research paper related to parallel computing

Software engineering and productivity as it relates to parallel computing. Applications (including scientific computing, deep learning, machine learning) or tool case studies demonstrating novel ways to achieve parallelism. Performance measurement results on state-of-the-art systems.

The first computer software was built for serial calculation, which can only execute a single instruction at a time. In contrast, parallel computing uses several processors to run an application or computation at the same time. In this paper, fundament theory of parallel computing is discussed.

We refrain from a comprehensive survey and concentrate on parallel programming patterns, design for parallel program, parallel programming models, parallel programming languages, design of parallel algorithms, together with a perspective of parallel computing.

The Journal of Parallel and Distributed Computing publishes original research papers and timely review articles on the theory, design, evaluation, and use of parallel and/or distributed computing systems. The journal also features special issues on these topics; again covering the full range from the design to the use of our targeted systems.

The selected papers of this special issue cover a variety of interesting topics reflecting some recent developments in theoretical and practical research in both core and interdisciplinary areas of parallel and distributed computing, applications, and technologies.

This section briefly summarizes top-level recommendations for research in parallel and distributed computing. Subsequent sections of the report provide more detail on specific research directions.

Parallel computing technologies enable the solution of large-scale numerical simulation and data processing problems in science and industry. The special issue highlights a number of research fields from a wide spectrum of parallel computing technologies.

In this chapter, we have dealt with parallelism by exploring its principle, its many architectures and its advantages over sequential architecture. In future research, we will apply parallel computing to sequential algorithms to study the effectiveness of Artificial Intelligence and Data Processing when working with Parallelism.

The teaching of parallel computing at the beginning of undergraduate courses appear in different papers. This paper contributes to research in teaching parallel computing, pointing out the state of the art of this area, highlighting challenges that should be the focus of investigations.

In this paper, we propose a novel parallel redistribution for DM that achieves an O (log 2 N) time complexity. We also present empirical results that indicate that our novel approach outperforms the O ( ( log 2 N ) 2 ) approach.