Parallelizing and Tuning the Core
Add threads and autotune for the machine.
Parallelizing and Tuning the Core is a free Mojo 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 Mojo Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
From Fast to Faster
Your kernel already beats Python on one core. Now spread it across all your cores and let Mojo tune it for the machine. ⚡
Why Go Parallel
Modern CPUs have many cores sitting idle. Parallelism lets each core work on part of the data at the same time for a big speedup.
Meet parallelize
Mojo's parallelize runs loop chunks across threads for you. You give it the work and how many pieces to split it into.
from algorithm import parallelizeWrap Work in a Closure
Express one chunk of work as a small function. parallelize calls it once per chunk, each on its own core.
fn work(chunk: Int):
process(chunk)
parallelize[work](num_chunks)Split the Range
Divide the data into roughly equal chunks, one per core. Balanced pieces keep every thread busy instead of one lagging behind.
var per_chunk = n // num_chunksStack SIMD on Threads
Keep your SIMD math inside each chunk. Now you get two wins at once: many cores, each crunching vectors per instruction.
Avoid Data Races
Two threads writing the same spot causes a data race. Give each chunk its own output slice so writes never collide.
Expose Tunable Knobs
Turn tile size and SIMD width into parameters. With knobs in place, you can try many settings without rewriting the kernel.
fn kernel[tile: Int, width: Int]():
passLet Autotuning Search
The best settings differ per machine. Autotuning tries candidate parameter values, times each, and keeps the fastest combination.
Lock In the Winner
Once you know the best tile and width, fix them as the chosen config. The kernel is now specialized for your hardware.
Verify Under Parallelism
Run the tuned, threaded kernel and compare to the baseline. Correctness must still hold once many cores write at once.
Quick Check
Pick the real risk when parallelizing the core.
Recap
You used parallelize over balanced chunks, kept SIMD inside each, avoided data races, then autotuned the knobs. The core is now multi-core fast. 🎯
Frequently asked questions
Is the “Parallelizing and Tuning the Core” lesson free?
Yes — the full text of “Parallelizing and Tuning the Core” 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 “Parallelizing and Tuning the Core”?
Add threads and autotune for the machine. 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 3 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Parallelizing and Tuning the Core” 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
- Profiling the Python Baseline
- Rewriting the Hot Path in Mojo
- Parallelizing and Tuning the Core
- Shipping the Accelerated Project