Efficient Programming

Efficient Programming

本课程是 High-Performance and Parallel Computing 专项课程的一部分

位教师：Shelley Knuth

访问权限由 New York State Department of Labor 提供

5个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

8 小时完成

灵活的计划

自行安排学习进度

5个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

8 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Describe the computing and memory architecture of a supercomputing node or cloud computing instance
Utilize compiler and libraries to increase the performance of your program
Understand how to utilize vector operations of a modern microprocessor to maximize performance
Use OpenMP directives to improve vectorization of your programs

您将获得的技能

要了解的详细信息

可分享的证书

添加到您的领英档案

作业

5 项作业

授课语言：英语（English）

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Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 High-Performance and Parallel Computing 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

向行业专家学习新概念
获得对主题或工具的基础理解
通过实践项目培养工作相关技能
获得可共享的职业证书

该课程共有5个模块

This course is targeted to scientists, engineers, scholars, or anyone seeking to solve problems efficiently in high-performance computing environments or in the cloud. Students completing this course will have a basic understanding of how to find bottlenecks in their programs as well as how to address those bottlenecks. The course will provide a high-level introduction to modern compute node architectures of high-performance and cloud computing instances.

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://hua.dididi.sbs/degrees/master-of-science-data-science-boulder.

In this module, we cover an approach to analyze and optimize program performance, such as profiling, using optimized libraries, and compiler options for increasing efficiency.

涵盖的内容

5个视频3篇阅读材料1个作业1个编程作业

5个视频总计39分钟

Course Overview 3分钟
Profiling with gprof 9分钟
Profiling For Python 6分钟
Numerical Libraries 12分钟
Compiler Options for Performance 8分钟

3篇阅读材料总计21分钟

Course Updates and Accessibility Support 1分钟
Earn Academic Credit for your Work! 10分钟
Course Support 10分钟

1个作业总计15分钟

Module Quiz 15分钟

1个编程作业总计45分钟

Matrix Multiplication Profiling 45分钟

In this module, we examine simple techniques that help with program performance. We are looking at scalar and loop optimization methods that can have a large impact on a program’s floating-point performance.

涵盖的内容

5个视频1个作业1个编程作业

In this module, we introduce the basic architecture of modern computers focusing on how the architecture influences program performance. We are looking at processor level data parallelism and how optimized code for parallelism has a much increased floating-point performance.

涵盖的内容

4个视频1个作业1个编程作业

Memory performance is generally the main performance bottleneck since the speed of the main memory has not kept up with the capabilities of processors to process floating-point numbers. We introduce how layers of fast memory, called cache memory, can speed up computations and provide an example of how to optimize algorithms for better memory performance.

涵盖的内容

4个视频1个作业1个编程作业

This module will provide an introduction to parallel and high throughput computing. It will also demonstrate slurm job arrays, where there are mechanisms for working with many similar jobs quickly and easily. Finally, this module will look at running many jobs concurrently with GNU Parallel.