Research on Multi-Core Processor Analysis for WCET Estimation

Real-time system timing analysis is crucial for estimating the worst-case execution time(WCET)of a program.To achieve this,static or dynamic analysis methods are used,along with targeted modeling of the actual hardware system.This literature review focuses on calculating WCET for multi-core processors,providing a survey of traditional methods used for static and dynamic analysis and highlighting the major challenges that arise from different program execution scenarios on multi-core platforms.This paper outlines the strengths and weaknesses of current methodologies and offers insights into prospective areas of research on multi-core analysis.By presenting a comprehensive analysis of the current state of research on multi-core processor analysis for WCET estimation,this review aims to serve as a valuable resource for researchers and practitioners in the field.

Hybridization of Metaheuristics Based Energy Efficient Scheduling Algorithm for Multi-Core Systems

The developments of multi-core systems(MCS)have considerably improved the existing technologies in thefield of computer architecture.The MCS comprises several processors that are heterogeneous for resource capacities,working environments,topologies,and so on.The existing multi-core technology unlocks additional research opportunities for energy minimization by the use of effective task scheduling.At the same time,the task scheduling process is yet to be explored in the multi-core systems.This paper presents a new hybrid genetic algorithm(GA)with a krill herd(KH)based energy-efficient scheduling techni-que for multi-core systems(GAKH-SMCS).The goal of the GAKH-SMCS tech-nique is to derive scheduling tasks in such a way to achieve faster completion time and minimum energy dissipation.The GAKH-SMCS model involves a multi-objectivefitness function using four parameters such as makespan,processor utilization,speedup,and energy consumption to schedule tasks proficiently.The performance of the GAKH-SMCS model has been validated against two datasets namely random dataset and benchmark dataset.The experimental outcome ensured the effectiveness of the GAKH-SMCS model interms of makespan,pro-cessor utilization,speedup,and energy consumption.The overall simulation results depicted that the presented GAKH-SMCS model achieves energy effi-ciency by optimal task scheduling process in MCS.

SQL Server性能优化的技术分析

随着客户端/服务器技术和多层架构的进步,系统效率问题日益引发广泛讨论。由于缺乏数据库管理系统(DBMS)提供的优化方法和工具,以及基于微软技术的数据库管理员和应用程序开发人员的知识,无法对设计和服务执行系统进行优化。文章回顾了提高SQL Server实例性能时需要考虑的目标,并阐述了用于优化查询的技术。

Shared Cache Based on Content Addressable Memory in a Multi-Core Architecture

Modern shared-memory multi-core processors typically have shared Level 2(L2)or Level 3(L3)caches.Cache bottlenecks and replacement strategies are the main problems of such architectures,where multiple cores try to access the shared cache simultaneously.The main problem in improving memory performance is the shared cache architecture and cache replacement.This paper documents the implementation of a Dual-Port Content Addressable Memory(DPCAM)and a modified Near-Far Access Replacement Algorithm(NFRA),which was previously proposed as a shared L2 cache layer in a multi-core processor.Standard Performance Evaluation Corporation(SPEC)Central Processing Unit(CPU)2006 benchmark workloads are used to evaluate the benefit of the shared L2 cache layer.Results show improved performance of the multicore processor’s DPCAM and NFRA algorithms,corresponding to a higher number of concurrent accesses to shared memory.The new architecture significantly increases system throughput and records performance improvements of up to 8.7%on various types of SPEC 2006 benchmarks.The miss rate is also improved by about 13%,with some exceptions in the sphinx3 and bzip2 benchmarks.These results could open a new window for solving the long-standing problems with shared cache in multi-core processors.

Python Server Page Performance Analysis and Modeling

Today, in the field of computer networks, new services have been developed on the Internet or intranets, including the mail server, database management, sounds, videos and the web server itself Apache. The number of solutions for this server is therefore growing continuously, these services are becoming more and more complex and expensive, without being able to fulfill the needs of the users. The absence of benchmarks for websites with dynamic content is the major obstacle to research in this area. These users place high demands on the speed of access to information on the Internet. This is why the performance of the web server is critically important. Several factors influence performance, such as server execution speed, network saturation on the internet or intranet, increased response time, and throughputs. By measuring these factors, we propose a performance evaluation strategy for servers that allows us to determine the actual performance of different servers in terms of user satisfaction. Furthermore, we identified performance characteristics such as throughput, resource utilization, and response time of a system through measurement and modeling by simulation. Finally, we present a simple queue model of an Apache web server, which reasonably represents the behavior of a saturated web server using the Simulink model in Matlab (Matrix Laboratory) and also incorporates sporadic incoming traffic. We obtain server performance metrics such as average response time and throughput through simulations. Compared to other models, our model is conceptually straightforward. The model has been validated through measurements and simulations during the tests that we conducted.

FedAdaSS: Federated Learning with Adaptive Parameter Server Selection Based on Elastic Cloud Resources

The rapid expansion of artificial intelligence(AI)applications has raised significant concerns about user privacy,prompting the development of privacy-preserving machine learning(ML)paradigms such as federated learning(FL).FL enables the distributed training of ML models,keeping data on local devices and thus addressing the privacy concerns of users.However,challenges arise from the heterogeneous nature of mobile client devices,partial engagement of training,and non-independent identically distributed(non-IID)data distribution,leading to performance degradation and optimization objective bias in FL training.With the development of 5G/6G networks and the integration of cloud computing edge computing resources,globally distributed cloud computing resources can be effectively utilized to optimize the FL process.Through the specific parameters of the server through the selection mechanism,it does not increase the monetary cost and reduces the network latency overhead,but also balances the objectives of communication optimization and low engagement mitigation that cannot be achieved simultaneously in a single-server framework of existing works.In this paper,we propose the FedAdaSS algorithm,an adaptive parameter server selection mechanism designed to optimize the training efficiency in each round of FL training by selecting the most appropriate server as the parameter server.Our approach leverages the flexibility of cloud resource computing power,and allows organizers to strategically select servers for data broadcasting and aggregation,thus improving training performance while maintaining cost efficiency.The FedAdaSS algorithm estimates the utility of client systems and servers and incorporates an adaptive random reshuffling strategy that selects the optimal server in each round of the training process.Theoretical analysis confirms the convergence of FedAdaSS under strong convexity and L-smooth assumptions,and comparative experiments within the FLSim framework demonstrate a reduction in training round-to-accuracy by 12%–20%compared to the Federated Averaging(FedAvg)with random reshuffling method under unique server.Furthermore,FedAdaSS effectively mitigates performance loss caused by low client engagement,reducing the loss indicator by 50%.

A Design of an SMTP Email Server

This study developed a mail server program using Socket API and Python.The program uses the Hypertext Transfer Protocol(HTTP)to receive emails from browser clients and forward them to actual email service providers via the Simple Mail Transfer Protocol(SMTP).As a web server,it handles Transmission Control Protocol(TCP)connection requests from browsers,receives HTTP commands and email data,and temporarily stores the emails in a file.Simultaneously,as an SMTP client,the program establishes a TCP connection with the actual mail server,sends SMTP commands,and transmits the previously saved emails.In addition,we also analyzed security issues and the efficiency and availability of this server,providing insights into the design of SMTP mail servers.

Excel+SQL Server在实验成绩信息管理中的应用研究

深入探讨了如何利用Excel和SQL Server集成化地管理和分析大学物理实验成绩信息。通过建立数据库、导入导出数据、数据查询与分析及使用Excel的数据透视表和图表功能,可以高效地管理实验成绩数据,进而深入分析学生的学习情况和教学效果。

基于ArcGIS Server的网络农田地理信息系统设计

该文为提高农田信息管理智能化水平,应用ArcGIS Server应用平台,完成对网络农田地理信息系统的设计。首先,设计和搭建系统架构和系统运行环境。其次,分别设计用户登录、信息表达、查询统计、专题图制作和空间分析等模块,保证系统功能设计效果。最后,从应用层、服务层、数据层3个方面,完成对系统设计和实现。结果表明,在ArcGIS Server应用平台的应用背景下,该文所设计的网络农田地理信息系统具有操作简单、安全可靠等特点,可以实时共享和发布农田空间信息,完全符合预期设计标准和要求。希望通过这次研究,为相关人员提供有效的借鉴和参考。

基于FreeRTOS的多连接TCP Server设计

文中提出了一种支持多客户端连接的TCP Server的嵌入式软硬件设计方案。该网络服务器以Cortex-M4芯片STM32F407VET6为核心控制器,通过RMII接口与LAN8742A以太网收发器进行网络数据交互。此外,文中将LwIP协议栈移植到FreeRTOS实时操作系统中,并根据TCP客户端连接情况,动态创建或消除相应客户端的网络通信线程。研究证明,TCP Server最大可支持4个客户端同时连接,进行稳定的网络数据传输,有效解决了嵌入式网络系统仅单点连接的问题。

Enrichment of Fetal Nucleated Red Blood Cells by Multi-core Magnetic Composite Particles for Non-invasive Prenatal Diagnosis

A novel kind of multi-core magnetic composite particles, the surfaces of which were respectively mo- dified with goat-anti-mouse IgG and antitransferrin receptor（anti-CD71）, was prepared. The fetal nucleated red blood cells（FNRBCs） in the peripheral blood of a gravida were rapidly and effectively enriched and separated by the mo- dified multi-core magnetic composite particles in an external magnetic field. The obtained FNRBCs were used for the identification of the fetal sex by means of fluorescence in situ hybridization（FISH） technique. The results demonstrate that the multi-core magnetic composite particles meet the requirements for the enrichment and speration of FNRBCs with a low concentration and the accuracy of detetion for the diagnosis of fetal sex reached to 95%. Moreover, the obtained FNRBCs were applied to the non-invasive diagnosis of Down syndrome and chromosome 3p21 was de- tected. The above facts indicate that the novel multi-core magnetic composite particles-based method is simple, relia- ble and cost-effective and has opened up vast vistas for the potential application in clinic non-invasive prenatal diag- nosis.

基于SQL Server的教学评价管理系统设计

教学评价是教学工作中的重要环节,传统的教学评价方式难以实现对学生学习过程性评价的及时存储及更新,存在时效性不高、难以量化的问题,很难依据评价实现对学生学习行为的及时指导。文章主要针对传统教学评价存在的以上问题,综合使用SQL Server开发平台设计了一套便捷、高效的教学评价管理系统,实现了混合式教学中相关教学数据的实时增删改查基本功能以及教学评价与管理等功能,从而实现对学生学业的多维过程性评价,进而指导教学。

基于改进Apriori算法的SQL Server数据库入侵识别方法

常规的数据库入侵识别方法以识别入侵行为数据为主,各种识别模块导致数据库入侵识别的恶意操作增加。因此,设计了基于改进Apriori算法的SQL Server数据库入侵识别方法。首先,识别SQL Server数据库入侵类型,将数据库中的关键词进行条件查询判断。其次,提取入侵攻击的语句特征,避免附加语句执行恶意操作。最后,基于改进Apriori算法迭代数据库的频繁项集,通过逐层识别的形式迭代寻找SQL Server数据库中所有项集的关系,并形成识别规则,提升了数据库入侵识别性能。

基于Java和SQL Server的仓库管理系统的设计与开发

本文针对依靠大量人力、物力的传统管理模式存在的缺陷,以药品的仓库管理为例,采用Java语言和SQL Server数据库,设计并开发了一套精准、高效、便捷的仓库管理系统。分析了系统的业务需求和功能需求,从功能设计和数据库设计两个方面设计了系统的总体方案,实现了系统的可视化图形用户界面、数据库功能、入库功能、出库功能。

Variation-Aware Task Mapping on Homogeneous Fault-Tolerant Multi-Core Network-on-Chips

A variation-aware task mapping approach is proposed for a multi-core network-on-chips with redundant cores, which includes both the design-time mapping and run-time scheduling algorithms. Firstly, a design-time genetic task mapping algorithm is proposed during the design stage to generate multiple task mapping solutions which cover a maximum range of chips. Then, during the run, one optimal task mapping solution is selected. Additionally, logical cores are mapped to physically available cores. Both core asymmetry and topological changes are considered in the proposed approach. Experimental results show that the performance yield of the proposed approach is 96% on average, and the communication cost, power consumption and peak temperature are all optimized without loss of performance yield.

Multi-core optimization for conjugate gradient benchmark on heterogeneous processors

Developing parallel applications on heterogeneous processors is facing the challenges of 'memory wall',due to limited capacity of local storage,limited bandwidth and long latency for memory access. Aiming at this problem,a parallelization approach was proposed with six memory optimization schemes for CG,four schemes of them aiming at all kinds of sparse matrix-vector multiplication (SPMV) operation. Conducted on IBM QS20,the parallelization approach can reach up to 21 and 133 times speedups with size A and B,respectively,compared with single power processor element. Finally,the conclusion is drawn that the peak bandwidth of memory access on Cell BE can be obtained in SPMV,simple computation is more efficient on heterogeneous processors and loop-unrolling can hide local storage access latency while executing scalar operation on SIMD cores.

Theoretical Analysis on Inter-Core Crosstalk Suppression Model for Multi-Core Fiber

Decreasing mode coupling coefficient(κ) is an effective approach to suppress the inter-core crosstalk. Therefore, we deploy a low index rod and rectangle trench in the middle of two neighboring cores to reduce κ so that the overlap of electric field distribution can be suppressed. We also propose approximate analytical solution(AAS) for κ of two crosstalk suppression models, which are two cores with one low index rod deployed in the middle and two cores with one low index rectangle trench deployed in the middle. We then do some modification for the results obtained by AAS and the modified results are proved to agree well with that obtained by finite element method(FEM). Therefore, we can use the modified AAS to get inter-core crosstalk for abovementioned two models quickly.

Modeling of Few-Mode Multi-Core Optical Fiber Channel Based on Non-Uniform Mode Field Distribution

In this paper, the influencing factors that affect few-mode and multi core optical fiber channel are analyzed in a comprehensive way. The theoretical modeling and computer simulation of the information channel are carried out and then the modeling scheme of few-mode multicore optical fiber channel based on non-uniform mode field distribution is put forward. The proposed modeling scheme can not only exponentially increases the system capacity through fewmode multi-core optical fiber channel, but has better transmission performance compared to the channel of the same type to the uniform channel revealing from the simulation results.

Cache performance optimization of irregular sparse matrix multiplication on modern multi-core CPU and GPU

This paper focuses on how to optimize the cache performance of sparse matrix-matrix multiplication(SpGEMM).It classifies the cache misses into two categories;one is caused by the irregular distribution pattern of the multiplier-matrix,and the other is caused by the multiplicand.For each of them,the paper puts forward an optimization method respectively.The first hash based method removes cache misses of the 1 st category effectively,and improves the performance by a factor of 6 on an Intel 8-core CPU for the best cases.For cache misses of the 2nd category,it proposes a new cache replacement algorithm,which achieves a cache hit rate much higher than other historical knowledge based algorithms,and the algorithm is applicable on CELL and GPU.To further verify the effectiveness of our methods,we implement our algorithm on GPU,and the performance perfectly scales with the size of on-chip storage.

Mobile Edge Computing Servers Deployment with Improved Genetic Algorithm in Cellular Internet of Things

Mobile edge computing(MEC)provides services to devices and reduces latency in cellular internet of things(IoT)networks.However,the challenging problem is how to deploy MEC servers economically and efficiently.This paper investigates the deployment problem of MEC servers of the real-world road network by employing an improved genetic algorithm(GA)scheme.We first use the threshold-based K-means algorithm to form vehicle clusters according to their locations.We then select base stations(BSs)based on clustering center coordinates as the deployment locations set for potential MEC servers.We further select BSs using a combined simulated annealing(SA)algorithm and GA to minimize the deployment cost.The simulation results show that the improved GA deploys MEC servers effectively.In addition,the proposed algorithm outperforms GA and SA algorithms in terms of convergence speed and solution quality.