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3 articles for “CPU-GPU scheduling”
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AI-PASS: An AI-Based Predictive, Adaptive and Energy Aware Task Scheduling Framework for Heterogeneous Parallel Computing
Abstract: Heterogeneous parallel computing systems are made up of a combination of multicore CPUs, GPUs, and specialized devices to boost computational power and utilize resources. But changes in workload intensity and processor capability, energy consumption and system conditions are dynamic, making efficient task scheduling difficult. Traditional scheduling approaches are based primarily on task properties or the state of the resources employed and can be sub-optimal when workloads are evolving. This paper …
Published in Journal of Operating Systems Development & Trends · Vol. 13, Issue 2, 2026 · pp. 28–34 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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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