cumprod — Compute cumulative products in MATLAB and RunMat.

cumprod(X) multiplies elements cumulatively along a chosen dimension. The output has the same size as X, and each element stores the running product under MATLAB semantics.

Syntax

B = cumprod(A)
B = cumprod(A, dim)
B = cumprod(A, direction)
B = cumprod(A, nanflag)
B = cumprod(A, dim, direction)
B = cumprod(A, direction, dim)

B = cumprod(A)
B = cumprod(A, dim)
B = cumprod(A, direction)
B = cumprod(A, nanflag)
B = cumprod(A, dim, direction)
B = cumprod(A, direction, dim)
B = cumprod(A, dim, nanflag)
B = cumprod(A, nanflag, dim)
B = cumprod(A, direction, nanflag)
B = cumprod(A, nanflag, direction)
B = cumprod(A, dim, direction, nanflag)
B = cumprod(A, dim, nanflag, direction)
B = cumprod(A, direction, dim, nanflag)
B = cumprod(A, direction, nanflag, dim)
B = cumprod(A, nanflag, dim, direction)
B = cumprod(A, nanflag, direction, dim)

Inputs

Returns

Errors

How cumprod works

• By default, the running product is taken along the first dimension whose length is greater than 1.
• cumprod(X, dim) lets you choose the dimension explicitly; if dim > ndims(X), the input is returned unchanged.
• Passing [] for the dimension argument keeps the default dimension (MATLAB treats it as a placeholder).
• cumprod(..., "reverse") accumulates from the end toward the beginning; "forward" (default) works from start to finish.
• cumprod(..., "omitnan") treats NaN values as missing. Empty prefixes yield 1, the multiplicative identity.
• Synonyms such as "omitmissing" / "includemissing" are accepted for MATLAB compatibility.
• The function supports real or complex scalars and dense tensors. Logical inputs are promoted to double precision.

Does RunMat run cumprod on the GPU?

When data already lives on the GPU, RunMat asks the active acceleration provider for a device-side prefix-product implementation. The runtime calls the cumprod_scan hook with the chosen dimension, direction, and NaN mode. Providers that lack this hook—or that report an error for the requested options—trigger a gather to host memory, perform the cumulative product on the CPU, and return the dense tensor result. Residency metadata is cleared so downstream operations can re-promote the tensor when profitable.

GPU memory and residency

Manual gpuArray calls are optional. RunMat promotes tensors automatically when the planner predicts a benefit, keeping fused expressions resident on the device. Explicit gpuArray is still supported for MATLAB compatibility or when you want to guarantee GPU residency before entering a critical loop.

Examples

Running products down each column (default dimension)

A = [1 2 3; 4 5 6];
columnProducts = cumprod(A)

Expected output:

columnProducts =
     1     2     3
     4    10    18

Tracking cumulative products across rows

A = [1 2 3; 4 5 6];
rowProducts = cumprod(A, 2)

Expected output:

rowProducts =
     1     2     6
     4    20   120

Reversing the accumulation direction

v = [2 3 4 5];
reverseProducts = cumprod(v, "reverse")

Expected output:

reverseProducts =
   120    60    20     5

Ignoring NaN values while multiplying

v = [2 NaN 4 NaN 3];
running = cumprod(v, "omitnan")

Expected output:

running =
     2     2     8     8    24

Computing a cumulative product inside a GPU workflow

G = gpuArray(1 + 0.1*rand(1, 5));
totals = cumprod(G);
hostResult = gather(totals)

Using cumprod with coding agents

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

FAQ

Related Math functions

Open-source implementation

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

• View the source for cumprod 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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