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.
Parallel Computing with MPI
Gain insight into a topic and learn the fundamentals.
Advanced level
Recommended experience
7 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
Details to know
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Recently updated!
August 2025
Assessments
5 assignments
Taught in English
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There are 5 modules in this course
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 videos2 readings1 assignment1 programming assignment
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
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.
What's included
5 videos1 assignment
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