Hunting Races with racecheck
Detecting shared-memory data races.
Hunting Races with racecheck is a free CUDA Academy lesson on CoddyKit — lesson 3 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.
What a Data Race Is
A data race happens when two threads touch the same shared memory and at least one writes, without ordering between them. 😬
Why Races Are Sneaky
Races often give the right answer in testing and the wrong one in production, because the result depends on unpredictable thread timing.
Meet racecheck
The racecheck tool of compute-sanitizer analyzes shared memory accesses and reports hazards that a normal run would hide.
compute-sanitizer --tool racecheck ./reduceA Missing Barrier
Writing to a shared array and then reading a neighbor's slot without a barrier creates a race, since the neighbor may not be written yet.
tile[tid] = in[i];
out[tid] = tile[tid + 1];The Fix: __syncthreads
Insert __syncthreads between the write and the read so every thread in the block finishes writing before anyone reads.
tile[tid] = in[i];
__syncthreads();
out[tid] = tile[tid + 1];Write-After-Write Hazards
racecheck also catches a write-after-write hazard, where two threads store to the same shared slot with no order between them.
Read-After-Write Hazards
The most common hazard is read-after-write: a thread reads a value another thread is still writing, so it reads stale data.
Reading the Hazard Report
racecheck names the hazard type plus the two threads and shared address involved, so you know exactly which barrier is missing.
It Watches Shared Memory
racecheck focuses on __shared__ memory within a block; global races usually need careful logic or atomics instead.
Add Line Info
Just like memcheck, compiling with -lineinfo lets racecheck point at the precise source line of each hazard.
nvcc -lineinfo reduce.cu -o reduceVerify the Fix
Rerun racecheck after adding the barrier. A report of zero hazards means your shared-memory access is now properly ordered.
Quick Check
What is the usual fix when racecheck reports a shared-memory hazard?
Recap
You used racecheck to find shared-memory hazards, read its reports, and fixed them with __syncthreads barriers. Timing bugs, exposed. 🎯
Frequently asked questions
Is the “Hunting Races with racecheck” lesson free?
Yes — the full text of “Hunting Races with racecheck” 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 “Hunting Races with racecheck”?
Detecting shared-memory data races. 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 3 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Hunting Races with racecheck” 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
- Stepping Kernels in cuda-gdb
- Finding Leaks with memcheck
- Hunting Races with racecheck
- Catching Sync Errors with synccheck