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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/introduction-high-performance-computing
课程评论：没有评论
课程名称：高性能与并行计算导论 课程概述：本课程介绍高性能和并行计算的基本原理，适合科学家、工程师、学者以及任何希望在并行软件环境中发展软件技能的人士。课程内容涵盖大数据分析、机器学习、并行编程和优化等技能。从Linux环境和bash脚本的基础知识，到高吞吐量计算和代码并行化，课程内容全面丰富。 完成本课程后，您将熟悉以下内容： - 高性能分布式计算系统的组成部分 - 不同类型的并行编程模型及其适用场景 - 高吞吐量计算 - 共享内存并行性 - 分布式内存并行性 - 导航典型的基于Linux的高性能计算环境 - 评估和分析应用可扩展性，包括弱扩展和强扩展 - 定量计算项目或工作流的处理、数据和成本需求 该课程可以作为科罗拉多大学博尔德分校数据科学硕士（MS-DS）学位的一部分获得学分。MS-DS是一个跨学科的学位，汇集了应用数学、计算机科学、信息科学等多个部门的教师。该课程采用基于表现的招生方式，无需申请，适合具有广泛计算机科学、信息科学、数学和统计学等本科教育和专业经验的个人。欲了解更多信息，请访问 https://www.coursera.org/degrees/master-of-science-data-science-boulder。 课程大纲： 1. 高性能计算（HPC）基础 - 了解高性能计算系统的基础知识，学习如何使用常见的高性能计算系统，包括导航文件系统、操作Linux操作系统以及一些HPC的基本概念。 2. HPC的基本构件 - 实际使用高性能计算基础设施，学习如何适当地在HPC系统上加载软件，用户可以在系统上遇到的不同类型节点，以及如何提交作业进行计算等。 3. 基本并行性 - 介绍高性能计算系统内存的细微差别，学习在系统上高效工作的方式以及并行编程的一些基本组件。 4. 评估并行程序性能 - 继续回顾高性能计算系统的高效使用主题，包括工作流的扩展性评估，测量在系统上工作的效率，以及如何充分利用计算资源。
Name:High-Performance Computing (HPC) for Non-Computer Scientists
Description:Get to know the basics of an HPC system. Users will learn how to work with common high performance computing systems they may encounter in future efforts. This includes navigating filesystems, working with a typical HPC operating system (Linux), and some of the basic concepts of HPC. We will also provide users some key information that is specific to the logistics of this course.
Name:Nuts and Bolts of HPC
Description:During this week we will actually begin to use HPC infrastructure. Some concepts we will learn are - how to load software appropriately onto an HPC system, what the different types of nodes a user can expect to encounter on a system, and how to submit a job to conduct work, such as perform calculations.
Name:Basic Parallelism
Description:In this module, we will introduce users to the nuances of memory on a high performance computing system. We will also cover some ways to conduct work on a system most efficiently. We will also introduce some beginning components of parallel programming.
Name:Evaluating Parallel Program Performance
Description:In this module, we will continue to review topics related to using a high performance computing system most efficiently, including scaling your workflow measuring how efficient your work on a system is, and how to utilize as much of the computing resource as possible.
This course introduces the fundamentals of high-performance and parallel computing. It is targeted to scientists, engineers, scholars, really everyone seeking to develop the software skills necessary for work in parallel software environments. These skills include big-data analysis, machine learning, parallel programming, and optimization. We will cover the basics of Linux environments and bash scripting all the way to high throughput computing and parallelizing code. After completing this course, you will familiar with: *The components of a high-performance distributed computing system *Types of parallel programming models and the situations in which they might be used *High-throughput computing *Shared memory parallelism *Distributed memory parallelism *Navigating a typical Linux-based HPC environment *Assessing and analyzing application scalability including weak and strong scaling *Quantifying the processing, data, and cost requirements for a computational project or workflow This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
