期刊信息

Journal of Parallel and Distributed Computing (JPDC)

CCF: B 影响因子: 3.4
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出版商:
Elsevier
ISSN:
0743-7315
浏览:
61000
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101
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征稿

Journal of Parallel and Distributed Computing (JPDC) is an academic journal published by Elsevier. (ISSN 0743-7315, impact factor 3.4, CCF B).

Aims & Scope This international journal is directed to researchers, engineers, educators, managers, programmers, and users of computers who have particular interests in parallel processing and/or distributed computing. The Journal of Parallel and Distributed Computing publishes original research papers and timely review articles on the theory, design, evaluation, and use of parallel and/or distributed computing systems. The journal also features special issues on these topics; again covering the full range from the design to the use of our targeted systems. Research Areas Include: • Theory of parallel and distributed computing • Parallel algorithms and their implementation • Innovative computer architectures • Parallel programming • Applications, algorithms and platforms for accelerators • Cloud, edge and fog computing • Data-intensive platforms and applications • Parallel processing of graph and irregular applications • Parallel and distributed programming models • Software tools and environments for distributed systems • Algorithms and systems for Internet of Things • Performance analysis of parallel applications • Architecture for emerging technologies e.g., novel memory technologies, quantum computing • Application-specific architectures e.g., accelerator-based and reconfigurable architecture • Interconnection network, router and network interface architecture
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特刊

特刊：The bridge between High Performance Computing and Large Astronomical Instruments: past, present and future prospects 截稿日期: 2026-12-14 The objectives of this Special Issue of JPDC can be outlined as follows: 1) Document the state of the art: establish a reference collection of HPC solutions deployed in or designed for large-scale astronomical instruments, spanning radio, optical, gamma-ray, gravitational wave, and neutrino domains. 2) Advance foundational methods: showcase research in parallel algorithms, heterogeneous architectures, and AI/ML frameworks that are transforming astro-nomical data processing and enabling new scientific capabilities. 3) Identify open challenges and explore emerging trends that define the research frontier: such as quantum computing, edge-to-cloud continuum, energy-efficient high performance computing, and AI-driven HPC for astronomy, that will shape the next decade of HPC-driven astronomy. Guest editors: Dr. Damien Gratadour, Director of Research at Laboratoire d’Astrophysique de Bordeaux (LAB) – CNRS, France o Expertise in HPC and AI for astronomical instrumentation (optical and radio) o Expertise in astronomical data processing (imaging, spectroscopy, polarimetry) Prof. Oleg Smirnov, South African Radio Observatory(SARAO), South Africa o Expertise in HPC for instrumentation and data processing in radio-astronomy Dr. Adam Bolton, Senior Scientist at SLAC National Accelerator Laboratory – Stanford, United States of America o Lead for US Rubin Data Facility Prof. Florina M. Ciorba, Professor for High Performance Computing – University of Basel, Switzerland o Expertise in HPC, extreme scalability and sustainable computing o Co-founding member of the Swiss SKA consortium (SKACH) o PI and Co-PI of the SPH-EXA simulation framework Special issue information: The Special Issue invites contributions on, but not limited to, the following topics and subtopics: HPC architectures for astronomical data processing: o heterogeneous computing and in particular hybrid systems (e.g. CPU-GPUFPGA) in astronomical instruments; o Opportunities for new hardware architectures (e.g. customized AI architectures, Quantum Computing) to address astronomical instruments and postprocessing workloads;o Edge-to-Cloud continuum computing in the context of large and distributed astronomical observatories. Algorithms and frameworks for astronomical data reduction & analysis: o (e.g., distributed imaging, transient detection, pulsar searching). o parallel and distributed algorithms for imaging, spectroscopy and polarimetry; o reduced / mixed precision approaches for astronomical data processing; o standard libraries integration in astronomical data processing tools; o performance portability of key astronomical software for large observatories; o future prospects for efficient and scalable data processing in astronomy. Real-time and near-real-time processing: o low-latency pipelines for time-domain astronomy; o HPC solutions and triggering strategies for real-time multi-messenger astronomy; o streaming data frameworks for astronomical instrumentation; o real-time systems for telescopes operations and image quality (e.g. adaptive optics, fringe tracking) Machine learning and AI in astronomy: o Deep learning for image reconstruction (e.g., radio interferometry, deconvolution in optical imaging) ; o AI-driven pipelines for automated source detection and classification; o AI-assisted transient events detection for time domain astronomy; o deployment of AI-augmented observatories operations; o scalable machine learning frameworks for large astronomical datasets processing. Data management and storage solutions: o Distributed storage solutions for petabyte-scale astronomical archives; o Efficient data compression and retrieval techniques; o Metadata management and indexing for large astronomical datasets and catalogs. Energy-efficient HPC for sustainable astronomical infrastructures: o Sustainable computing solutions for astronomical observatories; o Power-aware scheduling and resource allocation for astronomical data processing workflows; o Energy-efficient algorithms for large-scale astronomical data processing. Case studies from operational and upcoming astronomical infrastructures (e.g., SKA, Rubin Observatory, CTA), including: o HPC solutions deployed in operational astronomical infrastructures (e.g., ALMA, Rubin Observatory); o Lessons learned from past and present HPC-driven projects in astronomy; o Future challenges and opportunities for HPC in next-generation telescopes (e.g. SKA, CTA). https://www.sciencedirect.com/special-issue/337336/the-bridge-between-high-performance-computing-and-large-astronomical-instruments-past-present-and-future-prospects
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特刊：Quantum Machine Learning: Algorithms, Machine Learning Middleware, and Performance Evaluation 截稿日期: 2027-01-15 Quantum Machine Learning (QML) stands at the intersection of quantum computing and artificial intelligence, promising to accelerate data-driven discovery through new computational paradigms. The rise of Noisy Intermediate-Scale Quantum (NISQ) devices has made it possible to experiment with hybrid quantum–classical workflows that couple quantum subroutines with large-scale classical computing resources. These developments have created new challenges across the quantum computing system stack—from algorithm design and compilation to performance evaluation, middleware integration, and distributed execution. This JPDC Special Issue invites contributions that explore system-level innovations enabling scalable and efficient Quantum Machine Learning. We seek high-quality research that bridges theory and practice—linking quantum algorithms with parallel, distributed, and hybrid infrastructures that can harness near-term and future quantum processors. Guest editors: Dr. Ashfaq Khokhar – Ice College of Engineering – Kansas State University, United States of America Dr. Mary Mehrnoosh Eshaghian-Wilner – Computer Science Discipline – Los Angeles City Community College District, United States of America Dr. Shiplu Sarker – Dept of Electrical and Computer Engineering – Iowa state University, United States of America Dr. Mohamed Abdel Mottaleb – Computer Science – Indiana University Dr. Monu Verma – Computer Science – Indiana University Special issue information: Scope and Topics Quantum Machine Learning Algorithms: Novel models, training strategies, and resource-efficient designs Hybrid Quantum–Classical Learning Architectures: Workflow orchestration, data partitioning, and distributed training frameworks Compilation and Optimization for QML: Circuit optimization, noise-aware compilation, and variational parameter management Middleware and Runtime Systems: Scheduling, synchronization, and resource management for hybrid QML pipelines Performance Evaluation and Benchmarking: Metrics, profiling tools, and comparative analyses of QML workloads Quantum Data Encoding and Representation: Efficient embeddings, feature maps, and fidelity evaluation Cross-Layer Co-Design: Hardware–software co-optimization for QML workloads Quantum Neural Networks and Reinforcement Learning: Design, convergence, and implementation on distributed quantum–classical platforms Integration with HPC, Cloud, and Edge Systems: Scalable execution and containerized quantum services Simulation and Emulation of QML Models on classical and hybrid infrastructures Applications of QML in physics, materials science, healthcare, finance, and engineering https://www.sciencedirect.com/special-issue/338028/special-issue-on-quantum-machine-learning-algorithms-machine-learning-middleware-and-performance-evaluation
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特刊：Federated and Distributed AI Across the Edge–Cloud Continuum 截稿日期: 2027-01-30 This special issue seeks contributions that address these emerging challenges through novel models, architectures, abstractions, and empirical evaluations. Its goal is to bridge the gap between centralized HPC systems and adaptive distributed infrastructures, and between theoretical models of consistency and coordination and their practical implementations at scale. By embracing the convergence of parallel and distributed computing, federated learning, and edge-cloud architectures, this issue invites original research that advances scalable, resilient, and context-aware computing across the full compute continuum. The special issue builds upon research presented at the upcoming 16th International Conference on Parallel Processing and Applied Mathematics (PPAM 2026). While submissions that extend regular or keynote presentations from the conference are particularly encouraged, with the requirement of at least 30% new content beyond the material presented at PPAM, the call is also open to external, general contributions not affiliated with the conference. Guest editors: Dr. Anna Kobusińska, Poznań University of Technology, Poland Prof. Roman Wyrzykowski, Czestochowa University of Technology, Poland Prof. Ewa Deelman, University of Southern California, USA Prof. Maciej Drozdowski, Poznań University of Technology, Poland Special issue information: Topics of Interest (not limited to): Parallel and distributed algorithms for edge-to-cloud computing Federated learning: algorithms, convergence, and communication efficiency across heterogeneous nodes Distributed AI training and inference across edge, fog, and cloud platforms Privacy-preserving and secure federated learning in multi-stakeholder environments Consistency models and coordination protocols in heterogeneous edge-cloud environments Task scheduling and resource management across the compute continuum Data replication, synchronization, and mobility-aware protocols Programming models and abstractions for edge-aware distributed systems Fault tolerance, consensus, and adaptive replication strategies Performance modeling and benchmarking of distributed systems at scale Secure and privacy-preserving computing across edge, fog, and cloud layers Applications in smart cities, cyber-physical systems, and digital twins Manuscript submission information: Submissions must present original, unpublished research and must not be under consideration elsewhere. Manuscripts will be reviewed according to the editorial standards of the Journal of Parallel and Distributed Computing. Papers extending PPAM 2026 contributions should clearly identify the related conference paper and provide a cover letter describing the new material included in the journal version. A substantial extension, such as at least 30% new content beyond the conference version, should be provided. Manuscripts should be prepared according to the JPDC Guide for Authors. Submissions will undergo a rigorous peer-review process in line with JPDC standards. Papers should be submitted through the Submission site for Journal of Parallel and Distributed Computing under the article type "VSI: Federated AI in ECC". Final Manuscript Submission Deadline:30th January 2027 Keywords: Edge–Cloud Continuum; Parallel and Distributed Computing; Federated Learning; Distributed AI; Resource Management and Scheduling; Consistency Models; Fault Tolerance; Programming Models https://www.sciencedirect.com/special-issue/335444/special-issue-on-federated-and-distributed-ai-across-the-edge-cloud-continuum
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