Ben-Nun; Torsten Hoefler (SC, 2022)

Deinsum: Practically I/O Optimal Multi-Linear Algebra. - Alexandros Nikolaos Ziogas; Grzegorz Kwasniewski; Tal Ben-Nun; Timo Schneider; Torsten Hoefler (SC, 2022)

The spatial computer: A model for energy-efficient parallel computation. - Lukas Gianinazzi; Tal Ben-Nun; Saleh Ashkboos; Yves Baumann; Piotr Luczynski; Torsten Hoefler (CoRR, 2022)

Deinsum: Practically I/O Optimal Multilinear Algebra. - Alexandros Nikolaos Ziogas; Grzegorz Kwasniewski; Tal Ben-Nun; Timo Schneider; Torsten Hoefler (CoRR, 2022)

ENS-10: A Dataset For Post-Processing Ensemble Weather Forecast. - Saleh Ashkboos; Langwen Huang; Nikoli Dryden; Tal Ben-Nun; Peter Dueben; Lukas Gianinazzi; Luca Kummer; Torsten Hoefler (CoRR, 2022)

Python FPGA Programming with Data-Centric Multi-Level Design. - Johannes de Fine Licht; Tiziano De Matteis; Tal Ben-Nun; Andreas Kuster; Oliver Rausch; Manuel Burger; Carl-Johannes Johnsen; Torsten Hoefler (CoRR, 2022)

Bridging Control-Centric and Data-Centric Optimization. - Tal Ben-Nun; Berke Ates; Alexandru Calotoiu; Torsten Hoefler (CGO, 2023)

Maximum Flows in Parametric Graph Templates. - Tal Ben-Nun; Lukas Gianinazzi; Torsten Hoefler; Yishai Oltchik (CIAC, 2023)

Performance Embeddings: A Similarity-Based Transfer Tuning Approach to Performance Optimization. - Lukas Trümper; Tal Ben-Nun; Philipp Schaad; Alexandru Calotoiu; Torsten Hoefler (ICS, 2023)

VENOM: A Vectorized N: M Format for Unleashing the Power of Sparse Tensor Cores. - Roberto L. Castro; Andrei Ivanov; Diego Andrade; Tal Ben-Nun; Basilio B. Fraguela; Torsten Hoefler (SC, 2023)

FuzzyFlow: Leveraging Dataflow To Find and Squash Program Optimization Bugs. - Philipp Schaad; Timo Schneider; Tal Ben-Nun; Alexandru Calotoiu; Alexandros Nikolaos Ziogas; Torsten Hoefler (SC, 2023)

A Theory of I/O-Efficient Sparse Neural Network Inference. - Niels Gleinig; Tal Ben-Nun; Torsten Hoefler (CoRR, 2023)

Performance Embeddings: A Similarity-based Approach to Automatic Performance Optimization. - Lukas Trümper; Tal Ben-Nun; Philipp Schaad; Alexandru Calotoiu; Torsten Hoefler (CoRR, 2023)

STen: Productive and Efficient Sparsity in PyTorch. - Andrei Ivanov; Nikoli Dryden; Tal Ben-Nun; Saleh Ashkboos; Torsten Hoefler (CoRR, 2023)

Cached Operator Reordering: A Unified View for Fast GNN Training. - Julia Bazinska; Andrei Ivanov; Tal Ben-Nun; Nikoli Dryden; Maciej Besta; Siyuan Shen; Torsten Hoefler (CoRR, 2023)

ComPile: A Large IR Dataset from Production Sources. - Aiden Grossman; Ludger Paehler; Konstantinos Parasyris; Tal Ben-Nun; Jacob Hegna; William S. Moses; Jose Manuel Monsalve Diaz; Mircea Trofin; Johannes Doerfert (CoRR, 2023)

Performance on HPC Platforms Is Possible Without C++. - Anshu Dubey; Tal Ben-Nun; Bradford L. Chamberlain; Bronis R. de Supinski; Damian W. I. Rouson (Comput. Sci. Eng., 2023)

Arrow Matrix Decompositions. - Lukas Gianinazzi; Alexandros Nikolaos Ziogas; Piotr Luczynski; Saleh Ashkboosh; Langwen Huang; Florian Scheidl; Chio Ge; Armon Carigiet; Maciej Besta; Tal Ben-Nun; Torsten Hoefler (Zenodo, 2023)

Low-Depth Spatial Tree Algorithms. - Yves Baumann; Tal Ben-Nun; Maciej Besta; Lukas Gianinazzi; Torsten Hoefler; Piotr Luczynski (IPDPS, 2024)

Arrow Matrix Decomposition: A Novel Approach for Communication-Efficient Sparse Matrix Multiplication. - 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 (PPoPP, 2024)

Autonomous Execution for Multi-GPU Systems: Compiler Support. - Javid Baydamirli; Tal Ben-Nun; Didem Unat (SC Workshops, 2024)

Lion Cub: Minimizing Communication Overhead in Distributed Lion. - Satoki Ishikawa; Tal Ben-Nun; Brian Van Essen; Rio Yokota; Nikoli Dryden (CoRR, 2024)

Energy-Optimal and Low-Depth Algorithmic Primitives for Spatial Dataflow Architectures. - Lukas Gianinazzi; Tal Ben-Nun; Maciej Besta; Saleh Ashkboos; Yves Baumann; Piotr Luczynski; Torsten Hoefler (IPDPS, 2025)

Speculative Diffusion Decoding: Accelerating Language Generation through Diffusion. - Jacob K. Christopher; Brian R. Bartoldson; Tal Ben-Nun; Michael Cardei; Bhavya Kailkhura; Ferdinando Fioretto (NAACL (Long Papers), 2025)

A Unifying Framework to Enable Artificial Intelligence in High-Performance Computing Workflows. - Jens Domke; Mohamed Wahib; Anshu Dubey; Tal Ben-Nun; Erik W. Draeger (Comput. Sci. Eng., 2025)

Trajectory Balance with Asynchrony: Decoupling Exploration and Learning for Fast, Scalable LLM Post-Training. - Brian R. Bartoldson; Siddarth Venkatraman; James Diffenderfer; Moksh Jain; Tal Ben-Nun; Seanie Lee; Minsu Kim 004; Johan S. Obando-Ceron; Yoshua Bengio; Bhavya Kailkhura (CoRR, 2025)

BOOM: Benchmarking Out-Of-distribution Molecular Property Predictions of Machine Learning Models. - Evan R. Antoniuk; Shehtab Zaman; Tal Ben-Nun; Peggy Li; James Diffenderfer; Busra Demirci; Obadiah Smolenski; Tim Hsu; Anna M. Hiszpanski; Kenneth Chiu; Bhavya Kailkhura; Brian Van Essen (CoRR, 2025)

Leveraging AI for Productive and Trustworthy HPC Software: Challenges and Research Directions. - Keita Teranishi; Harshitha Menon; William F. Godoy; Prasanna Balaprakash; David Bau; Tal Ben-Nun; Abhinav Bhatele; Franz Franchetti; Michael E. Franusich; Todd Gamblin; Giorgis Georgakoudis; Tom Goldstein; et al. (CoRR, 2025)

Productivity, Portability, Performance, and Reproducibility: Data-Centric Python. - Alexandros Nikolaos Ziogas; Timo Schneider; Tal Ben-Nun; Alexandru Calotoiu; Tiziano De Matteis; Johannes de Fine Licht; Luca Lavarini; Torsten Hoefler (IEEE Trans. Parallel Distributed Syst., 2025)

Modeling Code: Is Text All You Need? - Daniel Nichols; Konstantinos Parasyris; Harshitha Menon; Brian R. Bartoldson; Giorgis Georgakoudis; Tal Ben-Nun; Abhinav Bhatele (CoRR, 2025)