MATLAB Parallel programming on GPUs, Cores and CPUs

MATLAB Parallel programming on GPUs, Cores and CPUs, available at $39.99, has an average rating of 4.35, with 23 lectures, based on 17 reviews, and has 77 subscribers.

You will learn about 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) This course is ideal for individuals who are Students, researchers and engineers It is particularly useful for Students, researchers and engineers.

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Summary

Title: MATLAB Parallel programming on GPUs, Cores and CPUs

Price: $39.99

Average Rating: 4.35

Number of Lectures: 23

Number of Published Lectures: 23

Number of Curriculum Items: 23

Number of Published Curriculum Objects: 23

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

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)

Who Should Attend

Students, researchers and engineers

Target Audiences

Students, researchers and engineers

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:

1. 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.

2. 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.

3. 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.

4. 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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: The concept of cores, CPUs and GPUs (important)

Lecture 2: The difference between the cores and the logical processors

Chapter 2: Multi-threading and Multi-cores with practical examples

Lecture 1: Practical examples on the multi-threading concept

Lecture 2: Practical examples on the multi-workers concept

Lecture 3: How to control the number of cores that execute your code?(practical examples)

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

Lecture 5: Nested parfor loops

Lecture 6: nested-loops: Calculating the amount of data sent to each core

Lecture 7: Functions in parfor loop

Lecture 8: Reduction variables (independence) in parfor loop

Lecture 9: displaying values inside the parfor loop

Lecture 10: Single instruction multiple data (SPMD)

Lecture 11: Run MATLAB functions in background using parfeval

Lecture 12: Run MATLAB files in background using batch jobs

Chapter 3: GPU parallel programming in MATLAB

Lecture 1: Check if your GPU supports parallel computing with MATLAB

Lecture 2: Read the GP information in MATLAB

Lecture 3: Running MATLAB functions on GPUs

Lecture 4: Important Notations to run functions on GPUs (Sharpen an image)

Lecture 5: Running codes on multiple GPUs

Chapter 4: Run Deep learning and Neural network GPUS (parallel)

Lecture 1: Train the neural networks in parallel using GPUS in detail

Lecture 2: Using multi-GPUs for training the neural networks

Lecture 3: Training deep learning model in parallel using GPUs

Lecture 4: Train deep NN in the background in parallel

Instructors

H Soltan
Research Assistant

Rating Distribution

1 stars: 1 votes

2 stars: 0 votes

3 stars: 2 votes

4 stars: 4 votes

5 stars: 10 votes

Frequently Asked Questions

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