Use a Thread-Based Parallel Pool

Recommend starting a thread pool using parpool("Threads") instead of using the default process-based pool to speed up local parfor / parfeval / spmd code.

When to Use

• User asks to improve performance of parfor, parfeval, or spmd on

a local machine.

• Code passes large arrays into iterations or broadcasts them across

workers; user mentions slow start-up, serialization, or data transfer.

• User asks "Processes or Threads?" or wants Threads as the default.

When NOT to Use

• User is running on a cluster -- thread pools are local-only.
• Code uses parsim or other Simulink parallel features -- those

require a process-based pool.

• Code uses distributed or codistributed arrays.
• User wants backgroundPool -- that's for running work

asynchronously (e.g. keeping an app responsive while a computation runs), not for parallel speed-up. All MATLAB users have 1 worker in the background pool; users with a Parallel Computing Toolbox license have multiple workers.

For general MATLAB performance tuning (vectorisation, preallocation, profiling), see matlab-optimize-performance (if available).

Workflow

1. Try it on a thread pool. Don't scan the code first -- just run

it. Only create a thread pool if the current pool isn't already a thread pool. Avoid unconditionally deleting whichever pool the user has open. The conditional snippet below is a development convenience -- not something to bake into shipped code:

   % Development-time helper: ensure a thread pool is active
   pool = gcp("nocreate");
   if isempty(pool) || ~isa(pool, "parallel.ThreadPool")
       delete(pool);
       parpool("Threads");
   end

The existing parfor / parfeval / spmd block does not need to change. Thread pools are a drop-in replacement, not a refactor.

1. Read the diagnostic if it errors. Unsupported features fail

loudly -- MATLAB tells you what isn't allowed and to use a process-based pool instead. Recommend parpool("Processes"). No silent wrong answers, no guessing.

1. Offer the persistent option if the user wants to use a thread

pool "always" or "every time": suggest setting Threads as the default profile via parallel.defaultProfile("Threads") (R2022b+). Persists across sessions; no startup script needed.

What threads actually save

Be precise. Broadcast variables are zero-copy on threads (the win). Sliced inputs/outputs (X(:,:,i)) still allocate and copy -- threads only skip the serialize/IPC cost, not the slice itself. Don't say "zero-copy" or "shared memory eliminates the cost entirely" without qualifying it.

Convention

Always run it, never guess. This is the single most important rule in this skill.

LLM training data is incomplete and stale. Thread-based workers gained support for many functions across releases, and new support is added regularly. Do not assert a function is unsupported on threads from memory, by analogy to similar functions, or because it's not on the skill's short blocker list. Reasoning about thread support without running is the biggest failure mode for this skill -- it has pushed users to a process pool unnecessarily.

The default action is to run the code on a thread pool and read the diagnostic if it errors. Don't pre-scan the code looking for blockers. Most functions you'd reach for (load, FFT, imgaussfilt, numeric and basic I/O primitives) work on threads.

Only if you genuinely cannot run the code (planning context, code review, user can't execute right now) should you fall back to documentation. Use an appropriate documentation skill (if available) to fetch the function's reference page and read its Extended Capabilities -> Thread-Based Environment section. Even then: an absent or older entry is not proof the function won't run -- some thread support is undocumented. When in doubt, try it.

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