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A minimal perfect hash function (MPHF) bijectively maps a set S of objects to the first |S| integers. It can be used as a building block in databases and data compression. RecSplit [Esposito et al., ALENEX'20] is currently the most space efficient practical minimal perfect hash function. It heavily relies on trying out hash functions in a brute force way. We introduce rotation fitting, a new technique that makes the search more efficient by drastically reducing the number of tried hash functions. Additionally, we greatly improve the construction time of RecSplit by harnessing parallelism on the level of bits, vectors, cores, and GPUs. In combination, the resulting improvements yield speedups up to 239 on an 8-core CPU and up to 5438 using a GPU. The original single-threaded RecSplit implementation needs 1.5 hours to construct an MPHF for 5 Million objects with 1.56 bits per object. On the GPU, we achieve the same space usage in just 5 seconds. Given that the speedups are larger than the increase in energy consumption, our implementation is more energy efficient than the original implementation.

The equidistant subsequence pattern matching problem is considered. Given a pattern string P and a text string T, we say that P is an equidistant subsequence of T if P is a subsequence of the text such that consecutive symbols of P in the occurrence are equally spaced. We can consider the problem of equidistant subsequences as generalizations of (sub-)cadences. We give bit-parallel algorithms that yield o(n²) time algorithms for finding k-(sub-)cadences and equidistant subsequences. Furthermore, O(nlog² n) and O(nlog n) time algorithms, respectively for equidistant and Abelian equidistant matching for the case |P| = 3, are shown. The algorithms make use of a technique that was recently introduced which can efficiently compute convolutions with linear constraints.

In the packed string matching problem, each machine word accommodates – characters, thus an n-character text occupies n/– memory words. We extend the Crochemore-Perrin constantspace O(n)-time string matching algorithm to run in optimal O(n/–) time and even in real-time, achieving a factor – speedup over traditional algorithms that examine each character individually. Our solution can be efficiently implemented, unlike prior theoretical packed string matching work. We adapt the standard RAM model and only use its AC0 instructions (i.e., no multiplication) plus two specialized AC0 packed string instructions. The main string-matching instruction is available in commodity processors (i.e., Intel’s SSE4.2 and AVX Advanced String Operations); the other maximal-suffix instruction is only required during pattern preprocessing. In the absence of these two specialized instructions, we propose theoretically-efficient emulation using integer multiplication (not AC0) and table lookup.

In the packed string matching problem, each machine word accomodates alpha characters, thus an n-character text occupies n/alpha memory words. We extend the Crochemore-Perrin constant-space O(n)-time string matching algorithm to run in optimal O(n/alpha) time and even in real-time, achieving a factor alpha speedup over traditional algorithms that examine each character individually. Our solution can be efficiently implemented, unlike prior theoretical packed string matching work. We adapt the standard RAM model and only use its AC0 instructions (i.e. no multiplication) plus two specialized AC0 packed string instructions. The main string-matching instruction is available in commodity processors (i.e. Intel's SSE4.2 and AVX Advanced String Operations); the other maximal-suffix instruction is only required during pattern preprocessing. In the absence of these two specialized instructions, we propose theoretically-efficient emulation using integer multiplication (not AC0) and table lookup.

In the packed string matching problem, each machine word accommodates – characters, thus an n-character text occupies n/– memory words. We extend the Crochemore-Perrin constantspace O(n)-time string matching algorithm to run in optimal O(n/–) time and even in real-time, achieving a factor – speedup over traditional algorithms that examine each character individually. Our solution can be efficiently implemented, unlike prior theoretical packed string matching work. We adapt the standard RAM model and only use its AC0 instructions (i.e., no multiplication) plus two specialized AC0 packed string instructions. The main string-matching instruction is available in commodity processors (i.e., Intel’s SSE4.2 and AVX Advanced String Operations); the other maximal-suffix instruction is only required during pattern preprocessing. In the absence of these two specialized instructions, we propose theoretically-efficient emulation using integer multiplication (not AC0) and table lookup.