Intelligent fitting global real‐time task scheduling strategy for high‐performance multi‐core systems

Abstract

With the development of high-performance computing, it is possible to solve large-scale computing problems. However, the irregularity and access characteristics of computing problems bring challenges to the realisation and performance optimisation. Improving the performance of a single core makes it challenging to maintain Moore's law, and multi-core processors emerge. A chip brings together multiple universal processor cores of equal status and has the same structure supported by an isomorphic multi-core processor. In high-performance computing, the granularity of computing tasks leads to the complexity of scheduling strategies. Satisfying high system performance, load balancing and processor fault tolerance at a minimum cost is the key to task scheduling in the high-performance field, especially in specific multi-core hardware architecture. In this study, global real-time task scheduling is implemented in a high-performance multi-core system. The system adopts the hybrid scheduling among clusters and the intelligent fitting within clusters to implement the global real-time task scheduling strategy. In the cluster scheduling policy, tasks are allowed to preempt the core with low priority, and the priority of tasks that access memory is dynamically improved, higher than that of all the tasks without memory access. An intelligent fitting method is also proposed. When the data read by the task is in the cache and the cache access ability value of the task is within a reasonable threshold, the priority of the task is promoted to the highest priority, preempting the core without the access memory task. The results show that the intelligently fitting global scheduling strategy for multi-core systems has better performance in the nuclear utilisation rate and task schedulability.