Parallel Lexical Analyzer on the Cell Processor

Srikanth, G. Umarani

doi:10.1109/ssiri-c.2010.16

Cited by 20 publications

(8 citation statements)

References 7 publications

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“…Daniele Paolo Scarpazza et al [13], contributed significantly to the development of a parallel lexical analyzer. They provided a method and a set of strategies for performing parallel regular expression- Umarani Srikanth [14] investigated how to parallelize a lexical analyzer for use with cell processors. The method works by segmenting the original source code into a predetermined number of parts and performing lexical analysis operations concurrently.…”

Section: Related Workmentioning

confidence: 99%

Improved Parallel Scanner for the Concurrent Execution of Lexical Analysis Tasks on Multi-Core Systems

Nethravathi

Aithal

2022

IJAEML

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Purpose: The processing power of machines will continue to accelerate massively. Modern eras of computing are driven by elevated parallel processing by the revolution of multi-core processors. This continuing trend toward parallel architectural paradigms facilitates parallel processing on a single machine and necessitates parallel programming in order to utilize the machine's enormous processing power. As a consequence, scanner generator applications will eventually need to be parallelized in order to fully leverage the throughput benefits of multi-core processors. This article discusses the way of processing the tasks in parallel during the scanning stage of lexical analysis. This is done by recognizing tokens in different lines of the source program in parallel along with auto detection of keyword in a character stream. Tasks are allocated line-by-line to the multiple instances of the lexical analyzer program. Then, each of the instances is run in parallel to detect tokens on different cores that are not yet engaged. Design/Methodology/Approach: Developing a theoretical and experimental approach for parallelizing the lexical scanning process on a multi-core system. Findings/Result: Based on the developed model, the theoretical and practical results indicate that the suggested methodology outperforms the sequential strategy in terms of tokenization consistently. It significantly decreases the amount of time spent on lexical analysis during the compilation process. It is clearly observed that the speedup should increase at or close to the same rate as the number of cores and keywords in the source program increases. This enhancement would improve the overall compilation time even more. Originality/Value: A hybrid model is developed for the concurrent execution of a lexical analyzer on multi-core systems using a dynamic task allocation algorithm and an auto-keyword detection method. Paper Type: Experimental Research.

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Section: Related Workmentioning

confidence: 99%

Improved Parallel Scanner for the Concurrent Execution of Lexical Analysis Tasks on Multi-Core Systems

Nethravathi

Aithal

2022

IJAEML

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“…As an experiment, the original Flex kernel was optimized to execute on a multi-core cell processor. Umarani Srikanth [6] explored the possibility of parallelizing a lexical analyzer for cell processors. The approach is founded on segmenting the original source code into a predetermined number of chunks and performing lexical analysis tasks in parallel.…”

Section: Related Workmentioning

confidence: 99%

A Parallel Scanner for the Concurrent Execution of Lexical Analyzer Tasks on Multi-Core Machines using Dynamic Task Allocation Algorithm

Nethravathi

Aithal

2022

IJMTS

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Purpose: Massive multi-core architecture is rapidly becoming the standard in digital technology due to its high and parallel computational capability and performance benefits. To fully utilize the technological capabilities of multi-core systems, system software such as compilers should be re-engineered for parallel processing. Several important contributions have been made in the past to enhance the efficiency of the lexical analysis process by leveraging the innate parallel processing capability of multi-core computers. This trend of implementation shows that a parallel lexical analyzer tends to perform lexing tasks better than a conventional sequential lexical analyzer. This article discusses the way of making the tasks in parallel during the scanning of source program in the phase of lexical analysis. The objective of this study is to explore how to perform lexical analysis in parallel. On multi-core processors, multiple processes of the lexical analyzer program can run concurrently to scan multiple lines in the input stream in parallel for token detection. This is done by allocating tasks line-by-line to the core which is not engaged yet. Design/Methodology/Approach: Developing a theoretical and experimental approach for parallelizing the lexical scanning process on a multi-core system. Findings/Result: According to the theoretical and experimental results, the proposed methodology significantly outperforms the sequential approach in terms of tokenization. It considerably reduces the time required for lexical analysis during the compilation process. The result establishes unequivocally that the parallel lexical analyzer's performance should scale linearly with the number of cores. It is clearly observed that the speedup is expected to increase further if the number of CPU cores increases. This enhancement would speed up the compilation process even more. Originality/Value: A dynamic task allocation algorithm is developed for the concurrent execution of a lexical analyzer task on multi-core systems. Paper Type: Experimental Research.

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“…Clearly, algorithm [47] is not intended as a lexer to be invoked by a parallel parser, but as a self-standing processor for matching regular expressions -yet partially so, since it does not address the central issue of ambiguous regular expression parsing, which fortunately does not concern our intended applications. Recently, [48] has experimented on the Cell Processor a parallel version of the Aho-Korasick string matching algorithm. This work was motivated by the good performance of that algorithm on multi-core machines for string search against large dictionaries.…”

Section: Related Workmentioning

confidence: 99%

Parallel parsing made practical

Barenghi

Reghizzi

Mandrioli

et al. 2015

Science of Computer Programming

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The property of local parsability allows to parse inputs through inspecting only a bounded-length string around the current token. This in turn enables the construction of a scalable, data-parallel parsing algorithm, which is presented in this work. Such an algorithm is easily amenable to be automatically generated via a parser generator tool, which was realized, and is also presented in the following. Furthermore, to complete the framework of a parallel input analysis, a parallel scanner can also combined with the parser. To prove the practicality of a parallel lexing and parsing approach, we report the results of the adaptation of JSON and Lua to a form fit for parallel parsing (i.e. an operator-precedence grammar) through simple grammar changes and scanning transformations. The approach is validated with performance figures from both high performance and embedded multicore platforms, obtained analyzing real-world inputs as a test-bench. The results show that our approach matches or dominates the performances of production-grade LR parsers in sequential execution, and achieves significant speedups and good scaling on multicore machines. The work is concluded by a broad and critical survey of the past work on parallel parsing and future directions on the integration with semantic analysis and incremental parsing.

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Parallel Lexical Analyzer on the Cell Processor

Cited by 20 publications

References 7 publications

Improved Parallel Scanner for the Concurrent Execution of Lexical Analysis Tasks on Multi-Core Systems

Improved Parallel Scanner for the Concurrent Execution of Lexical Analysis Tasks on Multi-Core Systems

A Parallel Scanner for the Concurrent Execution of Lexical Analyzer Tasks on Multi-Core Machines using Dynamic Task Allocation Algorithm

Parallel parsing made practical

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