Offload heavy work to other CPU cores. Learn how `std::async` and `std::future` enable concurrent execution.

Asynchronous Tasks with `std::async`

Combine the elegance of C++20 Ranges with the power of parallel execution policies.

Parallel Algorithms and Execution Policies

Learn how mutexes and atomics prevent race conditions, and why hardware contention can make multithreaded code slower than single-threaded code.

Locks and Atomics

Explore the performance cost of synchronization, how to mitigate it, and how to avoid it entirely with better algorithm design.

Cache Coherency and False Sharing

Learn how SIMD registers allow you to process multiple data points in a single instruction, unlocking the full power of each CPU core.

SIMD and Automatic Vectorization

TODO Iterators, Ranges, and Views

Modern processors are built for parallel execution, not sequential code. This course shows how to use multithreading and vectorization in C++ to match that reality.

If you're completely new to programming or want a thorough introduction, we recommend starting with our introductory course:

New to Programming?

Introduction to Parallelism

Asynchronous Tasks with std::async

C++20 Ranges

Execution policies

std::transform_reduce

Bryce Adelstein Lelbach: The C++17 Parallel Algorithms Library

Master parallel algorithms in C++. Learn the 'Pipeline Break' pattern to combine lazy Views with multi-threaded Execution Policies, and use std::transform_reduce for high-performance map-reduce operations.

Parallel Algorithms and Execution Policies

Understanding the Basics

Practical Applications

Common Mistakes

Next Steps

Additional Resources

Assessment Preparation

Introduction to Parallelism