Parallel implementation of machine translation using MPJ Express

Tomar, Anuradha; Bodhankar, Jahnavi; Kurariya, Pavan; Anarase, Pramod; Jain, Priyanka; Lele, Anuradha; Darbari, Hemant; Bhavsar, Virendrakumar C.

doi:10.1109/parcomptech.2013.6621391

Cited by 6 publications

(3 citation statements)

References 3 publications

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“…The design of MPJ Express consists of several layers that includes the MPJ API layer (highest layer), collective and point to point communications API (next lower layers), and mpjdev and xdec levels (next lower layers) for actual communications and interaction with the underlying networking hardware. MPJ Express has been utilized in a few domains, such as machine translation for natural language processing [15], and clustering scalability [16].…”

Section: Massage Passing In Javamentioning

confidence: 99%

Performance comparison of java based parallel programming models

Jamaluddin

Ismail

Rashid

et al. 2019

IJEECS

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<span lang="EN-MY">Parallel programming has been implemented in many areas to solve various computational problem with the aim, to improve the performance and scalability of the software application. There are a few parallel programming models commonly used, namely, threads, and message passing (distributed) models. Furthermore, various APIs have been proposed to implement these models based on two popular languages, notably, C/C++ and Java. A few studies have been done to compare the performance of parallel programming models, specifically, pure versus hybrid model. However, most of existing comparisons targeted on MPI/OpenMP based on C/C++ language. In this paper, our aim is to explore the performance comparison between threads, message passing and hybrid model in Java, specifically using Java multithreading and MPJ Express. For this reason, we have chosen a problem called word count occurrence which is significant in Natural Language Processing and use it to design and implement the parallel programs. We then present their performance and discuss the results.</span>

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Section: Massage Passing In Javamentioning

confidence: 99%

Performance comparison of java based parallel programming models

Jamaluddin

Ismail

Rashid

et al. 2019

IJEECS

View full text Add to dashboard Cite

show abstract

“…We have designed a visualizer, named Vishit, for visualizing texts in the Hindi language used in many states of India (Jain et al 2013(Jain et al , 2014. Finally, we have also carried out work on the parallel/distributed implementations of natural language processing systems on high performance computing platforms, such as multicore grid, cluster platforms Hadoop (Tomar et al 2013a(Tomar et al , 2013b(Tomar et al , 2014.…”

Section: Big Data Cognition: Graph Similarity and Natural Language Pr...mentioning

confidence: 99%

Cognitive Intelligence

Budin

Gavrilova

Shell

et al. 2016

International Journal of Cognitive Informatics and Natural Intelligence

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The theme of IEEE ICCI*CC'16 on Cognitive Informatics (CI) and Cognitive Computing (CC) was on cognitive computers, big data cognition, and machine learning. CI and CC are a contemporary field not only for basic studies on the brain, computational intelligence theories, and denotational mathematics, but also for engineering applications in cognitive systems towards deep learning, deep thinking, and deep reasoning. This paper reports a set of position statements presented in the plenary panel (Part I) in IEEE ICCI*CC'16 at Stanford University. The summary is contributed by invited panelists who are part of the world's renowned scholars in the transdisciplinary field of CI and CC.

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“…Here, Translation Memory aids the MT by avoiding processing time on repetitive translation by using balancing algorithm to distribute multiple sentences [2]. Another Experiment have been carried out sentence level parallelization and its parallel implementations on a multicore machine with varying number of cores and a computing cluster with multi-core nodes using Message Passing Interface [14]. Some other Experiments carried out parallelization on the GARUDA grid by [15] where MT application has been port on Garuda by using MPI framework for achieving speedup and improving performance of System.…”

Section: Literature Surveymentioning

confidence: 99%

File model approach to optimize the performance of Tree Adjoining Grammar based Machine Translation

Kurariya

Chaudhary

Jain

et al. 2015

2015 International Conference on Computer, Communication and Control (IC4)

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The growing pace of information technology demands fast operation for communication and other related applications. After having a successful Machine Translation System [MTS], it has been felt to optimize the performance of Machine Translation for its real-time uses. The considered MTS is Tree Adjoining Grammar [TAG] based system. An approach has been experimented to use File model instead of Database model for fast streaming of grammar into memory and operation. This model provides an efficient and a systematic way of encapsulating language resource with engineering solution to develop the speedy MTS. The computational experiments demonstrate that substantial performance in terms of time and memory has been obtained by using this approach.

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Parallel implementation of machine translation using MPJ Express

Cited by 6 publications

References 3 publications

Performance comparison of java based parallel programming models

Performance comparison of java based parallel programming models

Cognitive Intelligence

File model approach to optimize the performance of Tree Adjoining Grammar based Machine Translation

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