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Advanced Topic 37 of 51 Concurrency & Parallelism

CUDA Programming

NVIDIA's model for general-purpose GPU computing.

How it works

CUDA launches kernels over a grid of thread blocks; threads in a warp (32) execute in lockstep; memory spaces: global, shared, constant, registers; synchronize with __syncthreads.

Where it is used

Deep learning and simulation on NVIDIA GPUs.

Common pitfall

Uncoalesced memory access and warp divergence slow kernels.

Where it fits in the course

CUDA Programming is topic 37 of 51 in Concurrency & Parallelism, rated Advanced. It follows MPI. The next topic is GPU Memory Hierarchy.

Practice and explore

How to study this topic

  1. Read the explanation above and restate the definition in your own words.
  2. Open the interactive lesson and answer its mini quiz.
  3. Read the common pitfall, then write a short example that avoids it.
  4. Check your understanding in the Concurrency & Parallelism quiz, and revisit the topic if you miss a question.

Frequently asked questions

What is CUDA Programming?
NVIDIA's model for general-purpose GPU computing. It belongs to Concurrency & Parallelism and is an advanced-level topic.
Where do people use CUDA Programming?
Deep learning and simulation on NVIDIA GPUs.
What is the most common mistake with CUDA Programming?
Uncoalesced memory access and warp divergence slow kernels.
What should I learn before this topic?
Start with MPI, the topic just before it in Concurrency & Parallelism.
How can I practice it?
Answer the mini quiz in the interactive lesson, then take the Concurrency & Parallelism quiz.
Is CUDA Programming hard to learn?
It is an Advanced topic. Expect to read the explanation more than once and to connect it with earlier topics. Working through a small example by hand helps most.
How long does it take to learn CUDA Programming?
Most learners can read the explanation and finish the interactive lesson in under an hour. Fluency comes from applying the idea, so plan a few short practice sessions with the quiz.
Which topics are related to CUDA Programming?
MPI, GPU Memory Hierarchy, OpenMP are closely connected. The full list is in the related topics section of this page.
What should I study after CUDA Programming?
Continue with GPU Memory Hierarchy, the next topic in Concurrency & Parallelism.

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