Software-hardware codesign for efficient in-memory regular pattern matching

Kong, Lingkun; Yu, Qixuan; Chattopadhyay, Agnishom; Glaunec, Alexis Le; Huang, Yi; Mamouras, Konstantinos; Yang, Kaiyuan

doi:10.1145/3519939.3523456

Cited by 7 publications

(3 citation statements)

References 58 publications

(83 reference statements)

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“…The background information is input into the decoder. After passing through the encoder, all the information becomes the target language sequence [ 12 ]. The specific framework is shown in Figure 1 .…”

Section: Methodsmentioning

confidence: 99%

Semantic Analysis Technology of English Translation Based on Deep Neural Network

Wang

2022

Computational Intelligence and Neuroscience

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English translation plays an important role in the development of science and technology and cultural exchanges. With the increase in translation volume, intelligent translation has become inevitable, but there is no effective solution for semantic translation in English translation. To provide an effective translation improvement scheme in English translation, this paper studies and analyzes the application of deep neural network in English translation semantic analysis. Based on a brief analysis of the research progress of English translation analysis and the current situation of neural network, a neural network translation architecture is established, and a deep neural network model for English translation analysis is proposed. Aiming at the problem of gradient disappearance in RNN model, the ability of Gru neural network to deal with long-distance translation is enhanced, and the computational complexity of Gru neural network is reduced. At the same time, a bi-directional Gru model is designed to translate according to the context. For some nonlinear translation, a deep neural network model based on part of speech sequence information is proposed to realize semantic analysis, and experiments are designed to test the translation effect of the neural network model. The simulation results show that the English translation based on deep neural network can improve the translation effect, reduce errors and improve the accuracy of semantic analysis of English translation, which has certain reference significance for improving the level of English translation.

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Section: Methodsmentioning

confidence: 99%

Semantic Analysis Technology of English Translation Based on Deep Neural Network

Wang

2022

Computational Intelligence and Neuroscience

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“…However, their execution requires the simultaneous activation of multiple transitions, leading to severe bandwidth limitations. A set of works addressing this issue targets NFAs execution on hardware accelerators [13], [21], [41], [42], exploiting ad-hoc algorithms [27], [28], [43]. These algorithmic solution take advantage of NFAs multiple active states by parallelizing the automaton traversal [32].…”

Section: Background Knowledgementioning

confidence: 99%

“…To fully exploit FSAs executive potential, there exist various techniques fostering the optimization of both NFAs and DFAs that tackle NFAs partitioning [26], multi-stride DFAs [11], [28], [40], and DFAs compression [33], [46]. These optimization approaches involve both architectural [6], [13], [20]- [23], [26], [41], [47] and algorithmic aspects [11], [12], [14], [33]. An architectural approach to optimizing NFAs [26] relies on a toolchain to partition them, depending on the available hardware resources and units.…”

Section: Related Workmentioning

confidence: 99%

One Automaton to Rule Them All: Beyond Multiple Regular Expressions Execution

Cicolini,

Carloni,

Santambrogio

et al. 2024

2024 IEEE/ACM International Symposium on Code Generation and Optimization (CGO)

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Regular Expressions (REs) matching is crucial to identify strings exhibiting certain morphological properties in a data stream, resulting paramount in contexts such as deep packet inspection in computer security and genome analysis in bioinformatics. Yet, due to their intrinsic data-dependence characteristics, REs represent a complex computational kernel, and numerous solutions investigate pattern-matching efficiency in different directions. However, most of them lack a comprehensive ruleset optimization approach to truly push the pattern matching performance when considering multiple REs together. Thus, exploiting REs morphological similarities within the same dataset allows memory reduction when storing the patterns and drastically improves the dataset-matching throughput.Based on this observation, we propose the Multi-RE Finite State Automata (MFSA) that extends the Finite State Automata (FSA) model to improve REs parallelization by leveraging similarities within a specific application ruleset. We design a multi-level compilation framework to manage REs merging and optimization to produce MFSA(s). Furthermore, we extend iNFAnt algorithm for MFSAs execution with the novel iMFAnt engine. Our evaluation investigates the MFSA size-reduction impact and the execution throughput compared with the one of multiple FSA in both single-and multi-threaded configurations. This approach shows an average 71.95% compression in terms of states, introducing limited compilation time overhead. Besides, best iMFAnt achieves a geomean 5.99× throughput improvement and 4.05× speedup against single and multiple parallel FSAs.

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Asynchronous Automata Processing on GPUs

Liu

Pai

Jog

2023

Proc. ACM Meas. Anal. Comput. Syst.

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Finite-state automata serve as compute kernels for many application domains such as pattern matching and data analytics. Existing approaches on GPUs exploit three levels of parallelism in automata processing tasks: 1)~input stream level, 2)~automaton-level and 3)~state-level. Among these, only state-level parallelism is intrinsic to automata while the other two levels of parallelism depend on the number of automata and input streams to be processed. As GPU resources increase, a parallelism-limited automata processing task can underutilize GPU compute resources. To this end, we propose AsyncAP, a low-overhead approach that optimizes for both scalability and throughput. Our insight is that most automata processing tasks have an additional source of parallelism originating from the input symbols which has not been leveraged before. Making the matching process associated with the automata tasks asynchronous, i.e., parallel GPU threads start processing an input stream from different input locations instead of processing it serially, improves throughput significantly and scales with input length. When the task does not have enough parallelism to utilize all the GPU cores, detailed evaluation across 12 evaluated applications shows that AsyncAP achieves up to 58× speedup on average over the state-of-the-art GPU automata processing engine. When the tasks have enough parallelism to utilize GPU cores, AsyncAP still achieves 2.4× speedup.

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Software-hardware codesign for efficient in-memory regular pattern matching

Cited by 7 publications

References 58 publications

Semantic Analysis Technology of English Translation Based on Deep Neural Network

Semantic Analysis Technology of English Translation Based on Deep Neural Network

One Automaton to Rule Them All: Beyond Multiple Regular Expressions Execution

Asynchronous Automata Processing on GPUs

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