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关键词： Shared-memory parallel paradigm   MPI-OpenMP   hybrid parallelisation   performance evaluation   NEMO ocean model  

摘要： This work describes the introduction of a second level of parallelism based on the OpenMP shared memory paradigm to NEMO, one of the most widely used ocean models in the European climate community. Although the existing parallelisation scheme in NEMO, based on the MPI paradigm, has served it well for many years, it is becoming unsuited to current high-performance computing architectures due to their increasing tendency to have fat nodes containing tens of compute cores. Three different parallel approaches for introducing OpenMP are presented, discussed and compared on several platforms. Finally we have also considered the effect on performance of the data layout employed in NEMO.

关键词： Auralization   Virtual acoustics   Virtual reality   Interaction   Audio rendering   3D sound  

摘要： Virtual environments and their contents are dynamically changing, but also need to respond to the user immediately. While managing a dynamic scene is a common and well-understood problem for visual rendering, additional challenges exist for the high-quality audio rendering of such scenes. Audio rendering differs in a key aspect: Sound waves propagate substantially slower than light. For the acoustics in scenes of large dimensions, it is not sufficient to regard just the state at the current time. The sound propagation times become so significant (perceptible) that the past of the objects matter, making a time history of the scene necessary. Particularly the conjunction of multithreading and low-latency audio processing makes the description of the virtual acoustic scene a problem on its own. This paper presents a novel solution to this acoustic-related problem. We discuss the challenges of realizing a real-time auralization on modern (non-real-time) operating systems and state the main requirements of the data structure. A hierarchical state-based data structure with time history is presented, which not only fulfills the requirements for outdoor auralizations but also has key advantages for indoor simulations-such as room acoustics. A key feature is the integral support of atomic scene modifications, allowing several modifications to be performed at the same time. The presented concept is very modular and beneficial for a wide range of applications.

关键词： Concurrent data structures   Test case diversity   Test case generation   Active testing   Adaptive random testing  

摘要： Context Testing concurrent data structures remains a notoriously challenging task, due to the nondeterminism of multi-threaded tests and the exponential explosion on the number of thread schedules. Objective: We propose an automated approach to generate a series of concurrent test cases in an adaptive manner, i.e., the next test cases are generated with the guarantee to discover the thread schedules that have not yet been activated by the previous test cases. Method Two diversity metrics are presented to induce such adaptive test cases from a static and a dynamic perspective, respectively. The static metric enforces the diversity in the program structures of the test cases;while the dynamic one enforces the diversity in their capabilities of exposing untested thread schedules. We implement three adaptive test generation approaches for C/C + + concurrent data structures, based on the stateof-the-art active testing engine Maple. Results: We then report an empirical study with 9 real-world C/C + + concurrent data structures, which demonstrates the efficiency of our test generation approaches in terms of the number of thread schedules discovered, as well as the time and the number of tests required for testing a concurrent data structure. Conclusion: Hence, by using diverse test cases derived from the static and dynamic perspectives, our adaptive test generation approaches can deliver a more efficient coverage of the thread schedules of the concurrent data structure under test.

关键词： Sort python   linked list python   python data structure   python bubble sort   python search   python binary   python tree   python hash   python arrays  

ISBN: (数字)9781788991933 ISBN: (纸本)9781788995573

摘要： More InformationLearnUnderstand object representation, attribute binding, and data encapsulationGain a solid understanding of Python data structures using algorithmsStudy algorithms using examples with pictorial representationLearn complex algorithms through easy explanation, implementing PythonBuild sophisticated and efficient data applications in PythonUnderstand common programming algorithms used in Python data scienceWrite efficient and robust code in Python 3.7AboutData structures allow you to store and organize data efficiently. They are critical to any problem, provide a complete solution, and act like reusable code. Hands-On Data Structures and Algorithms with Python teaches you the essential Python data structures and the most common algorithms for building easy and maintainable *** book helps you to understand the power of linked lists, double linked lists, and circular linked lists. You will learn to create complex data structures, such as graphs, stacks, and queues. As you make your way through the chapters, you will explore the application of binary searches and binary search trees, along with learning common techniques and structures used in tasks such as preprocessing, modeling, and transforming data. In the concluding chapters, you will get to grips with organizing your code in a manageable, consistent, and extendable way. You will also study how to bubble sort, selection sort, insertion sort, and merge sort algorithms in *** the end of the book, you will have learned how to build components that are easy to understand, debug, and use in different applications. You will get insights into Python implementation of all the important and relevant *** the analysis and design of fundamental Python data structuresExplore advanced Python concepts such as Big O notation and dynamic programmingLearn functional and reactive implementations of traditional data structures

关键词： Unsupervised feature selection   Autoencoder   Manifold learning   Spectral graph analysis   Column sparsity  

摘要： Feature selection is a dimensionality reduction technique that selects a subset of representative features from high-dimensional data by eliminating irrelevant and redundant features. Recently, feature selection combined with sparse learning has attracted significant attention due to its outstanding performance compared with traditional feature selection methods that ignores correlation between features. These works first map data onto a low-dimensional subspace and then select features by posing a sparsity constraint on the transformation matrix. However, they are restricted by design to linear data transformation, a potential drawback given that the underlying correlation structures of data are often non-linear. To leverage a more sophisticated embedding, we propose an autoencoder-based unsupervised feature selection approach that leverages a single-layer autoencoder for a joint framework of feature selection and manifold learning. More specifically, we enforce column sparsity on the weight matrix connecting the input layer and the hidden layer, as in previous work. Additionally, we include spectral graph analysis on the projected data into the learning process to achieve local data geometry preservation from the original data space to the low-dimensional feature space. Extensive experiments are conducted on image, audio, text, and biological data. The promising experimental results validate the superiority of the proposed method. (C) 2018 Elsevier B.V. All rights reserved.

关键词： National Evaluation System for Health Technologies   objective performance goal   peripheral vascular intervention  

摘要： Corresponding Author

关键词： 翻转课堂   微课   GIS数据结构   最短路径算法  

摘要： 探讨了GIS专业的数据结构课程翻转课堂中的微课设计,提出了设计原则、设计要素和设计模式。以"最短路径算法"为例,应用于实践,取得了良好的教学效果。

关键词： 数据结构与算法分析   小组合作   立体化教学  

摘要： 针对数据结构与算法分析课程理论性强、概念抽象、学生学习效果不佳的问题,基于小组合作的同伴教学模式,阐述如何在教学实施过程、问卷调查、非标准化答案考核、多信息渠道补充等方面开展工作,以两个实例说明全方位立体化教学模式中不同教学模式的相互促进作用,并说明教学效果。

ISBN: (数字)9787302501824

摘要： 本书共分为概论、线性表、栈和队列、串、多维数组和广义表、村和二叉树等9章，包括什么是数据结构、为什么要学习数据结构、算法和算法分析等内容。

关键词： 数据结构  

ISBN: (数字)9787302501824 ISBN: (纸本)9787302501824

摘要： 本书是为“数据结构”课程编写的教材，也可以作为学习数据结构及其算法的C语言程序设计的参考书。书中系统地介绍了各种常用的数据结构与算法方面的基本知识。