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Recent Trends in Parallel Computing

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The Recent Trends in Parallel Computing (RTPC) journal delivers authoritative, peer-reviewed content for academicians, system architects, AI scientists, and technology leaders focusing on HPC, distributed computation, parallel processing, and transformative computing paradigms.

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Recent Trends in Parallel Computing

The Recent Trends in Parallel Computing (RTPC) journal is a distinguished peer-reviewed publication dedicated to advancing cutting-edge research, high-performance computing innovations, and emerging applications in the fields of parallel processing, distributed computing, and large-scale computational systems. This authoritative journal connects computer scientists, system architects, AI researchers, HPC engineers, academicians, and industry professionals to explore breakthrough technologies shaping the future of fast, reliable, and scalable computing.

What This Journal Covers: Key Focus Areas

The Recent Trends in Parallel Computing journal addresses a wide spectrum of essential and emerging topics within parallel and distributed computing, including:

  • Parallel Algorithms & High-Performance Processing: Algorithm design, task scheduling, concurrent programming, performance optimization, parallel models, scalable algorithmic frameworks, and multi-threaded computation.
  • Distributed Computing Architectures: Distributed systems, grid computing, decentralized computation, data distribution strategies, resource coordination, and fault-tolerant distributed environments.
  • GPU, Multicore & Heterogeneous Computing: GPU acceleration, multicore programming, hybrid CPU–GPU systems, vectorization, hardware parallelism, and performance-driven heterogeneous architectures.
  • Cloud, Edge & Cluster Computing: Virtualized resources, serverless architectures, cluster orchestration, scalable cloud platforms, fog/edge computing environments, and high-availability distributed clusters.
  • Parallel Data Processing & Big Data Frameworks: Parallel data analytics, streaming engines, distributed databases, map-reduce systems, scalable storage solutions, and real-time large-scale data processing.

Who Should Read the Journal

This journal serves a diverse community of technology leaders, researchers, and practitioners:

  • Computer scientists and HPC engineers are working on parallel computing frameworks and performance-optimized systems.

  • AI and data science professionals are accelerating large-scale model computations through parallel architectures.

  • Academicians and researchers studying distributed computing, cloud systems, big data platforms, and algorithmic scalability.

  • System architects and developers designing high-performance clusters, edge systems, and hybrid computing environments.

  • Policy makers and technology regulators are shaping standards for secure, efficient, and scalable computational infrastructure.

Why It Matters

As global computational demands increase across AI, big data, scientific simulations, and automation, the Recent Trends in Parallel Computing journal plays a crucial role by:

  • Publishing cutting-edge research that improves speed, efficiency, and reliability in large-scale computing

  • Encouraging advancements in scalable architectures, HPC frameworks, and parallel algorithms

  • Supporting innovation across cloud, edge, GPU-based, and distributed computing ecosystems

  • Promoting sustainable and energy-efficient computational models

  • Fostering interdisciplinary collaboration among computing, engineering, data science, and emerging technology domains

With rigorous peer review, global relevance, and technical excellence, the journal advances the future of parallel computing and drives innovation in high-performance computational infrastructure.

Subscription & Access Options

The Recent Trends in Parallel Computing journal offers flexible access modes designed for researchers, computing institutes, universities, and technology-driven organizations.

  • Digital Access: A fully integrated online platform offering instant access to current issues, archived volumes, and a comprehensive, searchable database of peer-reviewed research.
  • Print Edition: High-quality printed copies delivered to universities, research laboratories, technology centers, libraries, and individual subscribers.

Subscription plans, institutional licenses, and bulk access options are available through the STM Journals distribution network.

Why Choose STM Journals?

STM Journals is committed to delivering excellence in scientific and technical publishing through:

  • Rigorous peer review ensuring reliable, high-quality research

  • Global reach enabling widespread dissemination of innovations in computing

  • Multiple access formats supporting both digital-first environments and traditional libraries

  • Dedicated focus on advancing high-performance computing, parallel processing, and scalable system design

For developers, engineers, researchers, and technology innovators shaping the future of computational performance, the Recent Trends in Parallel Computing journal is a trusted and invaluable resource.

About Journal

Recent Trends in Parallel Computing Recent Trends in Parallel Computing [2393-8749(e)] is a peer-reviewed hybrid open-access journal launched in 2014. Parallel computing is a form of computation in which many calculations can be done at the same time and it works on the principle that large problems can often be divided into smaller ones, which are then solved in parallel. Specialized parallel computer architectures are sometimes used aboard traditional processors, to quicken specific tasks this increases the speed of execution of the task.

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Journal Information

Title: Recent Trends in Parallel Computing Abbreviation: RTPC
Issues Per Year: 3 Issues E-ISSN: 2393-8749
Publisher: STM Journals, An imprint of Consortium e-Learning Network Pvt. Ltd.
DOI: 10.37591/RTPC Starting Year: 2014
Language: English Publication Format: Hybrid Open Access
Subject: Parallel Computing Type: Peer-reviewed Journal (Refereed Journal)
Copyright Policy: CC BY-NC-ND

Indexing & Metrics

Indexing: Advanced Science Index (ASI), Index Copernicus, Citefactor, Scientific Literature (SCILIT), Google Scholar

ICV Value: 2022 : 60.42

Impact Factor (SJIF): 2024: 6.003, 2023: 6.022, 2022: 6.04, 2021: 6.112, 2020: 6.124

Focus & Scope

Parallel Computing, Network Topologies & Multi-Core Systems

An integrated systems roadmap—bridging Cayley graph topologies and hypercubic networks with GPU-accelerated linear algebra and dynamic thermal management.

Interconnection Networks

Designing low-diameter (d,k) graphs and Cayley graphs. Implementing Hypercubes, Butterfly networks, and Cube-Connected Cycles for all-to-all broadcast and Wavelength Division Multiplexing.

NoC & Mesh-of-Trees

Optimizing Network-on-Chip (NoC) through Deterministic Routing (DOR) and Particle Swarm Optimization. Utilizing Mesh-of-Tree (MoT) and Torus topologies to minimize communication costs.

Hierarchical & Pyramid Nets

Applying Pyramid and Convolutional Hypercube Pyramids for RGB-D object recognition. Bridging deep learning and graph embeddings via coarse-to-fine feature representation.

Task & Data Parallelism

Leveraging MPI and OpenMP for multithreaded performance. Managing load imbalance and cache coherence via directory schemes and scalable multi-processor runtime systems.

Hybrid Algorithms (MAGMA)

Implementing Cholesky and LU factorizations using GPU accelerators. Optimizing BLAS/LAPACK software for high bandwidth utilization and reduced power consumption.

Heterogeneous Multicore

Managing Dynamic Voltage and Frequency Scaling (DVFS) and activity migration. Utilizing hierarchical bus structures and single-ISA heterogeneous architectures for job matching.

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