Diagnose and Fix parfor Variable Classification

Do NOT reason about parfor classification rules from memory. LLM training data contains incomplete and often incorrect explanations of these rules. MATLAB's code analyser (checkcode / codeIssues / mcp__matlab__check_matlab_code) is the only reliable authority — it implements the exact same classifier that the runtime uses. Always run it first; never guess.

When to Use

• ANY code involving parfor where the user reports a problem or asks for help
• User gets a parfor classification error (runtime or in editor)
• User wants to convert a for loop to parfor
• User asks why parfor complains, won't run, or is slow due to classification
• You are about to suggest changes to parfor code — run the analyser first

When NOT to Use

• parfor runs but is slow — performance/pool-choice question, not a classification error
• Worker needs setup/state (paths, environment, toolboxes) — not a parfor variable problem
• Code simply mentions parfor but the user does not mention any problems with it

Workflow

Always follow this order. Never guess at classification rules.

1. Run the code analyser on the file containing the parfor loop using

the mcp__matlab__check_matlab_code MCP tool. Do NOT shell out to MATLAB via Bash — always use the MCP tool. Read the exact error message.

1. Identify the error type from the message (see table below).

1. Apply the matching fix pattern (see Patterns section).

1. Verify by re-running mcp__matlab__check_matlab_code on the fixed

code — confirm zero parfor classification errors.

Error Messages and Root Causes

For the full catalog of all 48 parfor analyser messages (with IDs), see references/parfor-analyser-messages.md. The table below covers the most common classification errors.

Variable Classification Rules (Reference)

parfor classifies each variable into exactly one category:

Critical constraint: A variable must fit exactly ONE category. Mixed usage (e.g. struct fields used as reductions, or same variable sliced with different subscripts) causes classification failure.

Patterns

Fix 1: Struct field as sliced output → plain array

Error: "Unable to classify variable 'out'"

The most common cause of "Unable to classify" with structs. Users write out.field(i) = someFcn() expecting to slice into a struct field, but first-level indexing on out is dot (.field), not parentheses. Sliced variables require first-level () or {} indexing with the loop variable — dot-indexing is never valid for slicing.

% BROKEN: first-level subscript on out is dot, not paren
parfor i = 1:N
    out.x(i) = computeX(i);
    out.y(i) = computeY(i);
end

Fix: Use separate arrays for sliced output, pack into struct after the loop:

xVals = zeros(1, N);
yVals = zeros(1, N);
parfor i = 1:N
    xVals(i) = computeX(i);
    yVals(i) = computeY(i);
end
out.x = xVals;
out.y = yVals;

A related variant: struct fields used as reductions (results.sum = results.sum + val). The same rule applies — extract each field into its own scalar variable, reduce independently, then pack the struct after the loop.

Fix 2: Multiple sliced subscripts → single assignment

Error: "multiple sliced accesses...do not all have the same list of subscripts"

% BROKEN: {i,1}, {i,2}, {i,3} are three different subscript patterns
output = cell(N, 3);
parfor i = 1:N
    [a, b, c] = computeStuff(i);
    output{i,1} = a;
    output{i,2} = b;
    output{i,3} = c;
end

Fix: Single parenthesis-indexed assignment with consistent subscripts:

output = cell(N, 3);
parfor i = 1:N
    [a, b, c] = computeStuff(i);
    output(i,:) = {a, b, c};
end

Note: use () parenthesis indexing (assigns cells), not {} brace indexing when assigning multiple cells.

Fix 3: Invalid sliced subscript patterns

Error: "Variable 'X' is indexed...but it is not a valid sliced output variable"

A sliced variable must be indexed by the loop variable in exactly one dimension, with the remaining subscripts fixed (literals, broadcast variables, or colon). Three common violations:

parfor i = 1:N
    out(i, randi(5)) = val;      % (a) non-deterministic subscript
    out2(i, 1:4, i) = row;      % (b) loop variable in two dimensions
    for j = 1:(N-1)
        out3(i, j) = fcn(i,j);  % (c) nested-for range is not constant
    end
end

Fixes:

(a) Pre-compute non-deterministic indices before the parfor, or accumulate into a local temporary and assign a full row once:

parfor i = 1:N
    localRow = zeros(1, 5);
    localRow(randi(5)) = val;
    out(i,:) = localRow;
end

(b) Remove the duplicate loop-variable dimension — restructure so i indexes only one dimension:

parfor i = 1:N
    out2(i,:) = computeRow(i);  % i in dim 1 only
end

(c) Make the nested-for range a constant or broadcast variable, OR collect into a local variable and assign the sliced output once:

innerN = N - 1;  % broadcast (constant before parfor)
parfor i = 1:N
    row = zeros(1, innerN);
    for j = 1:innerN
        row(j) = fcn(i, j);
    end
    out3(i, 1:innerN) = row;
end

Fix 4: Sliced write combined with full read

Error: "Unable to classify variable 'data'"

A sliced read combined with a broadcast (whole) read is legal — the variable simply "decays" to broadcast in that case. The unclassifiable combination is a sliced write together with a full read of the same variable in the same iteration. Often the whole read is simply attempting to extract size or type information from the sliced output variable.

% BROKEN: data is sliced-write (data(i,j) = ...) AND read whole (size(data,2))
data = buildMatrix(); % 
parfor i = 1:size(data,1)
    for j = 1:size(data,2) % This counts as a "full read" of data
        data(i,j) = myFunc(i,j);
    end
end

Fix: Extract the constant information needed ahead of the loop.

data = buildMatrix();
[N,M] = size(data);
parfor i = 1:N
    for j = 1:M
        data(i,j) = myFunc(i,j);
    end
end

Note that if the "whole read" of a sliced output variable depends on the actual values within the variable, then this fix is not appropriate, and may imply that iterations are not independent.

Fix 5: Reduction not in standard form

Error: "Unable to classify variable 'X'"

A reduction must be exactly: X = X op expr or X = expr op X or X = fcn(X, expr) where X is the entire variable (no indexing) and op is an associative operator +, .* etc.; fcn can be any associative function such as union, max etc.

% BROKEN: indexed reduction
counts = zeros(1, 5);
parfor i = 1:100
    bin = randi(5);
    counts(bin) = counts(bin) + 1;  % indexed reduction - not supported
end

Fix: Accumulate per-iteration, then reduce:

counts = zeros(1, 5);
parfor i = 1:100
    bin = randi(5);
    localCounts = zeros(1, 5);
    localCounts(bin) = 1;
    counts = counts + localCounts;  % whole-variable reduction
end

Fix 6: Temporary variable incorrectly flagged

If a variable is assigned before use in every iteration but checkcode still warns, ensure the first reference in the iteration is a full assignment (not indexed):

% BROKEN: temp is not fully-assigned as first reference
parfor i = 1:10
    temp.a = rand();
    temp.b = 2*rand();
    result(i) = temp.a * temp.b;
end

Fix: Create temp in single assignment

parfor i = 1:N
    temp = struct('a', rand(), 'b', 2*rand()); % Ok - single non-indexed assignment
    result(i) = sum(temp);
end

Fix 7: Conditional update that needs to survive the loop

Error: "The temporary variable 'X' might be used after the PARFOR loop"

Any temporary variable is unavailable after the loop — this is a fundamental parfor rule, not specific to conditional assignment. The "best so far" pattern fails because bestscore = score inside a conditional doesn't match reduction form (X = X op expr), so the classifier cannot treat it as a reduction. It falls through to temporary, and temporaries are discarded when the loop ends:

% BROKEN: bestscore doesn't match reduction form — classified as temporary
bestscore = -Inf;
parfor i = 1:N
    score = expensiveCompute(i);
    if score > bestscore
        bestscore = score;
        X = candidates(i);
    end
end
% bestscore and X are undefined here

Fix: Compute all candidates as sliced output, then reduce after the loop:

scores = zeros(1, N);
allX = cell(1, N);
parfor i = 1:N
    scores(i) = expensiveCompute(i);
    allX{i} = candidates(i);
end
[bestscore, idx] = max(scores);
X = allX{idx};

Conventions

• ALWAYS run the code analyser before diagnosing. Use

mcp__matlab__check_matlab_code (MCP tool), codeIssues (R2022b+), or checkcode. The analyser implements the exact same classifier as the runtime — it is the ground truth. Never diagnose from memory alone.

• ALWAYS verify the fix with the analyser — re-run on the fixed file until there are

no further parfor errors before declaring success. Fixing one problem may reveal other problems.

• Don't run the code — the parallel pool may be slow or unavailable. The

code analyser catches all classification errors statically.

• Preserve semantics — the fix must produce the same results as the

original for loop.

• Minimal changes — fix only the classification issue. Don't restructure

the entire algorithm unless the user asks.

----

Copyright 2026 The MathWorks, Inc.

----