Polly - Performing Polyhedral Optimizations on a Low-Level Intermediate Representation

By Tobias Grosser, Armin Größlinger, Christian Lengauer. Parallel Processing Letters, 2012

445 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Hardware and Architecture

View Paper: https://www.semanticscholar.org/paper/3d64988b882541afa8f03f912229fbba49c82e74

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Torsten Hoefler
2. Tal Ben-Nun
3. Shoaib Kamil
4. Saman P. Amarasinghe
5. Joel A. E. Andersson
6. Thomas Neumann

In Hardware and Architecture

1. Torsten Hoefler
2. Tal Ben-Nun
3. Yunming Zhang
4. Andrei Ivanov
5. Albert Cohen
6. Shoaib Kamil
7. River Riddle
8. Saman P. Amarasinghe

Citations in the corpus

• MLIR: Scaling Compiler Infrastructure for Domain Specific Computation (2021, unknown): “However, polyhedral transformations require raising into a representation often drastically different from the original [31], [32].”
• Data Movement Is All You Need: A Case Study on Optimizing Transformers (2020, background): “Optimizing compilers, most notably components that specialize in polyhedral programs (Grosser et al., 2012; Bondhugula et al., 2008), apply loop transformations (e.g., tiling, skewing) that belong to the class of data movement optimization.”
• Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code (2018, methodology): “Some of them are designed for specific domains (such as Tensor Comprehensions and PolyMage), while Pluto, PENCIL, and Polly are more general.”
• MLIR: A Compiler Infrastructure for the End of Moore's Law (2020, background): “However, polyhedral transformations require raising into a representation often drastically different from the original [20, 10].”