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Your kernel already beats Python on one core. Now spread it across all your cores and let Mojo tune it for the machine. ⚡

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.

Mojo's parallelize runs loop chunks across threads for you. You give it the work and how many pieces to split it into.

Express one chunk of work as a small function. parallelize calls it once per chunk, each on its own core.

Divide the data into roughly equal chunks, one per core. Balanced pieces keep every thread busy instead of one lagging behind.

Keep your SIMD math inside each chunk. Now you get two wins at once: many cores, each crunching vectors per instruction.

Two threads writing the same spot causes a data race. Give each chunk its own output slice so writes never collide.

Turn tile size and SIMD width into parameters. With knobs in place, you can try many settings without rewriting the kernel.

The best settings differ per machine. Autotuning tries candidate parameter values, times each, and keeps the fastest combination.

Once you know the best tile and width, fix them as the chosen config. The kernel is now specialized for your hardware.

Run the tuned, threaded kernel and compare to the baseline. Correctness must still hold once many cores write at once.

Pick the real risk when parallelizing the core.

You used parallelize over balanced chunks, kept SIMD inside each, avoided data races, then autotuned the knobs. The core is now multi-core fast. 🎯