Browsing by Author "Lin, Lan"

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    An Algorithm for Forward Reduction in Sequence-Based Software Specification Read More: http://www.worldscientific.com/doi/abs/10.1142/S0218194016400118
    (World Scientific, 2016-11) Lin, Lan; Xue, Yufeng; Song, Fengguang; Computer and Information Science, School of Science
    Sequence-based software specification is a rigorous method for deriving a formal system model based on informal requirements, through a systematic process called sequence enumeration. Under this process, stimulus (input) sequences are considered in a breadth-first manner, with the expected system response to each sequence given. Not every sequence needs to be further extended by the enumeration rules. The completed specification encodes a Mealy machine and forms a basis for other activities including code development and testing. This paper presents a forward reduction algorithm for sequence-based specification. The need for such an algorithm has been identified by field applications. We used the state machine as an intermediate tool to comprehend and analyze all change impacts resulted from a forward reduction, and used an axiom system for its development. We present the algorithm both mathematically in functional form and procedurally in pseudocode, illustrate it with a symbolic example, and report a larger case study from the published literature in which the algorithm is applied. The algorithm will prove useful and effective in deriving a system-level specification as well as in merging and combining partial work products towards a formal system model in field applications.
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    Designing a Synchronization-reducing Clustering Method on Manycores: Some Issues and Improvements
    (ACM, 2017-11) Zheng, Weijian; Song, Fengguang; Lin, Lan; Computer and Information Science, School of Science
    The k-means clustering method is one of the most widely used techniques in big data analytics. In this paper, we explore the ideas of software blocking, asynchronous local optimizations, and heuristics of simulated annealing to improve the performance of k-means clustering. Like most of the machine learning methods, the performance of k-means clustering relies on two main factors: the computing speed (per iteration), and the convergence rate. A straightforward realization of the software-blocking synchronization-reducing clustering algorithm, however, sees sporadic slower convergence rate than the standard k-means algorithm. To tackle the issues, we design an annealing-enhanced algorithm, which introduces the heuristics of stop conditions and annealing steps to provide as good or better performance than the standard k-means algorithm. This new enhanced k-means clustering algorithm is able to offer the same clustering quality as the standard k-means. Experiments with real-world datasets show that the new parallel implementation is faster than the open source HPC library of Parallel K-Means Data Clustering (e.g., 19% faster on relatively large datasets with 32 CPU cores, and 11% faster on a large dataset with 1,024 CPU cores). Moreover, the extent to which the program performance improves is largely determined by the actual convergence rate of applying the algorithm to different datasets.
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    On A Simpler and Faster Derivation of Single Use Reliability Mean and Variance for Model-Based Statistical Testing
    (KSI Research, 2018-07) Xue, Yufeng; Lin, Lan; Sun, Xin; Song, Fengguang; Computer and Information Science, School of Science
    Markov chain usage-based statistical testing has proved sound and effective in providing audit trails of evidence in certifying software-intensive systems. The system end-toend reliability is derived analytically in closed form, following an arc-based Bayesian model. System reliability is represented by an important statistic called single use reliability, and defined as the probability of a randomly selected use being successful. This paper continues our earlier work on a simpler and faster derivation of the single use reliability mean, and proposes a new derivation of the single use reliability variance by applying a well-known theorem and eliminating the need to compute the second moments of arc failure probabilities. Our new results complete a new analysis that could be shown to be simpler, faster, and more direct while also rendering a more intuitive explanation. Our new theory is illustrated with three simple Markov chain usage models with manual derivations and experimental results.
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    A Simpler and More Direct Derivation of System Reliability Using Markov Chain Usage Models
    (KSI, 2017) Lin, Lan; Xue, Yufeng; Song, Fengguang; Computer and Information Science, School of Science
    Markov chain usage-based statistical testing has been around for more than two decades, and proved sound and effective in providing audit trails of evidence in certifying software-intensive systems. The system end-to-end reliability is derived analytically in closed form, following an arc-based Bayesian model. System reliability is represented by an important statistic called single use reliability, and defined as the probability of a randomly selected use being successful. This paper reviews the analytical derivation of the single use reliability mean, and proposes a simpler, faster, and more direct way to compute the expected value that renders an intuitive explanation. The new derivation is illustrated with two examples.
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    suCAQR: A Simplified Communication-Avoiding QR Factorization Solver Using the TBLAS Framework
    (IEEE, 2016-12) Zheng, Weijian; Song, Fengguang; Lin, Lan; Chen, Zizhong; Computer and Information Science, School of Science
    The scope of this paper is to design and implement a scalable QR factorization solver that can deliver the fastest performance for tall and skinny matrices and square matrices on modern supercomputers. The new solver, named scalable universal communication-avoiding QR factorization (suCAQR), introduces a simplified and tuning-less way to realize the communication-avoiding QR factorization algorithm to support matrices of any shapes. The software design includes a mixed usage of physical and logical data layouts, a simplified method of dynamic-root binary-tree reduction, and a dynamic dataflow implementation. Compared with the existing communication avoiding QR factorization implementations, suCAQR has the benefits of being simpler, more general, and more efficient. By balancing the degree of parallelism and the proportion of faster computational kernels, it is able to achieve scalable performance on clusters of multicore nodes. The software essentially combines the strengths of both synchronization-reducing approach and communication-avoiding approach to achieve high performance. Based on the experimental results using 1,024 CPU cores, suCAQR is faster than DPLASMA by up to 30%, and faster than ScaLAPACK by up to 30 times.