Parallel Computing in Julia

Parallel Computing in Julia, available at $54.99, has an average rating of 4.8, with 35 lectures, based on 5 reviews, and has 1008 subscribers.

You will learn about Students will gain an understanding of fundamental commands in parallel computing in multi-threading and distributed computing paradigms in Julia. Students will be exposed to Julia's tools and packages for parallel programming. Students will explore common and also unusual applications of parallelism. Students will be able to use Multi-threading and Distributed Computing at the same time. This course is ideal for individuals who are Professionals who need to convert their existing code to Julia to take advantage of its parallel programming features and speed. or Julia developers who want to explore parallel paradigms of the language It is particularly useful for Professionals who need to convert their existing code to Julia to take advantage of its parallel programming features and speed. or Julia developers who want to explore parallel paradigms of the language.

Enroll now: Parallel Computing in Julia

Summary

Title: Parallel Computing in Julia

Price: $54.99

Average Rating: 4.8

Number of Lectures: 35

Number of Published Lectures: 35

Number of Curriculum Items: 35

Number of Published Curriculum Objects: 35

Original Price: $34.99

Quality Status: approved

Status: Live

What You Will Learn

Students will gain an understanding of fundamental commands in parallel computing in multi-threading and distributed computing paradigms in Julia.
Students will be exposed to Julia's tools and packages for parallel programming.
Students will explore common and also unusual applications of parallelism.
Students will be able to use Multi-threading and Distributed Computing at the same time.

Who Should Attend

Professionals who need to convert their existing code to Julia to take advantage of its parallel programming features and speed.
Julia developers who want to explore parallel paradigms of the language

Target Audiences

Professionals who need to convert their existing code to Julia to take advantage of its parallel programming features and speed.
Julia developers who want to explore parallel paradigms of the language

Welcome! I was waiting for you.

This course dives headfirst into the world of parallel programming.

We’ll equip you with the skills to tackle problems by harnessing the power of multiple processors. Forget the tired old “pi calculation” examples – we’ll explore practical applications that showcase the true potential of parallel computing.

Leveraging the JuliaHub infrastructure and its rich ecosystem of packages, we’ll guide you through the core concepts with clear, concise explanations and plenty of real-world examples. We’ll also equip you with performance optimization tips to ensure your code runs as efficiently as possible.

On this course, you can expect:

Practical Examples: Dive into hands-on examples that go beyond simple tasks, helping you understand and apply parallel programming concepts effectively.
JuliaHub Infrastructure: Utilize the JuliaHub platform to run and manage your computations seamlessly.
Julia Package Ecosystem: Explore and use a wide range of Julia packages that enhance your parallel programming capabilities.
Performance Tips: Learn valuable tips and techniques to optimize the performance of your parallel programs.
Focused Content: The course is designed to be straight to the point, emphasizing core messages and essential concepts to maximize your learning experience.

So, buckle up and get ready to unlock the power of parallel programming!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: JuliaHub

Chapter 2: Multi-threading

Lecture 1: Welcome to Multi-threading

Lecture 2: Before Getting Started

Lecture 3: @threads

Lecture 4: @spawn

Lecture 5: @sync

Lecture 6: Atomics

Lecture 7: Locks

Lecture 8: Channels

Lecture 9: Channels Application – Producer-Consumer

Lecture 10: Threading going wrong

Lecture 11: Threading going right – prologue

Lecture 12: Threading going right – code

Lecture 13: Threading going right – alternative

Lecture 14: Image Processing Algorithm

Lecture 15: Image Processing with Threads

Chapter 3: Distributed Computing

Lecture 1: Welcome to Distributed Computing

Lecture 2: addprocs local machine

Lecture 3: addprocs remote machine

Lecture 4: @everywhere

Lecture 5: Fine control

Lecture 6: Distributed + Multi-threading part 1

Lecture 7: Distributed + Multi-threading part 2

Lecture 8: SharedArrays

Lecture 9: Parallel k-means part 1

Lecture 10: Parallel k-means part 2

Lecture 11: DistributedArrays part 1

Lecture 12: DistributedArrays part 2

Lecture 13: Digging

Lecture 14: RemoteChannels

Lecture 15: Channels + Workpool

Lecture 16: Pipeline part 1

Lecture 17: Pipeline part 2

Lecture 18: Pipeline part 3

Instructors

Noel Araujo Moreira
College Professor

Rating Distribution

1 stars: 0 votes
2 stars: 0 votes
3 stars: 0 votes
4 stars: 1 votes
5 stars: 4 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

Menu