How to Write a CUDA Program - The Parallel Programming Edition | NVIDIA GTC 2025 Session

Join one of CUDA's architects on a journey through the concepts of parallel programming: how it works, why it works, why it's not working when you think it should be working, and in particular why it's different on a GPU from a CPU. We'll look at different approaches to parallel programming in CUDA and how to take advantage of the hardware it runs on. This is the next episode in what has become a series looking at the way that CUDA and the GPU work, and why they work this way. This session will focus on how to think about parallel programming on a massively-parallel GPU and why that might be different to what you're used to. If you've never even written a parallel algorithm then you'll learn all sorts of new things, but even if you're a ninja you'll walk away with some new tricks up your sleeve. Speaker: Stephen Jones, CUDA Architect, NVIDIA Key Takeaways: ▫️Introduction to parallel algorithms ▫️Hardware and even software design is running up against the laws of physics, which will change computing in a fundamental way ▫️How parallel programming on a GPU is different to what you might be used to ▫️Techniques for getting the most out of CUDA CUDA Toolkit: https://developer.nvidia.com/cuda-toolkit Watch more NVIDIA GTC sessions on demand: https://www.nvidia.com/en-us/on-demand/?ncid=so-yout-194474-vt33 Topic: CUDA Development and Optimization - Programming Languages / Compilers Level: Technical - Beginner Replay of NVIDIA GTC 2025 Session ID: S72897 #CUDA #NVIDIA #gpucomputing #parallelcomputing #parallelprogramming