Replaying Graphs to Cut Overhead
Amortizing launch cost across iterations.
Replaying Graphs to Cut Overhead is a free CUDA Academy lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the CUDA Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
The Whole Point: Replay
Graphs exist to be replayed. One launch call submits the entire recorded sequence to the GPU at once.
Launch the Exec Object
You replay with cudaGraphLaunch, passing the instantiated exec and a stream. That is the whole submission.
cudaGraphLaunch(exec, stream);One Call, Many Kernels
Instead of ten launches per step, you pay the CPU launch cost just once for the entire graph.
Loop and Repeat
In an iterative solver you call launch every step. The savings amortize across thousands of identical iterations.
for (int i = 0; i < steps; i++)
cudaGraphLaunch(exec, stream);Instantiate Cost Is Paid Once
The expensive instantiate step happens before the loop, so each replay inside the loop stays cheap.
Updating Without Rebuilding
If only parameters change, use cudaGraphExecUpdate to patch the exec instead of rebuilding from scratch.
cudaGraphExecUpdate(exec, newGraph, NULL, &res);Where the Speedup Shows
The gain is largest with short kernels: when GPU work is brief, launch overhead dominated, and graphs erase it.
Less Jitter, More Overlap
Submitting work as a batch also reduces CPU jitter, letting the GPU stay busy with fewer gaps on the timeline.
Mind the Tradeoffs
Graphs assume a stable structure. If the work pattern changes every step, the rebuild cost can outweigh the savings.
Free the Resources
When finished, release both objects with cudaGraphExecDestroy and cudaGraphDestroy to avoid leaks.
cudaGraphExecDestroy(exec);
cudaGraphDestroy(graph);Capture, Instantiate, Replay
The full lifecycle is three beats: capture the work, instantiate it once, then replay it many times.
Quick Check
Why do graphs cut launch overhead?
Recap: Replaying Graphs
Replay with cudaGraphLaunch to fire a whole sequence in one call, amortizing setup across iterations. Update in place and free when done. Well done! 🏁
Frequently asked questions
Is the “Replaying Graphs to Cut Overhead” lesson free?
Yes — the full text of “Replaying Graphs to Cut Overhead” is free to read here on the web, and the CUDA Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the CUDA Academy course, upgrade to CoddyKit PRO.
What will I learn in “Replaying Graphs to Cut Overhead”?
Amortizing launch cost across iterations. You practise CUDA Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start CUDA Academy?
No prior experience is required. CUDA Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Replaying Graphs to Cut Overhead” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this CUDA Academy lesson?
Yes. Every CUDA Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Launching Kernels from a Kernel
- When Dynamic Parallelism Pays
- Capturing Work into a Graph
- Replaying Graphs to Cut Overhead