Search-Based Regular Expression Inference on a GPU

• URL: http://arxiv.org/abs/2305.18575v1
• Date: Mon, 29 May 2023 19:37:15 GMT
• Title: Search-Based Regular Expression Inference on a GPU
• Authors: Mojtaba Valizadeh, Martin Berger
• Abstract summary: Regular expression inference (REI) is a supervised machine learning and program synthesis problem. It takes a cost metric for regular expressions, and positive and negative examples of strings as input. We present a novel algorithm for REI over arbitrary alphabets that is enumerative and trades off time for space.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Regular expression inference (REI) is a supervised machine learning and program synthesis problem that takes a cost metric for regular expressions, and positive and negative examples of strings as input. It outputs a regular expression that is precise (i.e., accepts all positive and rejects all negative examples), and minimal w.r.t. to the cost metric. We present a novel algorithm for REI over arbitrary alphabets that is enumerative and trades off time for space. Our main algorithmic idea is to implement the search space of regular expressions succinctly as a contiguous matrix of bitvectors. Collectively, the bitvectors represent, as characteristic sequences, all sub-languages of the infix-closure of the union of positive and negative examples. Mathematically, this is a semiring of (a variant of) formal power series. Infix-closure enables bottom-up compositional construction of larger from smaller regular expressions using the operations of our semiring. This minimises data movement and data-dependent branching, hence maximises data-parallelism. In addition, the infix-closure remains unchanged during the search, hence search can be staged: first pre-compute various expensive operations, and then run the compute intensive search process. We provide two C++ implementations, one for general purpose CPUs and one for Nvidia GPUs (using CUDA). We benchmark both on Google Colab Pro: the GPU implementation is on average over 1000x faster than the CPU implementation on the hardest benchmarks.

Related papers

• Efficient Algorithms for Recognizing Weighted Tree-Adjoining Languages [104.90415092306219]
  Four formalisms are equivalent to tree-adjoining grammars (TAG), linear indexed grammars (LIG), pushdown-adjoining automata (PAA) and embedded pushdown automata (EPDA) We design new algorithms for computing their stringsum derivations (the weight of all automatons of a string) and allsums (the weight of all derivations) For EPDA, our algorithm is both more space-efficient and time-efficient than the algorithm of Alonso et al. (2001) by factors of $mathcalO(|Gamma|2)$ and $
  arXiv  Detail & Related papers  (2023-10-23T18:26:00Z)
• High Performance Computing Applied to Logistic Regression: A CPU and GPU Implementation Comparison [0.0]
  We present a versatile GPU-based parallel version of Logistic Regression (LR) Our implementation is a direct translation of the parallel Gradient Descent Logistic Regression algorithm proposed by X. Zou et al. Our method is particularly advantageous for real-time prediction applications like image recognition, spam detection, and fraud detection.
  arXiv  Detail & Related papers  (2023-08-19T14:49:37Z)
• Correct and Optimal: the Regular Expression Inference Challenge [10.899596368151892]
  We propose regular expression inference (REI) as a challenge for code/language modelling. We generate and publish the first large-scale datasets for REI.
  arXiv  Detail & Related papers  (2023-08-15T17:40:10Z)
• Algorithms for Weighted Pushdown Automata [118.67634716230025]
  Weighted pushdown automata (WPDAs) are at the core of many natural language processing tasks. We develop novel algorithms that operate directly on WPDAs.
  arXiv  Detail & Related papers  (2022-10-13T10:21:31Z)
• Rapid Person Re-Identification via Sub-space Consistency Regularization [51.76876061721556]
  Person Re-Identification (ReID) matches pedestrians across disjoint cameras. Existing ReID methods adopting real-value feature descriptors have achieved high accuracy, but they are low in efficiency due to the slow Euclidean distance computation. We propose a novel Sub-space Consistency Regularization (SCR) algorithm that can speed up the ReID procedure by 0.25$ times.
  arXiv  Detail & Related papers  (2022-07-13T02:44:05Z)
• Batch-efficient EigenDecomposition for Small and Medium Matrices [65.67315418971688]
  EigenDecomposition (ED) is at the heart of many computer vision algorithms and applications. We propose a QR-based ED method dedicated to the application scenarios of computer vision.
  arXiv  Detail & Related papers  (2022-07-09T09:14:12Z)
• Efficient GPU implementation of randomized SVD and its applications [17.71779625877989]
  Matrix decompositions are ubiquitous in machine learning, including applications in dimensionality data compression and deep learning algorithms. Typical solutions for matrix decompositions have complexity which significantly increases their computational cost and time. We leverage efficient processing operations that can be run in parallel on modern Graphical Processing Units (GPUs) to reduce the computational burden of computing matrix decompositions.
  arXiv  Detail & Related papers  (2021-10-05T07:42:41Z)
• Implementation of Parallel Simplified Swarm Optimization in CUDA [2.322689362836168]
  In optimization computing, intelligent swarm algorithms (SIAs) method is suitable for parallelization. This paper proposed a GPU-based Simplified Swarm Algorithm Optimization (PSSO) based on the platform considering computational ability and versatility. As the results showed, the time complexity has successfully reduced by an order of magnitude of N, and the problem of resource preemption was avoided entirely.
  arXiv  Detail & Related papers  (2021-10-01T00:15:45Z)
• VersaGNN: a Versatile accelerator for Graph neural networks [81.1667080640009]
  We propose textitVersaGNN, an ultra-efficient, systolic-array-based versatile hardware accelerator. textitVersaGNN achieves on average 3712$times$ speedup with 1301.25$times$ energy reduction on CPU, and 35.4$times$ speedup with 17.66$times$ energy reduction on GPU.
  arXiv  Detail & Related papers  (2021-05-04T04:10:48Z)
• Systolic Computing on GPUs for Productive Performance [2.8064596842326575]
  We propose a language and compiler to productively build high-performance systolic arrays that run on GPUs. A programmer it' specifies a projection of a dataflow compute onto a linear systolic array, while leaving the detailed implementation of the projection to a compiler. The compiler implements the specified projection and maps the linear systolic array to the SIMD execution units and vector registers of GPUs.
  arXiv  Detail & Related papers  (2020-10-29T18:49:54Z)
• Kernel methods through the roof: handling billions of points efficiently [94.31450736250918]
  Kernel methods provide an elegant and principled approach to nonparametric learning, but so far could hardly be used in large scale problems. Recent advances have shown the benefits of a number of algorithmic ideas, for example combining optimization, numerical linear algebra and random projections. Here, we push these efforts further to develop and test a solver that takes full advantage of GPU hardware.
  arXiv  Detail & Related papers  (2020-06-18T08:16:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.