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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
A bloated FM-index reducing the number of cache misses du...
Szymon Grabowski, Aleksander Cisłak · 2015-12-07 · via cs.DS updates on arXiv.org

The FM-index is a well-known compressed full-text index, based on the Burrows-Wheeler transform (BWT). During a pattern search, the BWT sequence is accessed at "random" locations, which is cache-unfriendly. In this paper, we are interested in speeding up the FM-index by working on $q$-grams rather than individual characters, at the cost of using more space. The first presented variant is related to an inverted index on $q$-grams, yet the occurrence lists in our solution are in the sorted suffix order rather than text order in a traditional inverted index. This variant obtains $O(m/|CL| + \log n \log m)$ cache misses in the worst case, where $n$ and $m$ are the text and pattern lengths, respectively, and $|CL|$ is the CPU cache line size, in symbols (typically 64 in modern hardware). This index is often several times faster than the fastest known FM-indexes (especially for long patterns), yet the space requirements are enormous, $O(n\log^2 n)$ bits in theory and about $80n$-$95n$ bytes in practice. For this reason, we dub our approach FM-bloated. The second presented variant requires $O(n\log n)$ bits of space.