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D-Index & Metrics

Computer Science

D-Index
30
Citations
4612
World Ranking
13993
National Ranking
5560

Overview

Louis-Noël Pouchet is affiliated with Colorado State University in the United States and specializes in computer science with a focus on hardware and architecture, information systems, and computer networks and communications. Their research also spans areas such as software, electrical and electronic engineering, parallel computing, embedded systems design, software engineering, software testing, formal methods in verification, interconnection networks, and logic programming.

The scientist's recent publications reflect a focus on software engineering and hardware design, emphasizing optimization and formal verification techniques. Notable papers include:

  • "Self-Supervised Learning to Prove Equivalence Between Straight-Line Programs via Rewrite Rules" (2023), published in IEEE Transactions on Software Engineering
  • "Accelerator design with decoupled hardware customizations" (2022), Proceedings of the 59th ACM/IEEE Design Automation Conference
  • "Automatic Hardware Pragma Insertion in High-Level Synthesis: A Non-Linear Programming Approach" (2025), ACM Transactions on Design Automation of Electronic Systems
  • "Equivalence of Dataflow Graphs via Rewrite Rules Using a Graph-to-Sequence Neural Model" (2020), arXiv (Cornell University)
  • "Optimizing Coherence Traffic in Manycore Processors Using Closed-Form Caching/Home Agent Mappings" (2021), IEEE Access

Louis-Noël Pouchet has frequently published in venues such as arXiv (Cornell University), ACM Transactions on Design Automation of Electronic Systems, IEEE Transactions on Software Engineering, the ACM/IEEE Design Automation Conference, and IEEE Access.

Frequent collaborators include Steve Kommrusch, Gabriel Rodríguez, Juan Touriño, Stéphane Pouget, and Théo Barollet, indicating active partnerships primarily in the fields of software engineering and hardware design.

Main topics of their work encompass:

  • Parallel Computing and Optimization Techniques
  • Embedded Systems Design Techniques
  • Software Engineering Research
  • Software Testing and Debugging Techniques
  • Formal Methods in Verification
  • Interconnection Networks and Systems
  • Logic, programming, and type systems

Louis-Noël Pouchet's research contributions bridge both theoretical and applied aspects of computer science, with particular emphasis on improving hardware synthesis processes and software equivalence verification through formal and machine learning methods.

Best Publications

  • SequenceR : Sequence-to-Sequence Learning for End-to-End Program Repair

    Zimin Chen;Steve Kommrusch;Michele Tufano;Louis-Noel Pouchet

  • High-performance code generation for stencil computations on GPU architectures

    Justin Holewinski;Louis-Noël Pouchet;P. Sadayappan

  • The polyhedral model is more widely applicable than you think

    Mohamed-Walid Benabderrahmane;Louis-Noël Pouchet;Albert Cohen;Cédric Bastoul

  • Polly – Polyhedral optimization in LLVM

    Tobias Grosser;Hongbin Zheng;Raghesh Aloor;Andreas Simburger

  • Iterative optimization in the polyhedral model: part ii, multidimensional time

    Louis-Noël Pouchet;Cédric Bastoul;Albert Cohen;John Cavazos

  • Polyhedral-based data reuse optimization for configurable computing

    Louis-Noel Pouchet;Peng Zhang;P. Sadayappan;Jason Cong

  • Iterative Optimization in the Polyhedral Model: Part I, One-Dimensional Time

    Louis-Noel Pouchet;Cedric Bastoul;Albert Cohen;Nicolas Vasilache

  • A stencil compiler for short-vector SIMD architectures

    Tom Henretty;Richard Veras;Franz Franchetti;Louis-Noël Pouchet

  • When polyhedral transformations meet SIMD code generation

    Martin Kong;Richard Veras;Kevin Stock;Franz Franchetti

  • Data layout transformation for stencil computations on short-vector SIMD architectures

    Tom Henretty;Kevin Stock;Louis-Noël Pouchet;Franz Franchetti

  • Loop transformations: convexity, pruning and optimization

    Louis-Noël Pouchet;Uday Bondhugula;Cédric Bastoul;Albert Cohen

  • Automatic Selection of Sparse Matrix Representation on GPUs

    Naser Sedaghati;Te Mu;Louis-Noel Pouchet;Srinivasan Parthasarathy

  • Predictive Modeling in a Polyhedral Optimization Space

    Eunjung Park;John Cavazos;Louis-Noël Pouchet;Louis-Noël Pouchet;Cédric Bastoul

  • Combined Iterative and Model-driven Optimization in an Automatic Parallelization Framework

    Louis-Noël Pouchet;Uday Bondhugula;Cédric Bastoul;Albert Cohen

  • Using machine learning to improve automatic vectorization

    Kevin Stock;Louis-Noël Pouchet;P. Sadayappan

  • A framework for enhancing data reuse via associative reordering

    Kevin Stock;Martin Kong;Tobias Grosser;Louis-Noël Pouchet

  • Dynamic trace-based analysis of vectorization potential of applications

    Justin Holewinski;Ragavendar Ramamurthi;Mahesh Ravishankar;Naznin Fauzia

  • Analytical bounds for optimal tile size selection

    Jun Shirako;Kamal Sharma;Naznin Fauzia;Louis-Noël Pouchet

  • Code generation for parallel execution of a class of irregular loops on distributed memory systems

    Mahesh Ravishankar;John Eisenlohr;Louis-Noël Pouchet;J. Ramanujam

  • Improving polyhedral code generation for high-level synthesis

    Wei Zuo;Peng Li;Deming Chen;Louis-Noel Pouchet

Frequent Co-Authors

P. Sadayappan
P. Sadayappan University of Utah
J. Ramanujam
J. Ramanujam Louisiana State University
Atanas Rountev
Atanas Rountev The Ohio State University
Albert Cohen
Albert Cohen Google (United States)
Sriram Krishnamoorthy
Sriram Krishnamoorthy University of California, Santa Barbara
Vivek Sarkar
Vivek Sarkar Georgia Institute of Technology
Deming Chen
Deming Chen University of Illinois at Urbana-Champaign
Jason Cong
Jason Cong University of California, Los Angeles
Franz Franchetti
Franz Franchetti Carnegie Mellon University
Martin Monperrus
Martin Monperrus Royal Institute of Technology

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