MATLAB Parallel programming on GPUs, Cores and CPUs from Udemy

This course helps students, researchers, and anyone using the MATLAB decrease the execution time they take to execute a program

All computers today and the laptops have multi-cores and GPUs. But not all users use the to run or execute the programs in parallel.

The purpose of the course is to fill this gap. Is to teach you with practical examples how to use all resources on your computer and also how to monitor them.

The course is divided into many sections:

The first is an introduction to the hardware of the CPUs, cores, and GPUs. It is better to understand the basic components of these items to be able to get the best utilization when you use them.
The second section is explaining two concepts. The multi-threading and the multi-workers. The first is a built-in mechanism to run some functions in parallel using many cores but we can't control the number of cores and the way that the functions execute. The second one (multi-workers) is used to run any function on multiple cores but here we can control the number of cores to optimize the program execution. Also, I explained some examples and measured the performance parameters to differentiate between the two concepts.
The third section is the GPU section. In the section, I explained how to run any function on the GPUs to make use of the hundred or thousands of cores that the GPUs have. There are some notations to get the best results and I explained all of these notations with examples.
Deep learning and neural networks: in this section, you will learn how to train any neural network in parallel on GPUs or multi-cores. And also how to run the training process in the background in order to be able to use MATLAB while it is running.

What's inside

Learning objectives

Run deep learning models in parallel on gpus
Learn the difference between cores, cpus and gpus
Learn the concept of multi-threading in matlab with examples
Learn the concept of multi-workers in matlab with examples

Measuring the performance of each parallel computing code
Learn how to convert your code to parallel computing to increase the performance
Run matlab files and functions in the background
Using gpus to execute and run matlab functions (excellent performance)

Run deep learning models in parallel on gpus
Learn the difference between cores, cpus and gpus
Learn the concept of multi-threading in matlab with examples
Learn the concept of multi-workers in matlab with examples
Measuring the performance of each parallel computing code
Learn how to convert your code to parallel computing to increase the performance
Run matlab files and functions in the background
Using gpus to execute and run matlab functions (excellent performance)

Syllabus

Introduction

The concept of cores, CPUs and GPUs (important)

The difference between the cores and the logical processors

Multi-threading and Multi-cores with practical examples

Practical examples on the multi-threading concept

Practical examples on the multi-workers concept

How to control the number of cores that execute your code?(practical examples)

4 mistakes when using parfor loop (be carful about them)

Nested parfor loops

nested-loops: Calculating the amount of data sent to each core

Functions in parfor loop

Reduction variables (independence) in parfor loop

displaying values inside the parfor loop

Single instruction multiple data (SPMD)

Run MATLAB functions in background using parfeval

Run MATLAB files in background using batch jobs

GPU parallel programming in MATLAB

Check if your GPU supports parallel computing with MATLAB

Read the GP information in MATLAB

Running MATLAB functions on GPUs

Important Notations to run functions on GPUs (Sharpen an image)

Running codes on multiple GPUs

Run Deep learning and Neural network GPUS (parallel)

Train the neural networks in parallel using GPUS in detail

Using multi-GPUs for training the neural networks

Training deep learning model in parallel using GPUs

Train deep NN in the background in parallel

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops parallel programming skills, which are in high demand in industry

Taught by H Soltan, who are recognized for their expertise in parallel computing

Provides practical examples throughout the course, which helps learners grasp the concepts more easily

Covers a range of topics, from the basics of parallel computing to advanced techniques, which makes it suitable for learners with varying levels of experience

Teaches how to optimize code for parallel execution, which can significantly improve performance

Requires learners to have some programming experience, which may not be suitable for complete beginners

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in MATLAB Parallel programming on GPUs, Cores and CPUs with these activities:

Practice Basic Programming Skills

Show steps

Sharpen programming skills to better grasp parallel computing techniques

Browse courses on Code Challenges

Show steps

Solve coding problems
Participate in online coding challenges
Build small projects

Review Core Computing Concepts

Show steps

Review fundamental computing concepts to strengthen the foundation for parallel computing

Show steps

Go through course materials or textbooks
Solve practice problems
Discuss concepts with peers

Peer Code Review and Optimization

Show steps

Engage in peer code reviews to improve understanding and identify optimization opportunities

Browse courses on Code Optimization

Show steps

Form study groups with peers
Share and exchange code
Review and critique code

Four other activities

Expand to see all activities and additional details

Show all seven activities

Deep Dive into GPU Parallelization Techniques

Show steps

Explore advanced GPU parallelization techniques to enhance understanding of course concepts

Show steps

Identify suitable tutorials from recommended resources
Follow the tutorials to gain hands-on experience
Share learnings with peers or instructors

Participate in Coding Challenges

Show steps

Test and refine concepts through participation in coding challenges

Browse courses on Coding Challenges

Show steps

Identify suitable coding challenges
Attempt to solve challenges
Review solutions and learn from others

Contribute to Open Source Libraries

Show steps

Participate in open source projects to gain practical experience and contribute to the community

Browse courses on Open Source

Show steps

Identify suitable open source projects related to parallel computing
Study the project codebase and identify areas for contribution
Make meaningful contributions to the project

Develop a Parallel Computing Application

Show steps

Create a real-world application to apply and deepen understanding of parallel computing concepts

Browse courses on GPU Programming

Show steps

Identify a problem or opportunity for parallel computing
Design and develop the application
Test and evaluate the performance

Career center

Learners who complete MATLAB Parallel programming on GPUs, Cores and CPUs will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers are responsible for developing and deploying machine learning models. The skills learned in MATLAB Parallel programming on GPUs, Cores and CPUs can help you become a successful Machine Learning Engineer. By learning how to use GPUs, Cores, and CPUs to execute code in parallel, you can improve the performance of your training models and deploy them on a larger scale.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists are responsible for collecting, cleaning, analyzing, and interpreting large datasets. The skills learned in MATLAB Parallel programming on GPUs, Cores and CPUs can help you become a successful Data Scientist. By learning how to use GPUs, Cores, and CPUs to execute code in parallel, you can improve the performance of your programs.

See salaries and explore the career path for Data Scientist

Computer Scientist

Computer Scientists are responsible for researching and developing new computer technologies. The skills learned in MATLAB Parallel programming on GPUs, Cores and CPUs can help you become a successful Computer Scientist. By learning how to use GPUs, Cores, and CPUs to execute code in parallel, you can develop more efficient algorithms and applications.

See salaries and explore the career path for Computer Scientist

Software Engineer

Software Engineers are responsible for designing, developing, and maintaining software applications. The skills learned in MATLAB Parallel programming on GPUs, Cores and CPUs can help you become a successful Software Engineer. By learning how to use GPUs, Cores, and CPUs to execute code in parallel, you can improve the performance of your applications.

See salaries and explore the career path for Software Engineer

Financial Analyst

Financial Analysts are responsible for analyzing financial data and making recommendations to investors. The skills learned in MATLAB Parallel programming on GPUs, Cores and CPUs can help you become a successful Financial Analyst. By learning how to use GPUs, Cores, and CPUs to execute code in parallel, you can improve the performance of your financial models and make better investment decisions.

See salaries and explore the career path for Financial Analyst

Actuary

Actuaries are responsible for assessing and managing financial risk. The skills learned in MATLAB Parallel programming on GPUs, Cores and CPUs can help you become a successful Actuary. By learning how to use GPUs, Cores, and CPUs to execute code in parallel, you can improve the performance of your risk models and make better decisions.

See salaries and explore the career path for Actuary

Operations Research Analyst

Operations Research Analysts are responsible for developing and applying mathematical models to solve business problems. The skills learned in MATLAB Parallel programming on GPUs, Cores and CPUs can help you become a successful Operations Research Analyst. By learning how to use GPUs, Cores, and CPUs to execute code in parallel, you can improve the performance of your optimization models and make better decisions.

See salaries and explore the career path for Operations Research Analyst

Data Analyst

Data Analysts are responsible for collecting, cleaning, analyzing, and interpreting data. The skills learned in MATLAB Parallel programming on GPUs, Cores and CPUs can help you become a successful Data Analyst. By learning how to use GPUs, Cores, and CPUs to execute code in parallel, you can improve the performance of your data analysis tasks and generate insights faster.

See salaries and explore the career path for Data Analyst

Quantitative Analyst

Quantitative Analysts are responsible for using mathematical and statistical models to analyze financial data. The skills learned in MATLAB Parallel programming on GPUs, Cores and CPUs can help you become a successful Quantitative Analyst. By learning how to use GPUs, Cores, and CPUs to execute code in parallel, you can improve the performance of your financial models and make better investment decisions.

See salaries and explore the career path for Quantitative Analyst

Business Analyst

Business Analysts are responsible for analyzing business processes and developing solutions to improve efficiency. The skills learned in MATLAB Parallel programming on GPUs, Cores and CPUs can help you become a successful Business Analyst. By learning how to use GPUs, Cores, and CPUs to execute code in parallel, you can improve the performance of your data analysis tasks and generate insights faster.

See salaries and explore the career path for Business Analyst

Computer Support Specialist

Computer Support Specialists are responsible for providing technical support to computer users. The skills learned in MATLAB Parallel programming on GPUs, Cores and CPUs may be useful for Computer Support Specialists who need to support users who use parallel computing.

See salaries and explore the career path for Computer Support Specialist

Database Administrator

Database Administrators are responsible for managing and maintaining databases. The skills learned in MATLAB Parallel programming on GPUs, Cores and CPUs may be useful for Database Administrators who need to manage databases that are used for parallel computing.

See salaries and explore the career path for Database Administrator

Network Administrator

Network Administrators are responsible for managing and maintaining computer networks. The skills learned in MATLAB Parallel programming on GPUs, Cores and CPUs may be useful for Network Administrators who need to manage networks that are used for parallel computing.

See salaries and explore the career path for Network Administrator

Systems Analyst

Systems Analysts are responsible for analyzing and designing computer systems. The skills learned in MATLAB Parallel programming on GPUs, Cores and CPUs may be useful for Systems Analysts who need to design systems that use parallel computing.

See salaries and explore the career path for Systems Analyst

Project Manager

Project Managers are responsible for planning, executing, and controlling projects. The skills learned in MATLAB Parallel programming on GPUs, Cores and CPUs may be useful for Project Managers who need to manage projects that involve parallel computing.

See salaries and explore the career path for Project Manager