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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
Declarative Concurrent Data Structures
Aun Raza, Hamish Nicholson, Ioanna Tsakalidou, Anna Herlihy, Pra · 2024-04-20 · via cs.DS updates on arXiv.org

Implementing concurrent data structures is challenging and requires a deep understanding of concurrency concepts and careful design to ensure correctness, performance, and scalability. Further, composing operations on two or more concurrent data structures often requires a synchronization wrapper to ensure the operations are applied together atomically, resulting in serialization and, thereby, giving up the performance benefit of the individual data structures. DBMS provides generalized concurrency control (CC) and is a good fit for implementing concurrent data structures. However, DBMSs are over-generalized for this use case, which fails to match the performance of specialized implementations. This paper makes the case for the Declarative Concurrent Data Structures (DCDS) framework for automatically generating concurrent data structures from a serial specification. In DCDS, users declare the attributes and methods needed for their desired data structure through an embedded DSL at design time. DCDS automatically injects CC at build-time, generating a concurrent intermediate representation (IR) compiled into machine code. A declarative interface for designing data structure enables efficient composability through co-optimizing component structures; optimizations are applied to both the composed serial specification and the generated concurrent IR. We realize the DCDS framework in our prototype system Rosti and experimentally show that data structures declared in Rosti can be efficiently composed by co-optimizing their logical functionality and the generated CC protocol. Our evaluation shows that composing a map and a list to create an LRU container can benefit up to 2X performance scalability in Rosti compared to an open-source library. We demonstrate the applicability of DCDS as an in-process OLTP by comparing it with in-memory DBMS, Proteus, and showing up to 2X performance gains.