PARALLEL COMPUTING

Scope & Guideline

Connecting Ideas Across the Spectrum of Computing.

Introduction

Delve into the academic richness of PARALLEL COMPUTING 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
ISSN0167-8191
PublisherELSEVIER
Support Open AccessNo
CountryNetherlands
TypeJournal
Convergefrom 1984 to 2024
AbbreviationPARALLEL COMPUT / Parallel Comput.
Frequency10 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressRADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS

Aims and Scopes

The journal 'PARALLEL COMPUTING' focuses on advancing the field of parallel computing through innovative research and methodologies. It encompasses a wide range of topics that address the challenges and opportunities in high-performance computing, distributed systems, and optimization techniques. The journal aims to publish high-quality research that contributes to the theoretical and practical aspects of parallel computation.
  1. Parallel Algorithms and Optimization Techniques:
    Research on novel algorithms designed for efficient execution on parallel architectures, including improvements in computational speed and resource utilization.
  2. High-Performance Computing Architectures:
    Studies focusing on the design and implementation of hardware architectures that support parallel processing, including GPUs, FPGAs, and many-core processors.
  3. Distributed Systems and Cloud Computing:
    Exploration of methods and frameworks for managing and optimizing distributed computing environments, including cloud-based solutions and resource allocation strategies.
  4. Performance Analysis and Benchmarking:
    Investigations into the performance characteristics of parallel systems, including benchmarking methodologies and tools for evaluating system efficiency.
  5. Applications of Parallel Computing:
    Application-driven research that demonstrates the use of parallel computing techniques in various domains such as scientific computing, machine learning, and data analytics.
  6. Programming Models and Tools:
    Development of programming models, languages, and tools that facilitate the implementation of parallel algorithms and applications.
Recent publications in 'PARALLEL COMPUTING' have highlighted several emerging themes that reflect current trends in the field. These themes indicate a shift towards more complex and integrated approaches to parallel computing.
  1. Machine Learning and AI Integration:
    An increasing number of papers are focusing on the intersection of parallel computing with machine learning and artificial intelligence, particularly in optimizing algorithms for large datasets and model training.
  2. Heterogeneous Computing:
    There is a growing interest in heterogeneous computing environments that utilize a mix of CPUs, GPUs, and FPGAs to enhance computational efficiency and flexibility.
  3. Energy-Efficient Computing:
    Research aimed at developing energy-efficient parallel computing strategies is on the rise, driven by the need for sustainable computing practices and the reduction of operational costs.
  4. Federated Learning and Distributed AI:
    Emerging studies on federated learning showcase a trend towards decentralized approaches in AI training, leveraging parallel computing to enhance privacy and efficiency.
  5. Quantum Computing Simulations:
    Interest in quantum computing and its simulation on classical parallel architectures is gaining momentum, indicating a future direction for parallel computing research.
  6. Data-Driven Parallelism:
    The rise of big data analytics has led to an emphasis on data-driven approaches to parallelism, focusing on optimizing data partitioning and management in parallel applications.

Declining or Waning

While the journal has a broad focus, certain themes have shown a decline in frequency within recent publications. This may reflect shifts in research priorities or the maturation of certain areas within the field.
  1. Traditional MPI Applications:
    Research centered on conventional Message Passing Interface (MPI) applications has seen a decrease, possibly due to the emergence of newer programming models and paradigms that offer more flexibility and ease of use.
  2. Static Scheduling Algorithms:
    Studies focusing on static scheduling approaches have become less prevalent, as dynamic and adaptive scheduling methods gain traction due to their improved performance in variable workloads.
  3. Low-Performance Parallel Systems:
    Research related to low-performance or legacy parallel systems is waning, as the community shifts towards optimizing modern high-performance architectures.
  4. Single-Core Optimization:
    Focus on optimizing applications for single-core performance has diminished, reflecting the industry's move towards parallelism and multi-core processing as standard practice.

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