repmat — Tile arrays across one or more dimensions using MATLAB-compatible repmat rules.

repmat(A, reps) tiles A according to replication factors in reps. It follows MATLAB-compatible shape rules and supports numeric, logical, char, string, cell, and GPU-backed array inputs.

Syntax

B = repmat(A, r)
B = repmat(A, m, n)
B = repmat(A, d1, d2, ...)

Inputs

Returns

Errors

How repmat works

• repmat(A, m, n) or repmat(A, [m n]) repeats A m times along rows and n times along columns.
• A size vector [r1 r2 … rN] can be given as a row or column; trailing dimensions default to 1, letting you replicate only the axes you care about.
• Supplying a scalar replication factor (e.g., repmat(A, k)) duplicates A across every dimension, ensuring the first two dimensions are replicated even when the input is a row vector.
• Zero replication factors produce empty dimensions, e.g., repmat(A, 0, 3) yields an empty array with shape [0 size(A,2)].
• Char and cell arrays follow MATLAB by supporting row/column tiling; additional dimensions must be 1, while numeric, logical, complex, and string arrays support full N-D replication.
• Replication factors must be finite, non-negative integers; RunMat raises descriptive errors for negative, fractional, or excessively large sizes.
• GPU arrays remain on the device when the acceleration provider implements the tiling hook; otherwise, RunMat gathers to host memory, tiles in software, and re-uploads the replicated tensor so downstream GPU work keeps residency.

Does RunMat run repmat on the GPU?

RunMat first calls AccelProvider::repmat, giving the backend a chance to tile directly on the device. Providers that have not yet implemented this hook fall back to a safe path that gathers the tensor, performs tiling on the host, and uploads the replicated data back to the GPU. The fallback preserves correct behaviour today while enabling backend authors to drop in optimized kernels as they become available.

GPU memory and residency

You usually do not need to call gpuArray directly. RunMat's planner keeps values on the GPU when it detects that further operations benefit from staying there. Explicit gpuArray calls remain available for compatibility with legacy MATLAB code and when you want to steer residency manually.

Examples

Tiling a matrix across rows and columns

A = [1 2; 3 4];
B = repmat(A, 2, 3)

Expected output:

B =
     1     2     1     2     1     2
     3     4     3     4     3     4
     1     2     1     2     1     2
     3     4     3     4     3     4

Using a scalar replication factor

row = 1:4;
Tiled = repmat(row, 3);
size(Tiled)

Expected output:

ans =
     3    12

Replicating into three dimensions

A = reshape(1:6, [1 3 2]);
T = repmat(A, [2 1 4]);
size(T)

Expected output:

ans =
     2     3     8

Repeating logical masks with zero dimensions

mask = logical([1 0 1]);
emptyMask = repmat(mask, 0, 3);
size(emptyMask)

Expected output:

ans =
     0     3

Replicating string scalars into string arrays

name = "runmat";
names = repmat(name, 2, 2)

Expected output:

names =
  2x2 string array
    "runmat"    "runmat"
    "runmat"    "runmat"

Replicating data that lives on the GPU

G = gpuArray(magic(3));
T = repmat(G, [2 1]);
result = gather(T)

Using repmat with coding agents

Open a RunMat example with live inputs, then ask the agent to explain how repmat changes the result.

FAQ

Related Array functions

Open-source implementation

Unlike proprietary runtimes, every RunMat function is open-source. Read exactly how repmat is executed, line by line, in Rust.

• View the source for repmat in Rust on GitHub
• Learn how the RunMat runtime works
• Found a bug? Open an issue with a minimal reproduction.

About RunMat

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