Facilitating the search for compositions of program transformations. - Albert Cohen 001; Marc Sigler; Sylvain Girbal; Olivier Temam; David Parello; Nicolas Vasilache (ICS, 2005)

Polyhedral Code Generation in the Real World. - Nicolas Vasilache; Cédric Bastoul; Albert Cohen 001 (CC, 2006)

Violated dependence analysis. - Nicolas Vasilache; Cédric Bastoul; Albert Cohen 001; Sylvain Girbal (ICS, 2006)

Semi-Automatic Composition of Loop Transformations for Deep Parallelism and Memory Hierarchies. - Sylvain Girbal; Nicolas Vasilache; Cédric Bastoul; Albert Cohen 001; David Parello; Marc Sigler; Olivier Temam (Int. J. Parallel Program., 2006)

Automatic Correction of Loop Transformations. - Nicolas Vasilache; Albert Cohen 001; Louis-Noël Pouchet (PACT, 2007)

Iterative Optimization in the Polyhedral Model: Part I, One-Dimensional Time. - Louis-Noël Pouchet; Cédric Bastoul; Albert Cohen 001; Nicolas Vasilache (CGO, 2007)

A mapping path for multi-GPGPU accelerated computers from a portable high level programming abstraction. - Allen Leung; Nicolas Vasilache; Benoît Meister; Muthu Manikandan Baskaran; David Wohlford; Cédric Bastoul; Richard Lethin (GPGPU, 2010)

Loop transformations: convexity, pruning and optimization. - Louis-Noël Pouchet; Uday Bondhugula; Cédric Bastoul; Albert Cohen 001; J. Ramanujam; P. Sadayappan; Nicolas Vasilache (POPL, 2011)

R-Stream Compiler. - Benoît Meister; Nicolas Vasilache; David Wohlford; Muthu Manikandan Baskaran; Allen Leung; Richard Lethin (Encyclopedia of Parallel Computing, 2011)

Efficient and scalable computations with sparse tensors. - Muthu Manikandan Baskaran; Benoît Meister; Nicolas Vasilache; Richard Lethin (HPEC, 2012)

Automatic communication optimizations through memory reuse strategies. - Muthu Manikandan Baskaran; Nicolas Vasilache; Benoît Meister; Richard Lethin (PPoPP, 2012)

Memory reuse optimizations in the R-Stream compiler. - Nicolas Vasilache; Muthu Manikandan Baskaran; Benoît Meister; Richard Lethin (GPGPU@ASPLOS, 2013)

Runnemede: An architecture for Ubiquitous High-Performance Computing. - Nicholas P. Carter; Aditya Agrawal; Shekhar Borkar; Romain Cledat; Howard David; Dave Dunning; Joshua B. Fryman; Ivan Ganev; Roger A. Golliver; Rob C. Knauerhase; Richard Lethin; Benoît Meister; et al. (HPCA, 2013)

Re-Introduction of communication-avoiding FMM-accelerated FFTs with GPU acceleration. - Harper Langston; Muthu Manikandan Baskaran; Benoît Meister; Nicolas Vasilache; Richard Lethin (HPEC, 2013)

Tiling and optimizing time-iterated computations on periodic domains. - Uday Bondhugula; Vinayaka Bandishti; Albert Cohen 001; Guillain Potron; Nicolas Vasilache (PACT, 2014)

A Tale of Three Runtimes. - Nicolas Vasilache; Muthu Manikandan Baskaran; Thomas Henretty; Benoît Meister; Harper Langston; Sanket Tavarageri; Richard Lethin (CoRR, 2014)

Fast Convolutional Nets With fbfft: A GPU Performance Evaluation. - Nicolas Vasilache; Jeff Johnson; Michaël Mathieu; Soumith Chintala; Serkan Piantino; Yann LeCun (ICLR, 2015)

Learning Visual Features from Large Weakly Supervised Data. - Armand Joulin; Laurens van der Maaten; Allan Jabri; Nicolas Vasilache (ECCV (7), 2016)

Diagonal Rescaling For Neural Networks. - Jean Lafond; Nicolas Vasilache; Léon Bottou (CoRR, 2017)

Training Language Models Using Target-Propagation. - Sam Wiseman; Sumit Chopra; Marc'Aurelio Ranzato; Arthur Szlam; Ruoyu Sun 004; Soumith Chintala; Nicolas Vasilache (CoRR, 2017)

Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions. - Nicolas Vasilache; Oleksandr Zinenko; Theodoros Theodoridis; Priya Goyal; Zachary DeVito; William S. Moses; Sven Verdoolaege; Andrew Adams; Albert Cohen 001 (CoRR, 2018)

Openly revisiting derivative-free optimization. - Jérémy Rapin; Pauline Dorval; Jules Pondard; Nicolas Vasilache; Marie-Liesse Cauwet; Camille Couprie; Olivier Teytaud (GECCO (Companion), 2019)

MLIR: A Compiler Infrastructure for the End of Moore's Law. - Chris Lattner; Jacques A. Pienaar; Mehdi Amini; Uday Bondhugula; River Riddle; Albert Cohen 001; Tatiana Shpeisman; Andy Davis; Nicolas Vasilache; Oleksandr Zinenko (CoRR, 2020)

The Next 700 Accelerated Layers: From Mathematical Expressions of Network Computation Graphs to Accelerated GPU Kernels, Automatically. - Nicolas Vasilache; Oleksandr Zinenko; Theodoros Theodoridis; Priya Goyal; Zachary DeVito; William S. Moses; Sven Verdoolaege; Andrew Adams; Albert Cohen 001 (ACM Trans. Archit. Code Optim., 2020)

Progressive Raising in Multi-level IR. - Lorenzo Chelini; Andi Drebes; Oleksandr Zinenko; Albert Cohen 001; Nicolas Vasilache; Tobias Grosser; Henk Corporaal (CGO, 2021)

MLIR: Scaling Compiler Infrastructure for Domain Specific Computation. - Chris Lattner; Mehdi Amini; Uday Bondhugula; Albert Cohen 001; Andy Davis; Jacques A. Pienaar; River Riddle; Tatiana Shpeisman; Nicolas Vasilache; Oleksandr Zinenko (CGO, 2021)

Structured Operations: Modular Design of Code Generators for Tensor Compilers. - Nicolas Vasilache; Oleksandr Zinenko; Aart J. C. Bik; Mahesh Ravishankar; Thomas Raoux; Alexander Belyaev; Matthias Springer; Tobias Gysi; Diego Caballero; Stephan Herhut; Stella Laurenzo; Albert Cohen 001 (LCPC, 2022)

Composable and Modular Code Generation in MLIR: A Structured and Retargetable Approach to Tensor Compiler Construction. - Nicolas Vasilache; Oleksandr Zinenko; Aart J. C. Bik; Mahesh Ravishankar; Thomas Raoux; Alexander Belyaev; Matthias Springer; Tobias Gysi; Diego Caballero; Stephan Herhut; Stella Laurenzo; Albert Cohen 001 (CoRR, 2022)

TinyIREE: An ML Execution Environment for Embedded Systems From Compilation to Deployment. - Hsin-I Cindy Liu; Marius Brehler; Mahesh Ravishankar; Nicolas Vasilache; Ben Vanik; Stella Laurenzo (IEEE Micro, 2022)

Compiler Support for Sparse Tensor Computations in MLIR. - Aart J. C. Bik; Penporn Koanantakool; Tatiana Shpeisman; Nicolas Vasilache; Bixia Zheng; Fredrik Kjolstad (ACM Trans. Archit. Code Optim., 2022)

Code Generation for In-Place Stencils. - Mohamed Essadki; Bertrand Michel; Bruno Maugars; Oleksandr Zinenko; Nicolas Vasilache; Albert Cohen 001 (CGO, 2023)

nelli: a lightweight frontend for MLIR. - Maksim Levental; Alok Kamatar; Ryan Chard; Nicolas Vasilache; Kyle Chard; Ian T. Foster (CoRR, 2023)