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Parallel Computing with MPI

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Parallel Computing with MPI

This course is part of High-Performance and Parallel Computing Specialization

Instructors: Shelley Knuth

Included with

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5 modules

Gain insight into a topic and learn the fundamentals.

14 reviews

Advanced level

Recommended experience

8 hours to complete

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

14 reviews

Advanced level

Recommended experience

8 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

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

Skills you'll gain

Tools you'll learn

C++ (Programming Language)

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

5 assignments

Taught in English

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This course is part of the High-Performance and Parallel Computing Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 5 modules in this course

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://www.coursera.org/degrees/master-of-science-data-science-boulder.

Module details

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.

What's included

5 videos3 readings1 assignment1 programming assignment

5 videosTotal 23 minutes

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

3 readingsTotal 21 minutes

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

1 assignmentTotal 15 minutes

Module Quiz15 minutes

1 programming assignmentTotal 60 minutes

Distributed Weather Prediction System Using MPI60 minutes

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.

What's included

5 videos1 assignment1 programming assignment

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.

What's included

4 videos1 assignment1 programming assignment

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.

What's included

7 videos1 assignment1 programming assignment

7 videosTotal 30 minutes

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

1 assignmentTotal 15 minutes

Module Quiz15 minutes

1 programming assignmentTotal 60 minutes

MPI Derived Type for Complex Data Structures60 minutes

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.