MATLAB Parallel programming on GPUs, Cores and CPUs
Learn parallel programming for faster AI & deep learning! Practical examples for training neural networks and models. Boost your performance.
Course Content
MATLAB Parallel programming on GPUs, Cores and CPUs is an all-levels Udemy course taught by Hamdy egy. It runs 3h 22m, is taught in English, and includes a certificate of completion. Learners rate it 4.2 out of 5 from 23 reviews, with 97 students enrolled. It is a paid course, first published January 2022.
Unlock the Power of Parallel Computing in MATLAB
Are your MATLAB programs taking too long to run? This course is designed for students, researchers, and professionals who want to speed up MATLAB execution by leveraging the full potential of modern hardware. Today's computers, including laptops, are equipped with multi-core CPUs and GPUs, but many users aren't utilizing these resources for parallel processing. This course bridges that gap, providing practical examples to help you harness all available computing power and effectively monitor system resources.
Here's what you'll learn:
CPU & GPU Fundamentals: Gain a solid understanding of CPU architecture, cores, and GPUs. Learn the essential components to optimize your code for maximum performance. Understand how these hardware elements impact MATLAB performance.
Multi-threading vs. Multi-workers: Explore two powerful parallel processing techniques in MATLAB.
- Multi-threading: Discover MATLAB's built-in mechanism for parallel execution using multiple cores. Understand its limitations in terms of core control.
- Multi-workers: Master the
matlab.parallel.workerspackage to precisely control the number of cores used, allowing for optimized program execution. We'll compare performance using practical examples and performance measurements.
GPU Acceleration: Unlock the immense power of GPU computing in MATLAB. Learn how to execute functions on GPUs to utilize their thousands of cores. We'll cover key notations and best practices for achieving optimal results with GPU programming in MATLAB.
Parallel Deep Learning: Accelerate your deep learning and neural network training processes. Learn how to train models in parallel on GPUs or multi-cores. Also, discover how to run training in the background, freeing you to continue working in MATLAB while training progresses. This is crucial for efficient machine learning workflows.
This course provides hands-on experience and practical guidance to significantly reduce MATLAB execution time and improve overall computational efficiency. Learn to effectively utilize your computer's resources for faster results!
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