International Journal of Parallel Programming - Impact Factor

International Journal of Parallel Programming - Springer | 2024 Impact Factor:0.9 | Cite Score:3.5 | Q3

Impact Factor and Journal Rank of International Journal of Parallel Programming

About: International Journal of Parallel Programming (IJPP) is a leading journal dedicated to publishing high-quality research on all aspects of parallel programming, including the theory, design, implementation, and applications of parallel and distributed computing systems. The journal serves as a comprehensive forum for the dissemination of new ideas and significant research in the field.
Impact and Significance
International Journal of Parallel Programming is significant for several reasons:
Comprehensive Coverage: The journal covers a broad range of topics in parallel and distributed computing, making it a valuable resource for researchers and practitioners in the field.
Interdisciplinary Approach: It bridges the gap between theoretical research and practical applications, promoting the integration of new techniques and tools in real-world systems.
Cutting-Edge Research: The journal publishes innovative research that advances the state-of-the-art in parallel programming and distributed computing.
Global Reach: As an international journal, IJPP attracts contributions from researchers and practitioners worldwide, fostering a global exchange of ideas and knowledge.
Rigorous Peer Review: The journal maintains a strict peer-review process, ensuring the publication of high-quality and impactful research.
Types of Articles Accepted
International Journal of Parallel Programming accepts a variety of submission types, including:
Research Articles: Detailed reports of original research contributions in the field of parallel and distributed computing.
Review Articles: Comprehensive surveys of the current state of research on specific topics within the journals scope.
Short Communications: Brief reports on novel findings or preliminary research in parallel programming and distributed computing.
Technical Notes: Articles focusing on the development and validation of new techniques, tools, or methodologies for parallel programming.
Case Studies: In-depth studies demonstrating the application of parallel and distributed computing techniques to solve real-world problems.
Special Issues: Collections of articles focused on specific themes or emerging areas within the scope of the journal.

Journal Home: Journal Homepage

Editor-in-Chief: Alexandru Nicolau

Scope: International Journal of Parallel Programming (IJPP) is a peer-reviewed journal that publishes high-quality research articles, reviews, and technical notes on all aspects of parallel programming. The journal aims to advance the field by providing a platform for the dissemination of innovative findings and developments. The journal covers a broad range of topics within parallel programming, including but not limited to:
Parallel Algorithms: Research on the design, analysis, and implementation of parallel algorithms for various computational problems.
Programming Models: Studies on different programming models and paradigms for parallel computing, including message passing, shared memory, and data parallelism.
Parallel Architectures: Research on the architectural support for parallel computing, including multicore processors, distributed systems, and heterogeneous architectures.
Performance Analysis: Studies on the performance evaluation and optimization of parallel programs, including benchmarking, profiling, and tuning.
Distributed Systems: Research on the development and analysis of distributed computing systems and applications.
Concurrency and Synchronization: Studies on techniques for managing concurrency and synchronization in parallel programs, including lock-free data structures and transactional memory.
Parallel Languages and Compilers: Research on programming languages, compilers, and runtime systems designed to support parallel programming.
High-Performance Computing (HPC): Studies on the use of parallel programming techniques in high-performance computing applications.
Parallel Software Development: Research on tools, libraries, and frameworks for developing parallel software.
Applications: Case studies and applications of parallel programming in various domains, including scientific computing, engineering, data analytics, and artificial intelligence.