INTERNATIONAL JOURNAL OF PARALLEL PROGRAMMING

Scope & Guideline

Unleashing the Power of Parallel Processing

Introduction

Delve into the academic richness of INTERNATIONAL JOURNAL OF PARALLEL PROGRAMMING with our guidelines, detailing its aims and scope. Our resource identifies emerging and trending topics paving the way for new academic progress. We also provide insights into declining or waning topics, helping you stay informed about changing research landscapes. Evaluate highly cited topics and recent publications within these guidelines to align your work with influential scholarly trends.
LanguageEnglish
ISSN0885-7458
PublisherSPRINGER/PLENUM PUBLISHERS
Support Open AccessNo
CountryUnited States
TypeJournal
Convergefrom 1986 to 1992, from 1994 to 2024
AbbreviationINT J PARALLEL PROG / Int. J. Parallel Program.
Frequency6 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address233 SPRING ST, NEW YORK, NY 10013

Aims and Scopes

The INTERNATIONAL JOURNAL OF PARALLEL PROGRAMMING focuses on the development and application of parallel programming techniques and methodologies across various computing environments. The journal emphasizes both theoretical and practical aspects of parallel programming, targeting a range of systems from high-performance computing (HPC) to edge devices.
  1. Parallel Computing Techniques:
    The journal investigates various parallel computing techniques including task parallelism, data parallelism, and hybrid models, aimed at optimizing performance across diverse architectures.
  2. Heterogeneous Computing:
    A significant focus on heterogeneous computing environments, exploring the integration of diverse computational resources such as CPUs, GPUs, and FPGAs to enhance processing efficiency.
  3. Algorithm Design and Optimization:
    Research related to the design, analysis, and optimization of algorithms tailored for parallel execution, addressing challenges such as load balancing, resource allocation, and scalability.
  4. Performance Evaluation and Modeling:
    Studies that focus on performance evaluation methodologies, benchmarking, and modeling tools that help in assessing the efficiency of parallel systems and applications.
  5. Applications of Parallel Programming:
    The journal covers a wide array of applications for parallel programming, from scientific computing and machine learning to big data processing and real-time systems.
The journal has seen a rise in interest in several emerging themes that reflect contemporary challenges and innovations in parallel programming. These trends indicate a forward-looking approach to research and application in the field.
  1. Machine Learning and AI in Parallel Programming:
    There is a growing trend towards integrating machine learning and AI techniques into parallel programming frameworks, enabling smarter resource management and optimization strategies.
  2. Dynamic and Adaptive Systems:
    Research is increasingly focused on dynamic and adaptive systems that can adjust their execution strategy in real-time, particularly in heterogeneous environments, enhancing application resilience and performance.
  3. Graph Processing and Analysis:
    The importance of graph processing techniques has surged, driven by the need for efficient algorithms capable of handling large-scale graph data in applications such as social networks and bioinformatics.
  4. Edge Computing and IoT Applications:
    Emerging themes include parallel programming for edge computing and Internet of Things (IoT) applications, highlighting the need for efficient processing in resource-constrained environments.
  5. Energy Efficiency and Sustainability:
    There is a notable emphasis on energy-efficient parallel computing solutions, as researchers seek to minimize the environmental impact of high-performance systems while maintaining performance.

Declining or Waning

As the field of parallel programming evolves, certain themes appear to be losing prominence in the recent publications of the journal. These waning scopes indicate a shift in research focus among authors.
  1. Legacy System Support:
    Research focused on legacy systems and their integration with modern parallel programming techniques has decreased, as newer architectures gain traction and older systems become less relevant.
  2. Basic Parallel Programming Models:
    The exploration of fundamental parallel programming models, such as simple message passing or basic shared memory models, is becoming less common, as the community moves towards more complex and hybrid approaches.
  3. Manual Optimization Techniques:
    There is a noticeable decline in papers discussing manual optimization techniques for parallel programming, as automated tools and machine learning-driven optimization methods gain popularity.
  4. Static Analysis for Parallel Programs:
    Research on static analysis techniques specifically for parallel programming appears to be waning, possibly due to the increasing complexity and dynamic nature of contemporary parallel applications.
  5. Single-threaded Performance Enhancements:
    The focus on enhancing performance in single-threaded applications is diminishing, as the emphasis shifts towards leveraging multi-core and distributed computing environments.

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