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PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
Implementing Explicit and Finding Implicit Sharing in Emb...
Oleg Kiselyov · 2011-09-05 · via cs.DS updates on arXiv.org

Aliasing, or sharing, is prominent in many domains, denoting that two differently-named objects are in fact identical: a change in one object (memory cell, circuit terminal, disk block) is instantly reflected in the other. Languages for modelling such domains should let the programmer explicitly define the sharing among objects or expressions. A DSL compiler may find other identical expressions and share them, implicitly. Such common subexpression elimination is crucial to the efficient implementation of DSLs. Sharing is tricky in embedded DSL, since host aliasing may correspond to copying of the underlying objects rather than their sharing. This tutorial summarizes discussions of implementing sharing in Haskell DSLs for automotive embedded systems and hardware description languages. The technique has since been used in a Haskell SAT solver and the DSL for music synthesis. We demonstrate the embedding in pure Haskell of a simple DSL with a language form for explicit sharing. The DSL also has implicit sharing, implemented via hash-consing. Explicit sharing greatly speeds up hash-consing. The seemingly imperative nature of hash-consing is hidden beneath a simple combinator language. The overall implementation remains pure functional and easy to reason about.