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Introduction to High-Performance and Parallel Computing

Approx. 18 hours to complete

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Course Summary

Learn the basics of High Performance Computing (HPC) and how it can be used to solve complex problems in various fields. This course covers topics such as parallel computing, message passing, and optimization techniques for HPC applications.

Key Learning Points

Understand the fundamentals of High Performance Computing
Learn how to write and optimize parallel programs using MPI and OpenMP
Gain practical experience with HPC by running programs on a supercomputer

Learning Outcomes

Develop skills in writing and optimizing parallel programs using MPI and OpenMP
Gain practical experience in running programs on a supercomputer
Understand how HPC can be used to solve complex problems in various fields

Prerequisites or good to have knowledge before taking this course

Basic knowledge of programming in C or Fortran
Familiarity with Linux/Unix command line

Course Difficulty Level

Intermediate

Course Format

Online self-paced
Video lectures
Hands-on programming assignments

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Description

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.

Outline

High-Performance Computing (HPC) for Non-Computer Scientists
Course Overview
Tour of JupyterLab
Submitting Assignments
Linux - Part 1
Linux - Part 2
Accessing Remote Systems
Filesystems
Bash Scripting, Part 1
Bash Scripting - Part 2
Course Syllabus
Week 1 Quiz

Nuts and Bolts of HPC
HPC Architecture
Software
Allocations
Node Types
Job Submission with Slurm - Part 1
Job Submission with Slurm - Part 2
Week 2 Quiz

Basic Parallelism
Simple Application Timing
Serial vs. Parallel Processing - Part 1
Serial vs. Parallel Processing - Part 2
Parallel Memory Models
Data vs. Task Parallelism
High Throughput Computing
Week 3 Quiz

Evaluating Parallel Program Performance
How to Parallelize Code
Speedup and Parallel Efficiency
Scalability
Limits to Scaling
Summary of This Course
Week 4 Quiz

Summary of User Reviews

The Introduction to High-Performance Computing course on Coursera has received positive reviews from users. Many found the course to be comprehensive and informative. The overall rating of the course is high, indicating that users have found it to be valuable.

Key Aspect Users Liked About This Course

Comprehensive and informative content

Pros from User Reviews

Well-structured and easy to follow
Great introduction to HPC concepts
Engaging assignments and quizzes
Helpful instructor support
Useful resources and references

Cons from User Reviews

Requires prior knowledge of programming and computer architecture
Some users found the course to be too theoretical
Assignments can be time-consuming
Limited practical application of concepts
Not suitable for beginners with no prior knowledge of HPC

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