Design of Dynamic Scheduling Algorithm for Parallel Data Processing in Multi-Core Chips

Multi-core processors are widely used in modern distributed systems, but traditional scheduling algorithms face challenges in task response time and resource utilization under high-load scenarios. This paper aims to optimize task scheduling efficiency in multi-core environments through a novel dynamic scheduling approach. We design a dynamic scheduling algorithm based on multi-factor evaluation, which integrates three key components: a task priority evaluation model using weighted calculations of task urgency, resource demands, and dependency relationships; an improved minimum completion time (IMCT) strategy for load balancing; and an adaptive resource allocation method with threshold control mechanism. The experimental evaluation is conducted on the Intel i712700 K processor platform using the PARSEC benchmark suite. Results demonstrate significant performance improvements: system throughput increases by $35.7 \%$, CPU utilization reaches $87.6 \%$, and average task response time reduces by $42.3 \%$ compared to traditional scheduling algorithms. In high-load environments, the algorithm exhibits excellent scalability, maintaining scheduling overhead within 0.8 ms while processing 1000 concurrent tasks. The proposed algorithm provides an effective solution for task scheduling optimization in large-scale distributed systems, particularly suitable for scenarios with dynamic workload characteristics.