University of Colorado Boulder - Introduction to High-Performance and Parallel Computing
- Offered byCoursera
- Public/Government Institute
Introduction to High-Performance and Parallel Computing at Coursera Overview
Duration | 18 hours |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
- Overview
- Highlights
- Course Details
- Curriculum
Introduction to High-Performance and Parallel Computing at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Beginner Level
- Approx. 18 hours to complete
- English Subtitles: English
Introduction to High-Performance and Parallel Computing at Coursera Course details
- 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 be able to...
- *Describe the components of a high-performance distributed computing system
- *Describe the following types of parallel programming models and the situations in which they might be used
- *High-throughput computing
- *Shared memory parallelism
- *Distributed memory parallelism
- *Navigate a typical Linux-based HPC environment
- *Assess and analyze application scalability including weak and strong scaling
- *Quantify 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.
Introduction to High-Performance and Parallel Computing at Coursera Curriculum
Week 1 - 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
Week 2 - 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
Week 3 - 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
Week 4: Evaluating Parallel Program Performance
How to Parallelize Code
Speedup and Parallel Efficiency
Scalability
Limits to Scaling
Summary of This Course
Week 4 Quiz
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