多核处理器面向低功耗的共享Cache划分方案

随着多核处理器的发展,片上Cache的容量随之增大,其功耗占整个芯片功耗的比率也越来越大。如何减少Cache的功耗,已成为当今Cache设计的一个热点。本文研究了面向低功耗的多核处理器共享Cache的划分技术(LP-CP)。文中提出了Cache划分框架,通过在处理器中加入失效率监控器来动态地收集程序的失效率,然后使用面向低功耗的共享Cache划分算法,计算性能损耗阈值范围内的共享Cache划分策略。我们在一个共享L2 Cache的双核处理器系统中,使用多道程序测试集测试了面向低功耗的Cache划分:在性能损耗阈值为1%和3%的情况中,系统的Cache关闭率分别达到了20.8%和36.9%。     

"清远市电子政务核心平台与特色应用系统"成果介绍

当前电子政务正如火如荼地在中国展开,已成为政府改革的一种趋势.本文就本人参与建设的"清远市电子政务核心平台与特色应用系统"的项目成果和经验进行阐述介绍,希望能给大家带来一些启发.     

语文教学中如何发挥多媒体技术的作用

有效的教学才是好的教学.多媒体辅助教学利用图文并茂,化抽象为具体为课堂教学提高效率起着十分重要的作用.在小学语文教学中,老师要善于利用多媒体,从学生和教材的实际出发,以人为本,精心设计多媒体课件,激发学生的学习兴趣,提高学生学习的参与度,引导学生自主学习、乐于探究,让学生乐做学习的主人.     

数据驱动技术在WRSAFS中的研究与应用

本文以自动化测试过程中应用数据驱动时遇到的问题为切入点,提出了一种更为先进的数据驱动技术并与自动化测试框架WRSAFS(Win Runner Software Automation Framework Support)相结合,重点分析了数据驱动技术在WRSAFS中的应用及相互关系。借助实际被测试软件,应用此技术设计测试用例并取得了很好的效果。     

网络门禁控制系统的安全稳定性设计

门禁控制系统作为安防系统的主要子系统,在各种场所的应用非常广泛。作为安全防护的第一道防线,其安全性尤为重要。本文主要从门禁控制系统管理软件安全、硬件设备安全、工程安全、网络安全及数据安全等各方面进行了探讨与设计。     

基于Linux的SoPC应用系统设计

利用XILINX公司的EDK软件搭建一个基于PowerPC的片上系统,并且在定制的SoPC系统上移植Linux系统,最后利用此系统完成了一个具体的嵌入式应用系统设计。实验表明,这种基于Linux的SoPC应用系统设计,很好地结合了SoPC和Linux系统的优点,用户可以根据需要灵活地定制出软硬件平台,满足复杂多变的嵌入式需求,加速产品的开发。     

Numerical approximation of solution of nonhomogeneous backward heat conduction problem in bounded region

In this paper we consider a numerical approximation of solution of nonhomogeneous backward heat conduction problem (BHCP) in bounded region based on Tikhonov regularization method. Error estimate at t=0$t=0$ for this method is provided. According to the error estimate, a selection of regularization parameter is given. Meanwhile, a numerical implementation is described and the numerical results show that our algorithm is effective.     

Approximate factorization of multivariate polynomials using singular value decomposition

We describe the design, implementation and experimental evaluation of new algorithms for computing the approximate factorization of multivariate polynomials with complex coefficients that contain numerical noise. Our algorithms are based on a generalization of the differential forms introduced by W. Ruppert and S. Gao to many variables, and use singular value decomposition or structured total least squares approximation and Gauss–Newton optimization to numerically compute the approximate multivariate factors. We demonstrate on a large set of benchmark polynomials that our algorithms efficiently yield approximate factorizations within the coefficient noise even when the relative error in the input is substantial (10⁻³).     

A scalable kernel-based semisupervised metric learning algorithm with out-of-sample generalization ability

In recent years, metric learning in the semisupervised setting has aroused a lot of research interest. One type of semisupervised metric learning utilizes supervisory information in the form of pairwise similarity or dissimilarity constraints. However, most methods proposed so far are either limited to linear metric learning or unable to scale well with the data set size. In this letter, we propose a nonlinear metric learning method based on the kernel approach. By applying low-rank approximation to the kernel matrix, our method can handle significantly larger data sets. Moreover, our low-rank approximation scheme can naturally lead to out-of-sample generalization. Experiments performed on both artificial and real-world data show very promising results.