Recent Trends in Parallel Computing
Computer/IT ISSN 2393-8749 3 issues a year Hybrid open accessWhat does hybrid open access mean? This journal has two kinds of articles. Open access articles are free for anyone to read; the author pays an article processing charge for that. All other articles are for subscribers. Authors can also publish at no cost under the subscription-based option.
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About the journal
Journal metrics
Counted from this archive, not supplied by anyone.
- 187Articles published
- 10Published in 2026
- 415Authors
- 2Open access
Journal information
- Title
- Recent Trends in Parallel Computing
- Issues per year
- 3 issues
- ISSN
- 2393-8749
- Publisher
- STM Journals
- Starting year
- 2014
- Subject
- Computer/IT
- Publication format
- Hybrid open access
- Language
- English
- Copyright policy
- CC BY-NC-ND 4.0
- Type
- Peer-reviewed journal (refereed)
- Article processing charge
- ₹10,000 in India · $400 outside India
- Subscription-based publication
- No cost. Author copy, DOI and certificate are chargeable.
Indexed in
Editorial board
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Editor-in-Chief
Computer Science and Engineering, Indian Institute of Technology, Guwahati, India
Latest articles
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Energy-Efficient Parallel Computing for Edge Devices: Techniques, Challenges, and Emerging Research Directions
Abstract: Edge computing brings computation closer to data sources, which supports low-latency applications but places substantial demands on energy-constrained and heterogeneous devices. Parallel execution across multicore CPUs, GPUs, FPGAs, and other accelerators can improve responsiveness, but it also complicates scheduling, power management, and resource allocation. This review examines six closely related technique families: energy-aware task scheduling, dynamic voltage and frequency scaling (DVFS), task offloading and resource allocation, artificial-intelligence-based optimization, hardware acceleration, …
Published in Recent Trends in Parallel Computing · Vol. 13, Issue 2, 2026 · pp. 35–41 Read article
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Memory Reclamation Behavior Under cgroups v2 Versus Legacy cgroups: A Measurement Framework for Containerized Workloads
Abstract: Linux control groups (cgroups) are the kernel mechanism that lets container runtimes such as Docker, containerd, and CRI-O enforce per-container CPU, memory, and I/O limits, and the transition from the legacy cgroups v1 hierarchy to the unified cgroups v2 hierarchy changed several aspects of how the memory controller signals and responds to reclaim pressure most notably through the introduction of memory.low/memory.high soft-limit semantics and system-wide Pressure Stall Information (PSI). Kernel …
Published in Recent Trends in Parallel Computing · Vol. 13, Issue 2, 2026 · pp. 27–34 Read article
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A Comparative Review of Parallel Computing Techniques for High-Performance Computing
Abstract: High-Performance Computing (HPC) has become an essential technology for solving computationally intensive problems in scientific computing, engineering, artificial intelligence, weather forecasting, computational biology, financial modelling, and large-scale data analytics. The increasing complexity and heterogeneity of modern HPC systems have created a strong requirement for efficient parallel computing techniques and portable programming models. Several programming approaches, including Message Passing Interface (MPI), Open Multi-Processing (OpenMP), Compute Unified Device Architecture (CUDA), OpenACC, SYCL, …
Published in Recent Trends in Parallel Computing · Vol. 13, Issue 2, 2026 · pp. 16–26 Read article
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A Hierarchical Orchestration Framework for Parallelized Multi-Entity Web Ingestion and NoSQL Synchronization
Abstract: In the current EdTech sector, gathering structured data from various fragmented web portals is a major challenge for building reliable, real-time data systems. Standard sequential ETL (Extract, Transform, Load) pipelines often encounter significant bottlenecks, as a single failure in one module can halt the entire ingestion process. Furthermore, linear execution is often too slow to meet the demands for large, multi-category datasets. This paper presents a Modular Hierarchical Orchestration framework …
Published in Recent Trends in Parallel Computing · Vol. 13, Issue 2, 2026 · pp. 1–6 Read article
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Polyhedral Compilation for Imperative Loops: Foundations, Advances, and Emerging Frontiers
Abstract: Polyhedral compilation is a mathematically rigorous framework that models imperative loop nests as sets of integer points constrained by affine inequalities, enabling precise reasoning about data dependences and the legality of complex program transformations. Over the past decade, this framework has evolved from an academic formalism into a practical compiler technology deployed in production compilers, high-performance computing libraries, and machine learning acceleration toolchains. This review surveys the theoretical underpinnings of …
Published in Recent Trends in Parallel Computing · Vol. 13, Issue 2, 2026 · pp. 7–15 Read article
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A Review Study on CPU-Optimized Parameter-Efficient Fine-Tuning for Large Language Models to Increase Accuracy Using LoRA
Abstract: 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 graphics processing unit (GPU) are still expensive, intensive, and unavailable to most researchers. This paper is driven by the desire to have a more cost-efficient and democratized version by examining a CPU-efficient implementation of parameter-efficient fine-tuning (PEFT) based on low-rank adaptation (LoRA). The major purpose …
Published in Recent Trends in Parallel Computing · Vol. 13, Issue 1, 2026 · pp. 32–38 Read article
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Adaptive Task Scheduling and Resource Optimization Using AI Middleware
Abstract: 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, specialized schedulers are no longer useful in achieving the best system performance. Such limitations highlight the importance of the adaptive scheduling paradigms that can learn, forecast, and react to the actual real-world conditions in the system. Artificial intelligence middleware is also an attractive solution to this …
Published in Recent Trends in Parallel Computing · Vol. 13, Issue 1, 2026 · pp. 23–31 Read article
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Parallel Privacy-Preserving Adaptive Federated Learning on GPU-Enabled Multi-Core Architectures
Abstract: The increasing deployment of parallel and distributed intelligent systems has intensified the need for privacy-preserving learning frameworks that can exploit multi-core and graphics processing unit (GPU)-based architectures without centralizing sensitive data. This work proposes a parallel adaptive federated learning (AFL) framework that integrates differential privacy and secure aggregation over heterogeneous multi-core and GPU platforms to enhance both data confidentiality and convergence efficiency. The framework dynamically adjusts client participation, learning rates, …
Published in Recent Trends in Parallel Computing · Vol. 13, Issue 1, 2026 · pp. 09–16 Read article