Automatic test data generation using genetic algorithm and program dependence graphs

The complexity of software systems has been increasing dramatically in the past decade, and software testing as a labor-intensive component is becoming more and more expensive. Testing costs often account for up to 50% of the total expense of software development; hence any techniques leading to the automatic generation of test data will have great potential to considerably reduce costs. Existing approaches of automatic test data generation have achieved some success by using evolutionary computation algorithms, but they are unable to deal with Boolean variables or enumerated types and they need to be improved in many other aspects. This paper presents a new approach utilizing program dependence analysis techniques and genetic algorithms (GAs) to generate test data. A set of experiments using the new approach is reported to show its effectiveness and efficiency based upon established criterion.     

A parallel CFD rotor code using OpenMP

The extended full-potential (FPX) helicopter rotor computational fluid dynamics (CFD) code of Fortran in its reduced two-dimensional version is successfully converted into a parallel version for multiprocessing. The FPX code with an internal grid generator solves the compressible full-potential equation using an approximately factored finite-difference scheme with added numerous physical modeling enhancements, including viscous boundary layers, shock-induced entropy corrections and wake-vortex embedding. The parallel version of the code uses open multi-processing (OpenMP) directives as parallel programming tool in shared-memory (SM) environment. The OpenMP code is portable and scalable, which can run on various computer platforms including UNIX platforms and Windows NT platforms. The performance study of the parallel code on SGI Origin 2000 UNIX platform is made. The results show that reasonable speedups through parallelization are obtained and that OpenMP is easy to use and an efficient parallel programming tool for the present problem.     

Experience with parallel programming using code templates

For almost a decade we have been working at developing and using template-based models for parallel computing. Template-based models separate the specification of the parallel structuring aspects from the application code that is to be parallelized. A user provides the application code and specifies the parallel structure of the application using high-level icons, called templates. The parallel programming system then generates the code necessary for parallelizing the application. The goal here is to provide a mechanism for quick and reliable development of coarse-grain parallel applications that employ frequently occurring parallel structures. Our initial template-based system, FrameWorks, was positively received but had a number of shortcomings. The Enterprise parallel programming environment evolved out of this work. Now, after several years of experience with the system, its shortcomings are becoming evident. Controlled experiments have been conducted to assess the usability of our system in comparison with other systems. The paper outlines our experiences in developing and using these systems. A list of desirable characteristics of template-based models is given. The FrameWorks and Enterprise systems are discussed in the context of these characteristics and the results of our usability experiments. Many of our observations are relevant to other parallel programming systems, even though they may be based on different assumptions. Although template-base models have the potential for simplifying the complexities of parallel programming, they have yet to realize these expectations for high-performance applications. © 1998 John Wiley & Sons, Ltd.     

基于LLVM中间表示的数据依赖并行计算方法

底层虚拟机（LLVM）是一个广泛使用的编译框架,其中间表示（IR）中包含有丰富的程序分析信息,众多以LLVM为平台的相关工作均以IR为基础开展。数据依赖关系在错误检测、定位及程序调试等领域有着重要应用,基于IR的数据依赖关系计算多采用串行迭代方式,但在应对较大规模IR文件时可扩展性不够理想。对此进行了数据依赖关系计算中指令读写的可并行性挖掘,结合图形处理器并行计算优势,提出一种基于LLVM IR的数据依赖关系并行计算方法DRPC。以IR为输入,采用CPU-GPU双端协同方式实现程序数据依赖关系的高效计算。实验结果表明,针对基准程序集SPEC,DRPC分别在直接及传递数据依赖关系计算上最高获得了3.48×和4.91×的加速比。     

Large-scale data visualization using parallel data streaming

We present an architectural approach based on parallel data streaming to enable visualizations on a parallel cluster. Our approach requires less memory than other visualizations while achieving high code reuse. We implemented our architecture within the Visualization Toolkit (VTK). It includes specific additions to support message passing interfaces (MPIs); memory limit-based streaming of both implicit and explicit topologies; translation of streaming requests between topologies; and passing data and pipeline control between shared, distributed, and mixed memory configurations. The architecture directly supports both sort-first and sort-last parallel rendering     

A data dependence test based on the projection of paths over shape graphs

We propose a data dependence detection test based on a new conflict analysis algorithm for C codes which make intensive use of recursive data structures dynamically allocated in the heap. This algorithm requires two pieces of information from the code section under analysis (a loop or a recursive function): (i) abstract shape graphs that represent the state of the heap at the code section; and (ii) path expressions that collect the traversing information for each statement. Our algorithm projects the path expressions on the shape graphs and checks over the graphs to ascertain whether one of the sites reached by a write statement matches one of the sites reached by another statement on a different loop iteration (or on a different call instance in a recursive function), in which case a conflict between the two statements is reported. Although our algorithm presents exponential complexity, we have found that in practice the parameters that dominate the computational cost have very low values, and to the best of our knowledge, all the other related studies involve higher costs. In fact, our experimental results show reductions in the data dependence analysis times of one or two orders of magnitude in some of the studied benchmarks when compared to a previous data dependence algorithm. Thanks to the information on uncovered data dependences, we have manually parallelized these codes, achieving speedups of 2.19 to 3.99 in four cores.     

Safety of code motion

This paper discusses some of the constraints which should be placed on compiler optimization. First, the optimized program should produce the same output as the original program and second, the optimizer should not introduce new error side effects, such as a divide-check interrupt. The latter restriction might be called the safety constraint. Once these constraints are defined, the remainder of the paper is devoted to an algorithm which determines when it is“safe”to move expressions out of loops.     

A parallel fuzzy clustering algorithm for large graphs using Pregel

Large graphs are scale free and ubiquitous having irregular relationships. Clustering is used to find existent similar patterns in graphs and thus help in getting useful insights. In real-world, nodes may belong to more than one cluster thus, it is essential to analyze fuzzy cluster membership of nodes. Traditional centralized fuzzy clustering algorithms incur high communication cost and produce poor quality of clusters when used for large graphs. Thus, scalable solutions are obligatory to handle huge amount of data in less computational time with minimum disk access. In this paper, we proposed a parallel fuzzy clustering algorithm named ‘PGFC’ for handling scalable graph data. It will be advantageous from the viewpoint of expert systems to develop a clustering algorithm that can assure scalability along with better quality of clusters for handling large graphs.The algorithm is parallelized using bulk synchronous parallel (BSP) based Pregel model. The cluster centers are initialized using degree centrality measure, resulting in lesser number of iterations. The performance of PGFC is compared with other state of art clustering algorithms using synthetic graphs and real world networks. The experimental results reveal that the proposed PGFC scales up linearly to handle large graphs and produces better quality of clusters when compared to other graph clustering counterparts.     

Circular motion geometry using minimal data

Circular motion or single axis motion is widely used in computer vision and graphics for 3D model acquisition. This paper describes a new and simple method for recovering the geometry of uncalibrated circular motion from a minimal set of only two points in four images. This problem has been previously solved using nonminimal data either by computing the fundamental matrix and trifocal tensor in three images or by fitting conics to tracked points in five or more images. It is first established that two sets of tracked points in different images under circular motion for two distinct space points are related by a homography. Then, we compute a plane homography from a minimal two points in four images. After that, we show that the unique pair of complex conjugate eigenvectors of this homography are the image of the circular points of the parallel planes of the circular motion. Subsequently, all other motion and structure parameters are computed from this homography in a straighforward manner. The experiments on real image sequences demonstrate the simplicity, accuracy, and robustness of the new method.     

Region-to-region visibility analysis using data parallel machines

We propose an algorithm for solving region-to-region visibility problems on digital terrain models using data parallel machines. Since global communication is the bottleneck in this kind of algorithm, the algorithm we propose focuses on the reduction of global communication. The algorithm analyses a strip of the source region at a time and sweeps through the source strip by strip. At most four sweeps are needed for the analysis. By exploring the coherence properties in the processor structure, global communication is minimized and complexity is substantially improved. Furthermore, all global write operations are exclusive and concurrency in global read operations is minimized. Since the problem size is usually large, we also designed rules of decomposition to efficiently handle the cases where the required number of processors is greater than available. The algorithm has been implemented on a Connection Machine CM-2, and results of computational experiments are presented.     

基于运动图的路径编辑技术

改进了过渡运动的生成算法和路径搜索算法,提出了一种基于运动图的路径编辑的新方法。其中,针对过渡运动的构造,通过最小化融合帧之间的平均帧间距来自动确定用于运动融合的运动片段,并提出了改进动态时间变形(EDTW)算法来解决这一最优化问题;针对运动图上的路径搜索,提出了基于路径曲线所夹面积的目标函数并改进了分段搜索算法和剪枝策略。实验结果表明,该方法能够编辑生成与用户指定路径高度匹配的人物运动。     

Development of a parallel semi-implicit two-dimensional plasma fluid modeling code using finite-volume method

In this paper, the development of a two-dimensional plasma fluid modeling code using the cell-centered finite-volume method and its parallel implementation on distributed memory machines is reported. Simulated discharge currents agree very well with the measured data in a planar dielectric barrier discharge (DBD). Parallel performance of simulating helium DBD solved by the different degrees of overlapping of additive Schwarz method (ASM) preconditioned generalized minimal residual method (GMRES) for different modeling equations is investigated for a small and a large test problem, respectively, employing up to 128 processors. For the large test problem, almost linear speedup can be obtained by using up to 128 processors. Finally, a large-scale realistic two-dimensional DBD problem is employed to demonstrate the capability of the developed fluid modeling code for simulating the low-temperature plasma with complex chemical reactions.     

一种基于流图变换的代码迷惑算法

为了提高软件的安全性,常使攻击者难以理解专利软件系统内部的工作机制,代码迷惑技术因其代价低廉而越来越受到人们的重视。代码迷惑技术的提出对于软件保护具有非常重要的意义,代码迷惑技术的使用可以对程序代码及核心算法进行保护。简要概述了代码迷惑技术基本内容,阐述了基本块和流图的相关知识,给出了可归约流图变换为不可归约流图的迷惑变换具体的算法及实验结果,并对算法的有效性进行了分析。     

分布式数据库数据副本最优安置问题的研究

对树形网络上的数据副本最优安置问题,在已有K子树中心优化模型的基础上提出了K节点中心的改进模型.改进模型相对于原有模型优化了分布式数据库更新操作的执行代价.给出了两个动态规划算法来求解树形网络K节点中心问题,一个是非常简单的复杂度较高的动态规划,另一个是使用分治的较复杂的高效动态规划,最后通过实验验证了模型的优化作用.     

四元数样条插值的人体运动数据重构

为了得到平滑的人体动画,提出一种基于四元数的样条插值算法,利用提取的关键帧实现人体运动序列的有效重构。为减少重构误差、加快收敛速度,将已知关键帧集合作为初始条件,通过迭代算法求出样条曲线的控制点集合。利用样条曲线控制点计算贝塞尔曲线控制点,构造贝塞尔样条曲线段,将各段贝塞尔样条曲线段组合,构造一条基于四元数的样条曲线。根据德卡斯特里奥（de Casteljau）算法插值重构人体运动。实验结果表明,该算法在保证执行效率的同时,可得到光滑的插值结果,实现满足视觉要求的人体运动重构。     

Group graphs and computational symmetry on massively parallel architecture

Efficient space and time exploitation of symmetry in domains on highly parallel, distributed-memory architecture is, in certain cases, equivalent to routing along a labeled group action graph, with computation associated with each group element label, where the group of symmetries acts on the processors. The algebraic structure of the group can sometimes be analyzed to determine, a priori, space and time efficient routing schedules on the hardware network (which, in practice, is often another group action graph). The algorithms we develop were implemented on a 64K-processor CM-2 and used to solve certain natural classes of chess endgames, part of whose search space is invariant under a noncommutative crystallographic group. This program runs 400 times faster than any previous implementation, and discovered many interesting new results in the area; some of these results are not solvable in practice with current serial techniques because the time and space requirements are too large. It seems interesting that it was possible, albeit with difficulty, to implement efficiently certain irregular chess rules on the CM-2, which is optimized for regular data sets.An earlier version of this paper was presented at Supercomputing '90.Partially supported by NSF/DARPA Grant CCR-8908092.     

Performance analysis of parallel programs based on directed acyclic graphs

Task precedence graphs are widely used for modeling and evaluation of parallel applications. Their nodes represent the subtasks of the parallel program and the edges represent the precedence relations between the subtasks. The execution times of the subtasks are described by random variables and their distributions. In our paper we introduce a new class of distributions, particularly suited for the modeling and evaluation of parallel programs. Exponential polynomials introduced by Sahner and Trivedi have the disadvantage that a large number of parameters is needed for the representation of realistic task execution times, which usually have a small value of variation. We extend this class to derive the class of truncated -exponential polynomials which allow the representation of realistic task execution times with fewer parameters. Additionally this class of distributions has the advantage that minimum as well as maximum execution times can be guaranteed. Models with a large number of subtasks can not be evaluated on a computer using exact analytical methods because of memory requirements and numerical inaccuracies, which accumulate, when the operations of analysis are applied. Using extreme value theory we derive approximate formulas for the parallel independent execution of subtasks, a structure, which can be found in every parallel program. The obtained results for truncated and not truncated distributions show, that distributions with an infinite domain are not suitable, particularly for massively parallel structures. Received: 26 August 1994 / 13 May 1996