一种典型的A／D转换电路设计与测试

本文介绍一种典型的音频Ａ／Ｄ转换器的原理和性能测试的方法。较详细的阐述了工程中常用的Ａ／Ｄ转换器的主要性能－非线性失真和频率特性的测试方法，并把频谱分析中的ＦＦＴ快速算法用于测试中，得到了简单，实用，满意的结果。     

一个有效的数组下标相关性测试方法

在并行化编译中,DO-循环中数组下标对之间数据相关性的有效测试一直是人们研究的问题、本文针对两种常用的相关性测试方法——GCD测试方法Banerjee不等式测试方法—存在的不足,提出了一种区间方程测试方法,它是上述两种方法的综合,但在不增加测试复杂性的前提下具有更高的精度。     

基于多信号流图模型的导弹系统级故障诊断技术研究

为了实现对导弹装备系统级故障的诊断,提出了基于多信号流图模型的导弹故障诊断方法;该方法利用多信号流图模型建立系统的层次结构模型,在对导弹系统级测试性指标进行分析的同时,生成故障模式-测试相关性矩阵;在导弹测试出现故障时,利用故障模式与测试的相关性对相关性矩阵进行化简后,可将故障定位到具体LRU;最后,利用某型导弹电气系统对该方法进行了验证,试验结果证明,该方法对于导弹系统级的测试性建模与分析是有效的,利用该模型能够对导弹系统级故障进行准确定位。     

基于联合压力测试的Web应用程序性能预测方法

提出了一种基于联合压力测试的性能预测方法：在客户端对Web应用程序进行一系列负载测试的同时，对运行Web应用程序的服务器进行资源消耗，接着根据测试结果建立性能模型，做出性能预测。在一个典型的Web应用系统上应用了这种方法，通过与传统负载测试结果的比较，证明了这种方法的可行性、准确性和效率。     

Web应用程序运行响应时间的实验研究

性能测试是保证Web应用程序成功的重要手段，但是如果不能采用正确的方法测试或者不能完全理解测试结果对测试环境的依赖性，那么测试结果很可能会起误导作用．响应时间则是性能的一个重要指标，特别是从用户的角度来看．因此进行了一系列探索性的实验来研究Web应用程序运行响应时间的特性，用统计的方法对实验结果做了分析，发现Web事务的响应时间和用户数之间呈线性关系，测试的时间和测试配置对响应时间也有一定影响．     

多故障的电子设备系统诊断设计方法研究

针对复杂电子设备在使用过程中多故障诊断难的问题,以相关性模型为基础,提出了一种多故障诊断的设计方法;首先,根据相关性模型确定测试D矩阵,并获得组成单元与测试次数的关系矩阵;其次,根据每次实际测试的结果构建测试结果矩阵,并计算获得测试故障单元矩阵;最后,通过综合测试故障单元矩阵与测试次数关系矩阵的数据,确定故障单元的概率,根据故障概率定位出故障单元;通过实例验算表明:多故障诊断方法可准确定位故障单元,大幅降低误修率。     

RRTL：一种缩短随机测试长度的方法

本文提出了一种新的缩短随机测试序列长度的方法－ＲＲＴＬ法，它是在找到电路中难测故障分布的基础上，通过对电路的初始输入施加量佳概率的信号，使得最难测故障的测试长度变为极小值，因而也就大大缩短了测试时间。     

测试非确定性系统的一种结构化方法

本文对非确定性的分布式系统的相容性测试提出一种新的方法，这种方法是经典的状态识别方法的一种扩展。我们提出了集合识别的概念，重新定义了区别序列和ＵＩＯ序列，最后我们给出了层次测试的方法。     

基于多信号模型的测试性分析方法研究

多信号模型是一种简单而有效的建模表示方法,用于系统测试性分析、故障诊断等;在产品结构的基础上以分层有向图表示信号流向和各组成单元的连接关系,通过定义组成单元、信号、故障模式、测试等建立多信号模型,获取故障与测试之间的相关性来实现对产品的测试性分析;结合示例介绍了多信号模型的表示方法,给出故障-测试依存矩阵以及未检测故障、模糊组、冗余测试、隐藏故障、冒充故障、故障检测率和故障隔离率的具体分析过程和算法;算法分析结果与应用TADS软件分析结果相一致。     

面向复用的测试设计模型的研究与实现

本文讨论了测试复用的模型和方法．在对测试设计中的可复用资产及其可复用性进行了深入分析之后，提出了一种面向复用的软件测试设计模型ROTDM．该模型定义了测试的概念模型。抽象并提取了不同颗粒度的可复用测试资产，支持引用、组装和继承等测试复用方法．在此基础上，论文讨论了基于该模型的测试设计脚本以及基于XML的数据交换，并介绍了模型实现工具的设计与实现，最后给出了测试设计复用的实验结果．     

The direction vector I test

The GCD and Banerjee tests are the standard data dependence tests used to determine whether a loop may be parallelized/vectorized. In an earlier work, (1991) the authors presented a new data dependence test, the I test, which extends the accuracy of the GCD and the Banerjee tests. In the original presentation, only the case of general dependence was considered, i.e., the case of dependence with a direction vector of the form (*,*,...,*). In the present work, the authors generalize the I test to check for data dependence subject to an arbitrary direction vector     

The Banerjee–Wolfe and GCD Tests on Exact Data Dependence Information

The GCD test and the Banerjee–Wolfe test are the two tests traditionally used to determine statement data dependence, subject to direction vectors, in automatic vectorization/parallelization of loops. In an earlier study, a sufficient condition for the accuracy of the Banerjee–Wolfe test was stated and proved. In that work, we only considered the case of general data dependence, i.e., the case of data dependence without direction vector information. In this paper, we extend the previous result to the case of data dependence subject to an arbitrary direction vector. We also state and prove a sufficient condition for the accuracy of a combination of the GCD and Banerjee–Wolfe tests. Furthermore, we show that the sufficient conditions, for the accuracy of the Banerjee–Wolfe test and the accuracy of a combination of the GCD and Banerjee–Wolfe tests are necessary conditions as well. Finally, we demonstrate how these results can be used in actual practice to obtain exact data dependence information.     

An experimental evaluation of data dependence analysis techniques

Optimizing compilers rely upon program analysis techniques to detect data dependences between program statements. Data dependence information captures the essential ordering constraints of the statements in a program that need to be preserved in order to produce valid optimized and parallel code. Data dependence testing is very important for automatic parallelization, vectorization, and any other code transformation. In this paper, we examine the impact of data dependence analysis in practice. A number of data dependence tests have been proposed in the literature. In each test, there are different trade offs between accuracy and efficiency. We present an experimental evaluation of several data dependence tests, including the Banerjee test, the I-Test, and the Omega test. We compare these tests in terms of data dependence accuracy, compilation efficiency, effectiveness in parallelization, and program execution performance. We analyze the reasons why a data dependence test can be inexact and we explain how the examined tests handle such cases. We run various experiments using the Perfect Club Benchmarks and the scientific library Lapack. We present the measured accuracy of each test and the reasons for any approximation. We compare these tests in term's of efficiency and we analyze the trade offs between accuracy and efficiency. We also determine the impact of each data dependence test on the total compilation time. Finally, we measure the number of loops parallelized by each test and we compare the execution performance of each benchmark on a multiprocessor. Our results indicate that the Omega test is more accurate, but also very inefficient in the cases where the other two tests are inaccurate. In general, the cost of the Omega test is high and uses a significant percentage of the total compilation time. Furthermore, the difference in accuracy of the Omega test over the Banerjee test and the l-Test does not improve parallelization and program execution performance.     

The power test for data dependence

A data dependence decision algorithm called the power test is introduced. The power test is a combination of the extended GCD algorithm and the Fourier-Motzkin method to eliminate variables in a system of inequalities. This is the first test that can generate the information needed for some advanced transformations, and that can handle complex simultaneous loop limits. Previous work in data dependence decision algorithms is reviewed. Some examples which motivated the development of this test are examined, including those which demonstrate the additional power of the power test. Although it may be too expensive for use as a general-purpose dependence test in a compiler, the power test has proved useful in an interactive program restructuring environment     

快速自动程序流分析方法研究

介绍一种自动程序流信息分析方法,使用静态单赋值简化程序切片中的数据依赖关系,利用简单快速程序切片算法删除对循环控制无影响的语句和控制谓词,利用抽象解释自动精确获得程序流信息。实验结果表明,在不失精度的情况下,该方法的分析速度较普通方法快了近25%,且未假定任何程序格式,适用于任何程序格式的流分析过程。     

A flexible algorithm for calculating pair interactions on SIMD architectures

Calculating interactions or correlations between pairs of particles is typically the most time-consuming task in particle simulation or correlation analysis. Straightforward implementations using a double loop over particle pairs have traditionally worked well, especially since compilers usually do a good job of unrolling the inner loop. In order to reach high performance on modern CPU and accelerator architectures, single-instruction multiple-data (SIMD) parallelization has become essential. Avoiding memory bottlenecks is also increasingly important and requires reducing the ratio of memory to arithmetic operations. Moreover, when pairs only interact within a certain cut-off distance, good SIMD utilization can only be achieved by reordering input and output data, which quickly becomes a limiting factor. Here we present an algorithm for SIMD parallelization based on grouping a fixed number of particles, e.g. 2, 4, or 8, into spatial clusters. Calculating all interactions between particles in a pair of such clusters improves data reuse compared to the traditional scheme and results in a more efficient SIMD parallelization. Adjusting the cluster size allows the algorithm to map to SIMD units of various widths. This flexibility not only enables fast and efficient implementation on current CPUs and accelerator architectures like GPUs or Intel MIC, but it also makes the algorithm future-proof. We present the algorithm with an application to molecular dynamics simulations, where we can also make use of the effective buffering the method introduces.