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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
Fast and Efficient Merge of Sorted Input Lists in Hardwar...
Robert B. Kent, Marios S. Pattichis · 2025-07-11 · via cs.DS updates on arXiv.org

A new set of hardware merge sort devices are introduced here, which merge multiple sorted input lists into a single sorted output list in a fast and efficient manner. In each merge sorter, the values from the sorted input lists are arranged in an input 2-D setup array, but with the order of each sorted input list offset from the order of each of the other sorted input lists. In these new devices, called List Offset Merge Sorters (LOMS), a minimal set of column sort stages alternating with row sort stages process the input setup array into a final output array, now in the defined sorted order. LOMS 2-way sorters, which merge 2 sorted input lists, require only 2 merge stages and are significantly faster than Kenneth Batcher's previous state-of-the-art 2-way merge devices, Bitonic Merge Sorters and Odd-Even Merge Sorters. LOMS 2-way sorters utilize the recently-introduced Single-Stage 2-way Merge Sorters (S2MS) in their first stage. Both LOMS and S2MS devices can merge any mixture of input list sizes, while Batcher's merge sorters are difficult to design unless the 2 input lists are equal, and a power-of-2. By themselves, S2MS devices are the fastest 2-way merge sorters when implemented in this study's target FPGA devices, but they tend to use a large number of LUT resources. LOMS 2-way devices use fewer resources than comparable S2MS devices, enabling some large LOMS devices to be implemented in a given FPGA when comparable S2MS devices cannot fit in that FPGA. A List Offset 2-way sorter merges 2 lists, each with 32 values, into a sorted output list of those 64 values in 2.24 nS, a speedup of 2.63 versus a comparable Batcher device. A LOMS 3-way merge sorter, merging 3 sorted input lists with 7 values, fully merges the 21 values in 3.4 nS, a speedup of 1.36 versus the comparable state-of-the-art 3-way merge device.