Albert Cohen
Albert is a research scientist at Google. An alumnus of École Normale Supérieure de Lyon and the University of Versailles, he has been a research scientist at Inria, a visiting scholar at the University of Illinois, an invited professor at Philips Research, and a visiting scientist at Facebook Artificial Intelligence Research. Albert Cohen works on parallelizing and optimizing compilers, machine learning compilers, parallel and synchronous programming languages, with applications to high-performance computing, artificial intelligence and reactive control.
Authored Publications
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Google
Code Generation for Data-Dependent Stencils
Mohammed Essadki
Bertrand Michel
Bruno Maugars
Oleksandr Zinenko
Nicolas Vasilache
CGO, IEEE (2023)
Structured Operations: Modular Design of Code Generators for Tensor Compilers
Nicolas Vasilache
Oleksandr Zinenko
Aart Bik
Mahesh Ravishankar
Thomas Raoux
Alexander Belyaev
Matthias Springer
Tobias Gysi
Diego Caballero
Stephan Herhut
Stella Laurenzo
LCPC 2022, Springer (2023)
RL4ReAl: Reinforcement Learning for Register Allocation
S. VenkataKeerthy
Siddharth Jain
Anilava Kundu
Rohit Aggarwal
Ramakrishna Upadrasta
CC 2023, ACM
Autotuning Convolutions is Easier Than You Think
Nicolas Tollenaere
Guillaume Iooss
Stéphane Pouget
Hugo Brunie
Christophe Guillon
P. Sadayappan
Fabrice Rastello
ACM TACO (2022)
Reconciling Optimization With Secure Compilation
Son Tuan Vu
Arnaud De Grandmaison
Christophe Guillon
Karine Heydemann
Proceedings of the ACM (PACMPL) (2021)
Efficient Convolution Optimisation by Composing Microkernels
Nicolas Tollenaere
Auguste Olivry
Guillaume Iooss
Hugo Brunie
P Sadayappan
Fabrice Rastello
INRIA (2021)
