Parallel Computing - Elsevier | 2024 Impact Factor:2.1 | Cite Score:5.4 | Q2

Impact Factor and Journal Rank of Parallel Computing

• About: Parallel Computing Journal serves as a premier platform for researchers, practitioners, and academics to publish high-quality research articles, reviews, and application papers in the field of parallel computing. The journal focuses on the design, implementation, and analysis of parallel algorithms and systems, promoting advancements in the efficiency and performance of parallel computing technologies.
• Objective:
  The primary objective of Parallel Computing Journal is to provide a leading publication venue for research that explores the theoretical foundations, architectural innovations, and practical applications of parallel computing. The journal covers a wide range of topics including parallel algorithms, parallel architectures, distributed systems, high-performance computing, parallel programming models, and parallel applications in scientific computing, engineering, and industry. By integrating theoretical insights with practical implementations, the journal aims to advance the development of scalable and efficient parallel computing solutions.
• Interdisciplinary Approach:
  Parallel Computing Journal promotes an interdisciplinary approach by encouraging contributions that bridge computer science, mathematics, electrical engineering, and related disciplines. The journal welcomes research that explores the intersection of parallel computing with areas such as machine learning, big data analytics, computational biology, and real-time systems. By fostering collaborations across disciplines, the journal facilitates the dissemination of innovative research that addresses complex computational challenges and leverages parallelism to enhance computational performance and efficiency.
• Impact:
  The impact of Parallel Computing Journal is significant in advancing both theoretical and practical aspects of parallel computing. By publishing rigorous research articles, reviews, and application papers, the journal contributes to the development of new parallel algorithms, architectural enhancements, and efficient programming models. Its publications inform the design of high-performance computing systems, distributed computing frameworks, and parallel processing techniques, thereby influencing advancements in various computational fields. The journals emphasis on high-quality research and practical relevance ensures its contributions support the continuous evolution and adoption of parallel computing technologies.
• Significance:
  Parallel Computing Journal holds significant importance for researchers, educators, and practitioners interested in advancing the field of parallel computing. The journals contributions include theoretical advancements, practical applications, and critical analyses of parallel computing techniques. By providing a platform for scholarly exchange and knowledge dissemination, the journal supports the development of innovative solutions that address real-world computational challenges. It fosters the continuous advancement of parallel computing methodologies and technologies, facilitating the development of robust and scalable computing systems that meet the demands of modern scientific, engineering, and industrial applications.

• Journal Home:  Journal Homepage

• Editor-in-Chief:  A. Benoit

• Scope: The Parallel Computing journal focuses on the theory, design, and application of parallel computing systems. It covers a broad spectrum of topics related to parallel and distributed computing architectures, algorithms, programming models, and applications. Here is an overview:
• Parallel Architectures:
  Research on different types of parallel computing architectures, including multi-core processors, GPU computing, vector processors, distributed memory systems, shared memory systems, and heterogeneous computing platforms.
• Parallel Algorithms and Data Structures:
  Development and analysis of algorithms designed for parallel execution, including sorting, searching, graph algorithms, numerical algorithms, optimization algorithms, and parallel data structures.
• Parallel Programming Models:
  Languages, libraries, and frameworks for programming parallel systems, such as MPI (Message Passing Interface), OpenMP, CUDA, OpenCL, Pthreads, and higher-level programming paradigms for task parallelism and data parallelism.
• Parallel and Distributed Software Engineering:
  Methodologies, tools, and techniques for developing and managing software systems in parallel and distributed computing environments. This includes software design patterns, debugging, testing, and performance analysis.
• Performance Modeling and Evaluation:
  Methods for predicting, measuring, and analyzing the performance of parallel computing systems. This includes benchmarking, simulation, profiling, scalability analysis, and metrics for evaluating parallel algorithms.
• Parallel Applications and Case Studies:
  Real-world applications of parallel computing across various domains, including scientific computing, big data analytics, machine learning, bioinformatics, multimedia processing, financial modeling, and simulations.
• Parallel Computing in Cloud and Edge Computing:
  Use of parallel computing techniques in cloud computing architectures, edge computing, and IoT (Internet of Things) systems. Topics include resource allocation, load balancing, fault tolerance, and scalability in distributed environments.
• Parallel Computing in High-Performance Computing (HPC):
  Applications of parallel computing in high-performance computing systems, supercomputers, grid computing, and cluster computing. This includes advancements in achieving high throughput and low latency.
• Parallel I/O and Storage Systems:
  Techniques and architectures for efficient input/output operations and storage management in parallel and distributed computing systems. Topics include file systems, caching strategies, RAID (Redundant Array of Independent Disks), and distributed storage.
• Energy-Efficient Parallel Computing:
  Methods for reducing energy consumption and improving energy efficiency in parallel computing systems. This includes power-aware scheduling algorithms, low-power hardware designs, and green computing initiatives.
• Latest Research Topics for PhD in Computer Science

• Print ISSN:  01678191

  Electronic ISSN:  

• Abstracting and Indexing:  Scopus, Science Citation Index Expanded

• Imapct Factor 2024:  2.1

• Subject Area and Category:   Computer Science, Artificial Intelligence, Computer Graphics and Computer-Aided Design, Computer Networks and Communications, Hardware and Architecture, Software, Mathematics, Theoretical Computer Science

• Publication Frequency:  

• H Index:  72

• Best Quartile:

  Q1:  

  Q2:  Computer Graphics and Computer-Aided Design

  Q3:  

  Q4:  

• Cite Score:  5.4

• SNIP:  1.303

• Journal Rank(SJR):  0.541

• Latest Articles:   Latest Articles in Parallel Computing

• Guidelines for Authors: Parallel Computing Author Guidelines

• Paper Submissions: Paper Submissions in Parallel Computing

• Publisher:  Elsevier

  Country:  Netherlands

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