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Parallel Computing with MPI

Parallel Computing with MPI

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

位教师：Shelley Knuth

包含在中

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5个模块

深入了解一个主题并学习基础知识。

14 条评论

高级设置等级

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。

14 条评论

高级设置等级

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

Design and implement parallel algorithms using MPI
Apply key communication patterns, including point-to-point, collective, and nonblocking communication
Improve performance through load balancing and overlapping communication with computation
Work with custom communicators and derived data types

您将获得的技能

您将学习的工具

C++ (Programming Language)

要了解的详细信息

可分享的证书

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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。

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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
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该课程共有5个模块

This course is designed for scientists, engineers, students, and professionals looking to develop efficient solutions for high-performance and distributed computing systems. It focuses on parallel programming using the Message Passing Interface (MPI), a standard for scalable communication across multiple processors. Learners should have basic programming experience in C or C++ and familiarity with Linux. No prior knowledge of MPI is required.

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.

单元详情

This module focuses on the key concepts and techniques for transforming serial algorithms into parallel solutions using the Message Passing Interface (MPI). You will explore the principles of message passing, synchronization, and parallel thinking, equipping them with the skills to efficiently utilize parallel computing in their projects.

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23分钟

Course Overview3分钟
Introduction to Parallel Computing6分钟
Thinking in Parallel6分钟
Introduction to Message Passing5分钟
Synchronization3分钟

3篇阅读材料总计21分钟

Course Updates and Accessibility Support1分钟
Earn Academic Credit for your Work!10分钟
Course Support10分钟

1个作业总计15分钟

Module Quiz15分钟

1个编程作业总计60分钟

Distributed Weather Prediction System Using MPI60分钟

This module delves into the advanced communication techniques in MPI, focusing on transforming serial algorithms into parallel implementations. You will learn about nonblocking communication, point-to-point communication, and the intricacies of blocking sends and receives, along with strategies to avoid deadlock in their parallel applications.

涵盖的内容

5个视频1个作业1个编程作业

This module focuses on enhancing the performance of parallel applications using nonblocking communication and effective load-balancing strategies. You will learn how to implement nonblocking communication, overlap communication with computation, and achieve optimal load distribution to maximize speedup in their MPI programs.

涵盖的内容

4个视频1个作业1个编程作业

This module explores advanced parallel computing concepts using MPI, focusing on communicator creation, domain decomposition, and derived datatypes. You will learn to create custom communicators for process coordination and effectively divide computational domains. The module covers MPI's derived datatypes, including contiguous, vector, indexed, and struct types, enabling efficient communication for both regular and irregular data patterns in high-performance applications.

涵盖的内容

7个视频1个作业1个编程作业

7个视频总计30分钟

MPI Communicators4分钟
Communicator Creation Walkthrough4分钟
Communicators and Domain Decomposition3分钟
Derived Datatypes Overview4分钟
Derived Datatypes - Contiguous and Vector6分钟
Derived Datatypes - Indexed and Struct5分钟
Week 4 Wrap-Up3分钟

1个作业总计15分钟

Module Quiz15分钟

1个编程作业总计60分钟

MPI Derived Type for Complex Data Structures60分钟

This module focuses on parallel I/O in MPI, emphasizing efficient data management in high-performance computing. You will learn the principles of MPI I/O and explore practical examples of concurrent data operations. The module also introduces HDF5, a widely used data model and file format in scientific computing, highlighting its features for managing large datasets. By the end, students will be equipped to implement effective parallel I/O strategies using MPI and HDF5 in their applications.