Race Conditions on the GPU
Why concurrent writes corrupt data.
Race Conditions on the GPU is a free CUDA Academy lesson on CoddyKit — lesson 1 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.
Many Hands, One Counter
On the GPU, thousands of threads can touch the same memory at once. When several write to one spot together, you get a race condition. 🏁
What a Race Really Is
A race condition happens when the final result depends on the unpredictable order in which threads run. Same code, different answer each launch.
Read-Modify-Write
Incrementing a counter is really three steps: read the value, add one, write it back. This read-modify-write sequence is where things break.
counter = counter + 1;How the Steps Interleave
Two threads both read 5, both compute 6, both write 6. You expected 7 but got 6. One increment silently vanished.
The Lost Update
That vanished increment is called a lost update. With thousands of threads racing, dozens or hundreds of updates can disappear at once.
A Kernel That Looks Fine
This kernel looks correct, but every thread races on the same address. The final count will be wrong and will vary between runs.
__global__ void count(int* total) {
*total = *total + 1;
}Why It Is Nondeterministic
The hardware never promises a thread order. So a racy kernel is nondeterministic: it may even pass on small inputs and fail on big ones.
Reads Alone Are Safe
Many threads reading the same value is perfectly fine. Trouble starts only when at least one thread writes while others read or write.
Disjoint Writes Are Safe Too
If each thread writes its own unique slot, like out[i], there is no conflict. A race needs threads aiming at the same location.
out[i] = a[i] + b[i];The Fix Preview
The cure is to make read-modify-write happen as one indivisible step. That is an atomic operation, coming up in the next lesson. ⚛️
Spotting Races in Review
When reviewing a kernel, ask: do two threads write the same address without protection? If yes, you almost certainly have a data race.
Quick Check
Let's make sure the race idea clicked.
Recap: Races on the GPU
You learned that unguarded read-modify-write on shared data causes race conditions and lost updates. Atomics, up next, make those steps indivisible. ✅
Frequently asked questions
Is the “Race Conditions on the GPU” lesson free?
Yes — the full text of “Race Conditions on the GPU” 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 “Race Conditions on the GPU”?
Why concurrent writes corrupt data. 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 1 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Race Conditions on the GPU” 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
- Race Conditions on the GPU
- atomicAdd and Friends
- Building a Histogram
- Custom Atomics with atomicCAS