GPU-based similarity metrics computation and machine learning approaches for string similarity evaluation in large datasets

Abstract

The digital era brings up on one hand massive amounts of available data and on the other hand the need of parallel computing architectures for efficient data processing. String similarity evaluation is a processing task applied on large data volumes, commonly performed by various applications such as search engines, biomedical data analysis and even software tools for defending against viruses, spyware, or spam. String similarities are also used in musical industry for matching playlist records with repertory records composed of song titles, performer artists and producers names, aiming to assure copyright protection of mass-media broadcast materials. The present paper proposes a novel GPU-based approach for parallel implementation of the Jaro–Winkler string similarity metric computation, broadly used for matching strings over large datasets. The proposed implementation is applied in musical industry for matching playlist with over 100k records with a given repertory which includes a collection of over 1 million right owner records. The global GPU RAM memory is used to store multiple string lines representing repertory records, whereas single playlist string comparisons with the raw data are performed using the maximum number of available GPU threads and the stride operations. Further on, the accuracy of the Jaro–Winkler approach for the string matching procedure is increased using both an adaptive neural network approach guided by a novelty detection classifier (aNN) and a multiple-features neural network implementation (MF-NN). Thus, the aNN approach yielded an accuracy of 92% while the MF-NN approach achieved an accuracy of 99% at the cost of increased computational complexity. Timing considerations and the computational complexity are detailed for the proposed approaches compared with both the general-purpose processor (CPU) implementation and the state-of-the-art GPU approaches. A speed-up factor of 21.6 was obtained for the GPU-based Jaro–Winkler implementation compared with the CPU one, whereas a factor of 3.72 was obtained compared with the existing GPU implementation of string matching procedure based on Levenstein distance metrics.

Multi-Pattern GPU Accelerated Collision-Less Rabin-Karp for NIDS

“…This strategic choice enhances the algorithm's overall performance on parallel architectures as the recent article published by (Groth et al, 2023) in the knowledge and information systems journal has proved to be 40% higher throughput than the CPU-only approach when dealing with string manipulation operations. Our approach offers a considerable speedup, especially when dealing with a large data set of patterns as mentioned in the 2023 study published Application of soft computing (Baloi et al, 2023) which used GPU-accelerated pattern matching to find similarity metrics. Two algorithms have emerged as the most widely utilized for multiple string matching on GPUs -the Aho-Corasick (AC) and Rabin-Karp algorithms.…”