Journal of Parallel and Distributed Computing - Impact Factor

Journal of Parallel and Distributed Computing - Elsevier | 2024 Impact Factor:4.0 | Cite Score:9.8 | Q1

Impact Factor and Journal Rank of Journal of Parallel and Distributed Computing

About: The Journal of Parallel and Distributed Computing publishes original research papers and timely review articles focused on the theory, design, evaluation, and utilization of parallel and/or distributed computing systems. The journal serves as a premier publication for researchers and practitioners interested in advancing the field of parallel and distributed computing, covering a wide range of topics from theoretical foundations to practical applications.
Objective: The primary objective of the Journal of Parallel and Distributed Computing is to foster innovation and dissemination of knowledge in parallel and distributed computing systems. The journal aims to publish cutting-edge research that addresses fundamental challenges and explores novel solutions in parallel processing, distributed algorithms, cloud computing, grid computing, and related areas. By showcasing high-quality research articles and reviews, the journal facilitates collaboration among researchers and practitioners worldwide, promoting advancements in scalable computing technologies.
Interdisciplinary Focus: Journal of Parallel and Distributed Computing embraces an interdisciplinary approach, welcoming contributions from computer science, electrical engineering, mathematics, and other related fields. It encourages research that integrates theoretical insights with practical implementations, addressing complex issues in parallel computing architectures, distributed systems design, fault tolerance, performance optimization, and scalability. The journal supports the development of innovative algorithms, methodologies, and frameworks that enable efficient utilization of computing resources across distributed environments.
Global Reach and Impact: With a global readership and authorship, the Journal of Parallel and Distributed Computing influences research and development in parallel and distributed computing worldwide. Its publications contribute to advancing state-of-the-art technologies and methodologies, enhancing the performance, reliability, and efficiency of computing systems. The journal impact extends beyond academia to industry applications, scientific simulations, big data analytics, and emerging technologies such as edge computing and Internet of Things (IoT).
High Standards and Rigorous Review: Maintaining rigorous academic standards, the Journal of Parallel and Distributed Computing employs a thorough peer-review process. Each submitted manuscript undergoes comprehensive evaluation by experts in the field to ensure scientific rigor, methodological soundness, and originality of research contributions. This stringent review process upholds the integrity and credibility of the journal, ensuring that only impactful and authoritative studies are published.
Significance: The Journal of Parallel and Distributed Computing plays a critical role in advancing research and innovation in parallel and distributed computing systems. By providing a platform for disseminating research findings and technological advancements, the journal supports the development of scalable and efficient computing solutions for addressing societal challenges and scientific discoveries. It serves as an essential resource for researchers, educators, and industry professionals seeking to leverage parallel and distributed computing technologies in diverse applications.

Journal Home: Journal Homepage

Editor-in-Chief: V.K. Prasanna

Scope: The journal provides a platform for researchers and practitioners to contribute to advancements in scalable computing architectures, parallel algorithms, distributed systems, and their real-world applications. Here is an overview of its key focus areas and scope:
1. Scalable Computing Architectures:
Research on novel architectures, hardware designs, and platforms for parallel and distributed computing.
Advancements in multi-core processors, GPU computing, FPGA-based systems, and specialized accelerators.
2. Parallel Algorithms:
Development and analysis of efficient parallel algorithms for solving computational problems.
Research on algorithmic techniques such as divide-and-conquer, dynamic programming, and parallel data structures.
3. Distributed Systems:
Design, implementation, and evaluation of distributed computing systems.
Research on distributed databases, cloud computing, edge/fog computing, peer-to-peer networks, and Internet of Things (IoT).
4. High-Performance Computing:
Advancements in high-performance computing (HPC) techniques and supercomputing.
Research on parallel processing, task scheduling, load balancing, and performance optimization.
5. Parallel and Distributed Software Engineering:
Development methodologies, tools, and frameworks for building scalable and reliable parallel/distributed software systems.
Research on software architectures, middleware, programming models, and software-defined networking.
6. Big Data Analytics and Machine Learning:
Applications of parallel and distributed computing techniques in big data analytics and machine learning.
Research on scalable data processing frameworks, distributed machine learning algorithms, and deep learning models.
7. Performance Evaluation and Benchmarking:
Methods and metrics for evaluating the performance, scalability, and efficiency of parallel/distributed computing systems.
Research on benchmark suites, simulation techniques, and empirical studies.
8. Fault Tolerance and Resilience:
Techniques for fault tolerance, reliability, and resilience in distributed systems.
Research on fault detection, recovery mechanisms, replication strategies, and consensus algorithms.
9. Applications in Science and Engineering:
Real-world applications of parallel and distributed computing in scientific simulations, computational biology, engineering, finance, and beyond.
Research on domain-specific challenges and optimizations.
10. Emerging Technologies and Trends:
Exploration of emerging technologies and future trends in parallel and distributed computing.
Research on quantum computing, distributed ledger technologies (blockchain), edge computing, and hybrid cloud systems.