Characterizing and optimizing TPC-C workloads on large-scale systems using SSD arrays在大规模系统上优化 TPC-C 评测程序

Transaction processing performance council benchmark C (TPC-C) is the de facto standard for evaluating the performance of high-end computers running on-line transaction processing applications. Differing from other standard benchmarks, the transaction processing performance council only defines specifications for the TPC-C benchmark, but does not provide any standard implementation for end-users. Due to the complexity of the TPC-C workload, it is a challenging task to obtain optimal performance for TPC-C evaluation on a large-scale high-end computer. In this paper, we designed and implemented a large-scale TPC-C evaluation system based on the latest TPC-C specification using solid-state drive (SSD) storage devices. By analyzing the characteristics of the TPC-C workload, we propose a series of system-level optimization methods to improve the TPC-C performance. First, we propose an approach based on SmallFile table space to organize the test data in a round-robin method on all of the disk array partitions; this can make full use of the underlying disk arrays. Second, we propose using a NOOP-based disk scheduling algorithm to reduce the utilization rate of processors and improve the average input/output service time. Third, to improve the system translation lookaside buffer hit rate and reduce the processor overhead, we take advantage of the huge page technique to manage a large amount of memory resources. Lastly, we propose a locality-aware interrupt mapping strategy based on the asymmetry characteristic of non-uniform memory access systems to improve the system performance. Using these optimization methods, we performed the TPC-C test on two large-scale high-end computers using SSD arrays. The experimental results show that our methods can effectively improve the TPC-C performance. For example, the performance of the TPC-C test on an Intel Westmere server reached 1.018 million transactions per minute.     

基于Cortex-A8智能家居系统的设计与实现

本文设计了一套低成本无线智能家居系统的解决方案,具有安全,可靠,稳定的显著特点,以及智能家居系统市场的广大前景.本文还阐明了系统的体系结构,详细介绍了系统软件和硬件部分的设计与实现。并对关键技术(GPRS、24L01)的流程做了详细的阐述。     

浅析电子商务中博客营销的使用技术与特点

随着博客的兴盛,博客营销作为一种新的营销方式出现在人们面前.本文通过总结博客营销的发展现状和特点,对这种新的电子商务模式进行了探讨和展望.     

浅谈计算机技术在交通运输规划与管理中的应用

自从计算机被应用于交通运输业以来,计算机技术在交通运输规划与管理中就得到了越来越广泛的应用.本文分析了计算机技术在交通运输规划与管理中的应用领域,详细讨论了计算机技术在智能运输系统和道路交通系统仿真中的应用.     

软件开发框架模型——现实世界问题的结构化分析

对软件开发框架模型提出了一种新的构化的分析方法,同时介绍了常用的结构化分析的工具及其更实用情况。     

浅谈网络安全风险的评估方法

本文通过分析网络系统风险的概念及评价体系建设原则,针对风险评估的流程进行探讨,并就应注意的几个问题提出几点建议。以期通过本文的阐述为网络管理人员更好地制定安全策略提供有效的技术支持。     

Locally linear discriminant embedding: An efficient method for face recognition

In this paper an efficient feature extraction method named as locally linear discriminant embedding (LLDE) is proposed for face recognition. It is well known that a point can be linearly reconstructed by its neighbors and the reconstruction weights are under the sum-to-one constraint in the classical locally linear embedding (LLE). So the constrained weights obey an important symmetry: for any particular data point, they are invariant to rotations, rescalings and translations. The latter two are introduced to the proposed method to strengthen the classification ability of the original LLE. The data with different class labels are translated by the corresponding vectors and those belonging to the same class are translated by the same vector. In order to cluster the data with the same label closer, they are also rescaled to some extent. So after translation and rescaling, the discriminability of the data will be improved significantly. The proposed method is compared with some related feature extraction methods such as maximum margin criterion (MMC), as well as other supervised manifold learning-based approaches, for example ensemble unified LLE and linear discriminant analysis (En-ULLELDA), locally linear discriminant analysis (LLDA). Experimental results on Yale and CMU PIE face databases convince us that the proposed method provides a better representation of the class information and obtains much higher recognition accuracies.