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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/scala2-parallel-programming
课程评论：没有评论
课程名称：并行编程（Scala 2版本） 课程概述：随着每部智能手机和计算机都配备了多个处理器，利用函数式编程思想来促进并行编程的应用变得越来越普遍。在本课程中，您将学习并行编程的基本原理，包括任务并行性和数据并行性。我们将展示许多来自函数式编程的熟悉思想如何与数据并行范式完美对应。课程将从有效并行化常见集合操作的基本知识入手，逐步介绍并行集合，这是Scala标准库中可用的生产级数据并行集合库。整个过程中，我们将通过多个动手示例应用这些概念，分析真实世界的数据，例如流行的算法如k-means聚类。 学习成果：通过本课程，您将能够： - 理解任务和数据并行程序的运行逻辑， - 以函数式风格表达常见算法并实现并行求解， - 熟练进行并行代码的微基准测试， - 编写有效利用并行集合以提升性能的程序。 推荐背景：您至少需有一年编程经验。具备Java或C#的专业技能更为理想，但拥有C/C++、Python、Javascript或Ruby等其他语言的经验也可以。您应对命令行有一定熟悉度。本课程推荐在学习《Scala中的函数程序设计》之后进行。 课程大纲： 第一部分：并行编程 描述：我们动机并行编程并介绍在JVM和Scala上构建并行程序的基本结构。通过数组规范和蒙特卡罗计算等例子来说明这些概念。我们展示如何估算并行程序的工作量和深度，以及如何对实现进行基准测试。 第二部分：基本任务并行算法 描述：继续通过并行算法的例子，我们介绍并行归并排序。接着解释如何并行计算map、reduce和scan等操作。我们提出结合性是实现并行的reduce和scan的关键条件。 第三部分：数据并行性 描述：我们展示数据并行操作如何能够开发优雅的数据并行代码在Scala中。我们提供并行集合层次结构的概述，包括用于支持迭代器和构建器的分割器和合并器的特性。 第四部分：并行计算的数据结构 描述：我们浏览并行计算数据结构的内部，这有助于我们理解并行集合的底层运行机制。
Part: 1
Title:Parallel Programming
Description:We motivate parallel programming and introduce the basic constructs for building parallel programs on JVM and Scala. Examples such as array norm and Monte Carlo computations illustrate these concepts. We show how to estimate work and depth of parallel programs as well as how to benchmark the implementations.
Part: 2
Title:Basic Task Parallel Algorithms
Description:We continue with examples of parallel algorithms by presenting a parallel merge sort. We then explain how operations such as map, reduce, and scan can be computed in parallel. We present associativity as the key condition enabling parallel implementation of reduce and scan.
Part: 3
Title:Data-Parallelism
Description:We show how data parallel operations enable the development of elegant data-parallel code in Scala. We give an overview of the parallel collections hierarchy, including the traits of splitters and combiners that complement iterators and builders from the sequential case.
Part: 4
Title:Data Structures for Parallel Computing
Description:We give a glimpse of the internals of data structures for parallel computing, which helps us understand what is happening under the hood of parallel collections.
With every smartphone and computer now boasting multiple processors, the use of functional ideas to facilitate parallel programming is becoming increasingly widespread. In this course, you'll learn the fundamentals of parallel programming, from task parallelism to data parallelism. In particular, you'll see how many familiar ideas from functional programming map perfectly to to the data parallel paradigm. We'll start the nuts and bolts how to effectively parallelize familiar collections operations, and we'll build up to parallel collections, a production-ready data parallel collections library available in the Scala standard library. Throughout, we'll apply these concepts through several hands-on examples that analyze real-world data, such as popular algorithms like k-means clustering. Learning Outcomes. By the end of this course you will be able to: - reason about task and data parallel programs, - express common algorithms in a functional style and solve them in parallel, - competently microbenchmark parallel code, - write programs that effectively use parallel collections to achieve performance Recommended background: You should have at least one year programming experience. Proficiency with Java or C# is ideal, but experience with other languages such as C/C++, Python, Javascript or Ruby is also sufficient. You should have some familiarity using the command line. This course is intended to be taken after Functional Program Design in Scala: https://www.coursera.org/learn/progfun2.
