Graph IRs for Impure Higher-Order Languages (Technical Report)
September 15, 2023 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Oliver BraΔevac, Guannan Wei, Songlin Jia, Supun Abeysinghe, Yuxuan Jiang, Yuyan Bao, Tiark Rompf
arXiv ID
2309.08118
Category
cs.PL: Programming Languages
Citations
3
Venue
arXiv.org
Last Checked
4 months ago
Abstract
This is a companion report for the OOPSLA 2023 paper of the same title, presenting a detailed end-to-end account of the $Ξ»^*_{\mathsf{G}}$ graph IR, at a level of detail beyond a regular conference paper. Our first concern is adequacy and soundness of $Ξ»^*_{\mathsf{G}}$, which we derive from a direct-style imperative functional language (a variant of Bao et al.'s $Ξ»^*$-calculus with reachability types and a simple effect system) by a series of type-preserving translations into a calculus in monadic normalform (MNF). Static reachability types and effects entirely inform $Ξ»^*_{\mathsf{G}}$'s dependency synthesis. We argue for its adequacy by proving its functional properties along with dependency safety via progress and preservation lemmas with respect to a notion of call-by-value (CBV) reduction that checks the observed order of effects. Our second concern is establishing the correctness of $Ξ»^*_{\mathsf{G}}$'s equational rules that drive compiler optimizations (e.g., DCE, $Ξ»$-hoisting, etc.), by proving contextual equivalence using logical relations. A key insight is that the functional properties of dependency synthesis permit a logical relation on $Ξ»^*_{\mathsf{G}}$ in MNF in terms of previously developed logical relations for the direct-style $Ξ»^*$-calculus. Finally, we also include a longer version of the conference paper's section on code generation and code motion for $Ξ»^*_{\mathsf{G}}$ as implemented in Scala~LMS.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Programming Languages
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
R.I.P.
π»
Ghosted
Glow: Graph Lowering Compiler Techniques for Neural Networks
R.I.P.
π»
Ghosted
Learnable Programming: Blocks and Beyond
R.I.P.
π»
Ghosted
Scenic: A Language for Scenario Specification and Scene Generation
R.I.P.
π»
Ghosted
Vandal: A Scalable Security Analysis Framework for Smart Contracts
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted