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cs.DS updates on arXiv.org

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
An Associativity Threshold Phenomenon in Set-Associative ...
Michael A. Bender, Rathish Das, Martín Farach-Colton, Guido Tagl · 2023-04-11 · via cs.DS updates on arXiv.org

In an $α$-way set-associative cache, the cache is partitioned into disjoint sets of size $α$, and each item can only be cached in one set, typically selected via a hash function. Set-associative caches are widely used and have many benefits, e.g., in terms of latency or concurrency, over fully associative caches, but they often incur more cache misses. As the set size $α$ decreases, the benefits increase, but the paging costs worsen. In this paper we characterize the performance of an $α$-way set-associative LRU cache of total size $k$, as a function of $α= α(k)$. We prove the following, assuming that sets are selected using a fully random hash function: - For $α= ω(\log k)$, the paging cost of an $α$-way set-associative LRU cache is within additive $O(1)$ of that a fully-associative LRU cache of size $(1-o(1))k$, with probability $1 - 1/\operatorname{poly}(k)$, for all request sequences of length $\operatorname{poly}(k)$. - For $α= o(\log k)$, and for all $c = O(1)$ and $r = O(1)$, the paging cost of an $α$-way set-associative LRU cache is not within a factor $c$ of that a fully-associative LRU cache of size $k/r$, for some request sequence of length $O(k^{1.01})$. - For $α= ω(\log k)$, if the hash function can be occasionally changed, the paging cost of an $α$-way set-associative LRU cache is within a factor $1 + o(1)$ of that a fully-associative LRU cache of size $(1-o(1))k$, with probability $1 - 1/\operatorname{poly}(k)$, for request sequences of arbitrary (e.g., super-polynomial) length. Some of our results generalize to other paging algorithms besides LRU, such as least-frequently used (LFU).