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Recent Trends in Parallel Computing Cover

Recent Trends in Parallel Computing

E-ISSN: 2393-8749 | Peer-Reviewed Journal (Refereed Journal) | Hybrid Open Access

About the 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
Subject: Parallel Computing
Publication Format: Hybrid Open Access
Language: English
Copyright Policy: CC BY-NC-ND
Type: Peer-reviewed Journal (Refereed Journal)

Address:

STM Journals, An imprint of Consortium e-Learning Network Pvt. Ltd. A-118, 1st Floor, Sector-63, Noida, U.P. India, Pin - 201301

Editorial Board

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rtpc maintains an Editorial Board of practicing researchers from around the world, to ensure manuscripts are handled by editors who are experts in the field of study.

Editor in Chief

Editor

Prof. Pinaki Mitra, Associate Professor

Indian Institute of Technology, Guwahati, Assam, India,

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Latest Articles

Ahead of Print

A Reviewed Study On Cpu-Optimized Parameter-Efficient Fine- Tuning For Large Language Models To Increase Accuracy Using Lora

The fast proliferation of Large Language Models (LLMs) has increased the need to optimize the process of fine-tuning but the existing workflows that require a GPU are still expensive, intensive, and unavailable to most researchers.

LLMs, LoRA, Parameter- Efficient Fine-Tuning, CPU Optimization, Model Compression, Low- Rank Adaptation, AI Efficiency

Adaptive Task Scheduling And Resource Optimization Using Ai Middleware

Modern distributed and heterogeneous computing systems face significant challenges in dealing with dynamically changing workloads, resource fragmentation, and changing latencies; existing traditional, or rule-based, schedulers are no longer useful in achieving the best system performance.

Adaptive Scheduling, AI Middleware, Resource Optimization, Distributed Systems, Edge Computing, Workload Management

Parallel Privacy-Preserving Adaptive Federated Learning on GPU-Enabled Multi-Core Architectures

The increasing deployment of parallel and distributed intelligent systems has intensified the need for privacy-preserving learning frameworks that can exploit multi-core and GPU-based architectures without centralizing sensitive data.

Parallel computing; Adaptive Federated Learning; Multi-core architectures; GPU accelerators; Differential Privacy; Secure Aggregation; Interconnection networks; Privacy-preserving machine learning.

Design and Optimization of Domain-Specific Languages for High-Performance Computing Applications

The accelerating demand for computational power in scientific, engineering, and data-intensive domains has driven High-Performance Computing (HPC) systems toward unprecedented levels of parallelism and architectural complexity.

Domain-Specific Languages, High-Performance Computing, Compiler Optimization, Parallel Architectures, Performance Portability, Heterogeneous Systems

Application of Proportional-Share with Punishment Principle for Resource Sharing in Parallel Computing Applications

Efficient resource sharing is a cornerstone of high-performance parallel computing.

Big data, cluster, distributed, high performance computing, resource

A Meta-Analysis of the Role of Serverless Computing Models in Modern e-Healthcare Systems

The integration of serverless computing models in e-healthcare systems represents a paradigm shift in healthcare technology infrastructure.

Serverless computing, e-healthcare, cloud computing, FAAS, healthcare information systems, medical data processing