An Analytical study of Performance towards Task-level Parallelism on Many-core systems using Java API

Nair, Lekshmi S

doi:10.1109/icces51350.2021.9489215

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…As shown in figure 3.1 the programming language helps to create a communication bridge between the computer operating system and humans. Multi-threading languages help process multiple programs through a single systematic way of processing multiple programming codes sequentially [9]. This helps in arranging all the input programming codes through a multi-channel transformation of language to binary language.…”

Section: Methodsmentioning

confidence: 99%

Multi-Threaded Data Communication in Java for Advanced Computing Environments

Wang,

Yang

2023

SCPE

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The performance of operating systems like computers requires the proper functioning of the computer language interpreter. This interpreter follows various types of programming languages that make the performing of computer programming easy and effective. The application of the programming language of Java helps in processing multiple tasks at once. This research analyzes the novelty of Java-based data communication models in advanced computing services. This performance makes the saving of the resources used for the development of the programming language. All these development includes the performing of the multiple threads communication data processing. These multiple threads help distribute the single processed input in the multi-channel language processing, thus helping the work competition in time. It also reduces the cost of maintaining the programming languages reduced. Thus, the implementing cost of resources required for programming performance is reduced. Therefore, this implementation impacts the programmer to become more indented to use the language transformation process of Java. Moreover, it creates a more effective representation of the audio or visual content represented by a multi-tasking operating system. In this process of development in the language transformation of the operating system, the ability of the operating system for data processing improves. The systematic process of this language transformation helps in systematically transforming multiple programs at once.

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Section: Methodsmentioning

confidence: 99%

Multi-Threaded Data Communication in Java for Advanced Computing Environments

Wang,

Yang

2023

SCPE

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“…In the proposed PEWOA, a separate thread using Java Executor Framework [35] is allocated to the encircling and bubble net attacking maneuvers of each whale using a distinct set of values for faster convergence. It utilizes a masterslave parallelization model with multiple threads and a shared memory to explore different regions of the solution space.…”

Section: Proposed Parallel Enhanced Whale Optimization Algorithmmentioning

confidence: 99%

Parallel Enhanced Whale Optimization Algorithm for Independent Tasks Scheduling on Cloud Computing

Khan,

Aziz,

Osman

et al. 2024

IEEE Access

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Cloud computing has been imperative for computing systems worldwide since its inception. The researchers strive to leverage the efficient utilization of cloud resources to execute workload quickly in addition to providing better quality of service. Among several challenges on the cloud, task scheduling is one of the fundamental NP-hard problems. Meta-heuristic algorithms are extensively employed to solve task scheduling as a discrete optimization problem and therefore several meta-heuristic algorithms have been developed. However, they have their own strengths and weaknesses. Local optima, poor convergence, high execution time, and scalability are the predominant issues among meta-heuristic algorithms. In this paper, a parallel enhanced whale optimization algorithm is proposed to schedule independent tasks in the cloud with heterogeneous resources. The proposed algorithm improves solution diversity and avoids local optima using a modified encircling maneuver and an adaptive bubble net attacking mechanism. The parallelization technique keeps the execution time low despite its internal complexity. The proposed algorithm minimizes the makespan while improving resource utilization and throughput. It demonstrates the effectiveness of the proposed PEWOA against the best performing enhanced whale optimization algorithm (WOAmM) and Multi-core Random Matrix Particle Swarm Optimization (MRMPSO). The algorithm consistently produces better results with varying number of tasks on GoCJ dataset, indicating better scalability. The experiments are conducted in CloudSim utilizing a variety of GoCJ and HCSP instances. Various statistical tests are also conducted to evaluate the significance of the results.

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