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The Complete Parallelism Course: From Zero to Expert!

Name: The Complete Parallelism Course: From Zero to Expert!
Rating: 4.4 (44 reviews)

The modern Parallelism course for everyone! Master Parallelism with projects, challenges and theory

Created byLucas Bazilio

Last updated 7/2025

English

What you'll learn

Become an advanced, confident, and modern parallel computing developer from scratch
Learn OpenMP and MPI from zero
Become job-ready by understanding how Parallelism really works behind the scenes
Parallelism fundamentals: speedup, task dependency graph, overheads, critical path, scalability, etc.
How to think and work like a developer: problem-solving, researching, workflows
Get fast and friendly support in the Q&A area
Complex concepts like the Geometric Data Decomposition, Recursive Structures, etc.
Practice your skills with 20+ challenges and assignments (solutions included)

Course content

13 sections • 66 lectures • 14h 17m total length

Course Introduction2:29
On this lecture we make a brief introduction to the course.

Introduction to Parallelism9:40
On this lesson we make our first contact to the world of Parallelism.
Expressing Tasks7:29
On this lesson we introduce tasks and study an example to express tasks.
Tasks and Dependencies11:13
On this lesson we study dependences between tasks.
Task Dependency Graph9:09
On this lesson we study the Task Dependency Graph and the basic metrics we can compute.
Task Dependency Graph Exercise
Granularity and Parallelism5:53
On this lesson we introduce the concept of granularity in the world of parallelism.
We also study the two main task decompositions: coarse-grain and fine-grain.
Task Definition14:45
On this lesson we study the task definition.
Advanced Granularity29:01
On this lesson, we study in deep the granularity that we can achieve and we explore
different task decompositions with the same algorithm.
Speedup and Efficiency14:46
On this lesson we study the concepts of speedup and efficiency.
We also introduce the scalability.
Amdahl's Law12:44
On this lesson we study the Amdahl's Law.
Overhead Sources10:21
On this lesson we study the overhead sources.
We also learn that Amdahl's Law is only applied when we maintain the problem size fixed.
Common Overheads7:25
On this lesson we study the most common overheads.
We also see a first approach to model the data sharing overhead.

Problem 1 - Question A8:49
On this lesson we solve the point A of the problem 1 of the problems section on parallelism fundamentals.
Problem 1 - Question B8:29
On this lesson we solve the point B of the problem 1 of the problems section on parallelism fundamentals.
Problem 1 - Question C5:57
On this lesson we solve the point C of the problem 1 of the problems section on parallelism fundamentals.
Problem 26:45
On this lesson we solve the problem 2 of the problems section on parallelism fundamentals.
Problem 3 - Question A20:27
On this lesson we solve the point A of the problem 3 of the problems section on parallelism fundamentals.
Problem 3 - Question B6:17
On this lesson we solve the point B of the problem 3 of the problems section on parallelism fundamentals.
Problem 3 - Question C11:31
On this lesson we solve the point C of the problem 3 of the problems section on parallelism fundamentals.
Problem 4 - Question A43:44
On this lesson we solve the point A of the problem 4 of the problems section on parallelism fundamentals.
IMPORTANT: on the code, at the initialization part, the inner loop is for (k = i; k < N; k++)
This is something we update along the lesson, important to have in mind.
Problem 4 - Question B5:58
On this lesson we solve the point B of the problem 4 of the problems section on parallelism fundamentals.
Problem 4 - Question C10:23
On this lesson we solve the point C of the problem 4 of the problems section on parallelism fundamentals.
Problem 5 - Question A12:49
On this lesson we solve the point A of the problem 5 of the problems section on parallelism fundamentals.
Problem 5 - Question B3:26
On this lesson we solve the point B of the problem 5 of the problems section on parallelism fundamentals.
Problem 5 - Question C7:45
On this lesson we solve the point C of the problem 5 of the problems section on parallelism fundamentals.

Task Decomposition Strategies12:33
On this lesson we study the task decomposition strategies in programming.
Recursive Task Decomposition6:10
On this lesson, we deeply study the Recursive Task Decomposition. We distinguish between the tree strategy and the leaf strategy.
Introduction to OpenMP4:26
In this lesson we introduce the OpenMP interface.
Task Generation Control22:00
On this lesson we introduce mechanisms to control number of tasks and/or their granularity.
We begin studying the control we can set in an Iterative Task Decomposition Strategy.
Leaf Strategy11:52
In this lesson we study the Recursive Task Control Generation with OpenMP.
Specifically, we study the Leaf Strategy.
Tree Strategy9:14
In this lesson we study the Tree Strategy in OpenMP.
Depth Recursion Control7:39
On this lesson we study the generation control mechanisms of recursive tasks with Tree Strategy.
We study the Depth Recursion Control using a cut-off mechanism.
Atomic Directive3:59
On this lesson we study the atomic directive in OpenMP.
Critical Directive6:05
On this lesson we study the critical directive in OpenMP.
Reduction Clause3:59
On this lesson we study the reduction clause in OpenMP.
Locks in OpenMP10:39
On this lesson we study Locks in OpenMP.

Taskwait and Taskgroup15:29
On this lesson we study the taskwait and taskgroup clauses in OpenMP to represent Task Ordering.
Task Dependency Clauses6:01
On this lesson we study the task dependency clauses in OpenMP to represent Task Ordering.
Wavefront Example4:11
On this lesson we explain how we can use the task dependency clause of OpenMP to compute
the wavefront. We also learn how to use the clause in a matrix.

Problem 1 - Question A11:54
On this lesson we solve the point A of the problem 1 of the problems section on OpenMP.
Problem 1 - Question B19:03
On this lesson we solve the point B of the problem 1 of the problems section on OpenMP.
Problem 2 - Question A23:24
On this lesson we solve the point A of the problem 2 of the problems section on OpenMP.
Problem 2 - Question B11:13
On this lesson we solve the point B of the problem 2 of the problems section on OpenMP.
Problem 2 - Question C29:55
On this lesson we solve the point C of the problem 2 of the problems section on OpenMP.
Problem 312:47
On this lesson we solve the problem 3 of the problems section on OpenMP.
Problem 417:53
On this lesson we solve the problem 4 of the problems section on OpenMP.
Problem 5 - Question A17:03
On this lesson we solve the point A of the problem 5 of the problems section on OpenMP.
Problem 5 - Question B11:41
On this lesson we solve the point B of the problem 5 of the problems section on OpenMP.
Problem 6 - Question A6:35
On this lesson we solve the point A of the problem 6 of the problems section on OpenMP.
Problem 6 - Question B10:11
On this lesson we solve the point B of the problem 6 of the problems section on OpenMP.

Collective Operations34:28
On this lecture we study collective operations in MPI and implement a practical example.
Gather and Scatter Operations8:43
On this lecture we study the Gather and Scatter Operations in MPI.
Parallel Sum Calculation20:08
On this lecture we solve a problem in which we need to use the functions MPI_Scatter and MPI_Gather.
Introduction to MPI_Allgather2:37
On this lecture we introduce the collective communication function MPI_Allgather.
Parallel Maximum Element Search29:22
On this lecture we solve the Parallel Maximum Element Search problem using the function MPI_Allgather.
MPI Reduce Operations10:07
On this lecture we list the possible operations of MPI_Reduce.

Requirements

No parallelism experience is necessary to take this course! I take you from beginner to expert!
Basic understanding of one programming language

Description

You’ve just stumbled upon the most complete, in-depth Parallelism programming course online.

Whether you want to:

- build the skills you need to get your first computer science programming job

- move to a more senior software developer position

- become a computer scientist mastering in parallel programming

- or just learn Parallelism to be able to create your own parallel applications quickly.

...this complete Parallelism and Concurrency Masterclass is the course you need to do all of this, and more.

This course is designed to give you the Parallelism skills you need to become an expert developer on parallel applications. By the end of the course, you will understand Parallelism extremely well and be able to build your own Parallel apps and be productive as a computer scientist and software developer.

What makes this course a bestseller?

Like you, thousands of others were frustrated and fed up with fragmented Youtube tutorials or incomplete or outdated courses which assume you already know a bunch of stuff, as well as thick, college-like textbooks able to send even the most caffeine-fuelled coder to sleep.

Like you, they were tired of low-quality lessons, poorly explained topics, and confusing info presented in the wrong way. That’s why so many find success in this complete Parallel Programming developer course. It’s designed with simplicity and seamless progression in mind through its content.

This course assumes no previous coding experience and takes you from absolute beginner core concepts. You will learn the core Parallelism skills and master parallalel programming. It's a one-stop shop to learn Parallelism and Concurrency. If you want to go beyond the core content you can do so at any time.

Here’s just some of what you’ll learn

(It’s okay if you don’t understand all this yet, you will in the course)

Designing and Analysis of Parallel Applications
Tasks and Task Dependency Graph
Parallel Architectures
Basic metrics: parallelism, execution time, speedup and scalability
Programming with Shared Memory: OpenMP
Synchronization mechanisms between tasks and threads
Static/Dynamic Work Distribution
Granularity
Task Decomposition vs Data Decomposition
Parallelism Patterns: Iterative vs Divide and Conquer Task Decompositions
Data Decomposition (Geometric Decomposition vs Recursive Structures) for Architectures with Shared Memory

What if I have questions?

As if this course wasn’t complete enough, I offer full support, answering any questions you have 7 days a week.

This means you’ll never find yourself stuck on one lesson for days on end. With my hand-holding guidance, you’ll progress smoothly through this course without any major roadblocks.

There’s no risk either!

This course comes with a full 30-day money-back guarantee. Meaning if you are not completely satisfied with the course or your progress, simply let me know and I’ll refund you 100%, every last penny no questions asked.

You either end up with Parallelism skills, go on to develop great programs and potentially make an awesome career for yourself, or you try the course and simply get all your money back if you don’t like it…

You literally can’t lose.

Ready to get started, developer?

Enroll now using the “Add to Cart” button on the right, and get started on your way to creative, advanced Parallel Programming brilliance. Or, take this course for a free spin using the preview feature, so you know you’re 100% certain this course is for you.

See you on the inside (hurry, OpenMP is waiting!)

Who this course is for:

Take this course if you want to gain a true and deep understanding of Parallelism
Take this course if you have been trying to learn Parallelism but: 1) still don't really understand Parallelism, or 2) still don't feel confident to code real apps
Take this course if you already know Parallelism and are looking for an advanced course. This course includes expert topics!

The Complete Parallelism Course: From Zero to Expert!

What you'll learn

Explore related topics

Course content

Course Overview1 lecture • 2min

Parallelism Fundamentals11 lectures • 2hr 12min

Solved Problems - Fundamentals of Parallelism13 lectures • 2hr 32min

OpenMP - Task Decomposition11 lectures • 1hr 39min

OpenMP - Task Ordering3 lectures • 26min

OpenMP - Solved Problems11 lectures • 2hr 52min

MPI - Introduction2 lectures • 9min

MPI - First Steps1 lecture • 16min

MPI - Point-to-Point Communication2 lectures • 32min

MPI - Collective Communications6 lectures • 1hr 45min

Requirements

Description

Who this course is for: