Avoiding Data Races
Keep parallel writes safe.
Avoiding Data Races is a free Mojo 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 Mojo Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
What Is a Data Race
A data race happens when two threads touch the same memory at once and at least one writes. The result becomes unpredictable. ⚠️
The Classic Trap
Many workers adding into one shared total will clash. Updates get lost because writes overlap and stomp each other.
total += out[i] # shared, unsafe in parallelWrite Disjoint Slots
The simplest fix: give each worker its own indices to write. If outputs never overlap, there is no race.
out[i] = compute(i) # each i is uniqueReads Can Share
Many threads may read the same input safely. Trouble starts only when someone writes what others read or write.
Per-Worker Partials
For a sum, give each worker its own partial slot. They never share a cell, so the adds stay safe.
partials[c] += out[i] # one slot per workerReduce After Joining
Once all workers finish, add the partials together in a single thread. This final reduction needs no locking.
for c in range(workers):
total += partials[c]Avoid Shared Mutable State
The safest parallel code shares only read-only data. Keep every mutable piece private to one worker when you can.
Order Is Not Guaranteed
Workers may finish in any order. Never rely on one chunk running before another, since the schedule can vary.
Mojo Ownership Helps
Mojo's ownership and value semantics make accidental sharing harder, but parallel logic is still yours to keep correct.
Test With Many Runs
Races hide behind timing, so run the parallel code many times and compare against a serial reference result.
Design It Out
The best defense is a layout where writes never overlap. Prevent races by design, not by patching afterward.
Quick Check
Several workers need to compute a single sum in parallel.
Recap
Avoid races by writing disjoint slots, using per-worker partials, reducing after joining, and never relying on order, so parallel results stay correct. 🚀
Frequently asked questions
Is the “Avoiding Data Races” lesson free?
Yes — the full text of “Avoiding Data Races” is free to read here on the web, and the Mojo 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 Mojo Academy course, upgrade to CoddyKit PRO.
What will I learn in “Avoiding Data Races”?
Keep parallel writes safe. You practise Mojo 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 Mojo Academy?
No prior experience is required. Mojo 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 “Avoiding Data Races” 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 Mojo Academy lesson?
Yes. Every Mojo 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.