Splitting Work into Chunks
Divide a range for balanced load.
Splitting Work into Chunks is a free Mojo Academy lesson on CoddyKit — lesson 2 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.
Why Chunk at All
Launching a thread per element is wasteful. Instead you split the range into a few larger chunks, one per worker.
A Chunk Is a Slice
Each chunk is just a contiguous slice of indices, like 0 to 99 or 100 to 199. One worker owns one slice.
Pick a Chunk Count
A common choice is one chunk per core. You ask the machine for that number with num_physical_cores.
var workers = num_physical_cores()Compute the Chunk Size
Divide the total length by the worker count to get how many items each worker should handle.
var chunk = (n + workers - 1) // workersFind a Chunk's Start
Inside the work function, the chunk index tells you where to begin. Multiply it by the chunk size.
var start = c * chunkClamp the End
The last chunk may run past the data, so clamp its end to the total length with min to stay in bounds.
var end = min(start + chunk, n)Loop Inside the Chunk
Each worker runs a normal loop over just its own range. Inside the chunk it is plain, ordinary code.
for i in range(start, end):
out[i] = heavy(i)parallelize Over Chunks
Now you call parallelize with the number of chunks, not the number of elements. Each call processes one whole chunk.
parallelize[do_chunk](workers)Balance the Load
Equal-sized chunks keep every core equally busy. If one chunk is far bigger, that worker finishes late.
More Chunks Than Cores
When some items cost more than others, using more, smaller chunks lets fast workers grab extra work and stay busy.
Tune by Measuring
The best chunk count depends on your data and machine. Try a few sizes and keep the one your benchmark likes.
Quick Check
You split a length-n array into one chunk per worker.
Recap
You divide the range into balanced chunks, clamp each end with min, loop inside a chunk, and call parallelize over the chunk count for even load. 🚀
Frequently asked questions
Is the “Splitting Work into Chunks” lesson free?
Yes — the full text of “Splitting Work into Chunks” 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 “Splitting Work into Chunks”?
Divide a range for balanced load. 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 2 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Splitting Work into Chunks” 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.
All lessons in this course
- The parallelize Function
- Splitting Work into Chunks
- Combining Parallel and Vector
- Avoiding Data Races