Lightweight modular staging: a pragmatic approach to runtime code generation and compiled DSLs

By Tiark Rompf, Martin Odersky. International Conference on Generative Programming: Concepts and Experiences, 2010

439 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Hardware and Architecture, Information Systems

View Paper: https://www.semanticscholar.org/paper/74fd59dabab7b156702b148bb64f8e1ee54f6862

How researchers see this paper

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

In Artificial Intelligence

1. Albert Cohen
2. Nicolas Vasilache
3. Oleksandr Zinenko
4. Andy Davis
5. River Riddle
6. Uday Bondhugula
7. Tatiana Shpeisman
8. Jacques A. Pienaar

In Hardware and Architecture

1. Albert Cohen
2. Nicolas Vasilache
3. Oleksandr Zinenko
4. Andy Davis
5. River Riddle
6. Tatiana Shpeisman
7. Jacques A. Pienaar
8. Mehdi Amini

In Information Systems

1. Chris Lattner
2. Nicolas Vasilache
3. Rastislav Bodík
4. Rajat Monga

Citations in the corpus

• TensorFlow Eager: A Multi-Stage, Python-Embedded DSL for Machine Learning (2019, background): “Not only is specializing functions on input types required for correctness, it also lets us generate optimized graphs — this kind of optimization is well-known, and, indeed, one of the primary motivations for partial evaluation (Jones et al., 1993; Taha, 2004; Rompf & Odersky, 2010).”
• MLIR: Scaling Compiler Infrastructure for Domain Specific Computation (2021, positive): “Lightweight Modular Staging (LMS) [45] is a state of the art framework and runtime code generator, providing a library of core components for generating efficient code and embedding DSLs in Scala.”
• Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions (2018, background): “In other words, the system must be able to offer “abstraction without regret” [71, 20] while conveying rich semantical information available at compilation time; • select appropriate intermediate representations and optimization algorithms that deal with deep parallelism and memory hierarchies, as w”
• MLIR: A Compiler Infrastructure for the End of Moore's Law (2020, background): “Lightweight Modular Staging (LMS) [39] is a state of the art framework and runtime code generator, providing a library of core components for generating efficient code and embedding DSLs in Scala.”
• Programming With Models: Writing Statistical Algorithms for General Model Structures With NIMBLE (2015, methodology): “This was accomplished by allowing these steps to be written separately and using the concepts of specialization and staged evaluation from computer science (Taha and Sheard, 1997; Rompf and Odersky, 2010).”