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

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,pre-empting 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.

Offline/online attribute-based searchable encryption scheme from ideal lattices for IoT

1 Introduction The Internet of Things(IoT)has facilitated the development of numerous fields in our lives.However,some equipment in IoT environment lacks sufficient storage and data processing capabilities[1].A feasible strategy is to leverage the powerful computing capabilities of cloud servers to process the data within the IoT devices.

Image Super-Resolution Reconstruction Based on Dual Residual Network

Research shows that deep learning algorithms can ffectivelyimprove a single image's super-resolution quality.However,if the algorithmis solely focused on increasing network depth and the desired result is not achieved,difficulties in the training process are more likely to arise.Simultaneously,the function space that can be transferred from a iow-resolution image to a high-resolution image is enormous,making finding a satisfactory solution difficult.In this paper,we propose a deep learning method for single image super-resolution.The MDRN network framework uses multi-scale residual blocks and dual learning to fully acquire features in low-resolution images.Finally,these features will be sent to the image reconstruction module torestore high-quality images.The function space is constrained by the closedloop formed by dual learning,which provides additional supervision forthe super-resolution reconstruction of the image.The up-sampling processincludes residual blocks with short-hop connections,so that the networkfocuses on learning high-frequency information,and strives to reconstructimages with richer feature details.The experimental results of ×4 and ×8 super-resolution reconstruction of the image show that the quality of thereconstructed image with this method is better than some existing experimental results of image super-resolution reconstruction in subjective visual ffectsand objective evaluation indicators.