cumsum — Compute cumulative sums in MATLAB and RunMat.

cumsum(X) computes running sums along a chosen dimension. The output has the same size as X, with each element storing the cumulative total under MATLAB semantics.

Syntax

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

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

Inputs

Returns

Errors

How cumsum works

• By default, the running total is taken along the first dimension whose length is greater than 1.
• cumsum(X, dim) lets you pick the dimension explicitly; if dim > ndims(X), the input is returned unchanged.
• Passing [] for the dimension argument keeps the default dimension (MATLAB uses this as a placeholder).
• cumsum(..., "reverse") works from the end toward the beginning, whereas "forward" (default) works from start to finish.
• cumsum(..., "omitnan") treats NaN values as missing. Leading NaN values yield zeros until a valid number appears.
• Synonyms such as "omitmissing" / "includemissing" are also accepted for MATLAB compatibility.
• The function supports real or complex scalars and dense tensors. Logical inputs are promoted to double precision.

Does RunMat run cumsum on the GPU?

When a tensor already lives on the GPU, RunMat asks the active acceleration provider for a device-side prefix-sum implementation. The WGPU provider ships a native scan kernel; other providers may still fall back. If no hook is available, RunMat gathers the data to host memory, performs the cumulative sum on the CPU, and returns a dense tensor value. Residency metadata is cleared so later 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, and it keeps 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 totals down each column (default dimension)

A = [1 2 3; 4 5 6];
columnTotals = cumsum(A)

Expected output:

columnTotals =
     1     2     3
     5     7     9

Tracking cumulative sums across rows

A = [1 2 3; 4 5 6];
rowTotals = cumsum(A, 2)

Expected output:

rowTotals =
     1     3     6
     4     9    15

Reversing the direction of accumulation

v = [1 3 5 7];
reverseTotals = cumsum(v, "reverse")

Expected output:

reverseTotals =
    16    15    12     7

Ignoring NaN values while accumulating

v = [2 NaN 5 NaN 1];
running = cumsum(v, "omitnan")

Expected output:

running =
     2     2     7     7     8

Computing a cumulative sum inside a GPU workflow

G = gpuArray(rand(1, 5));
totals = cumsum(G);
hostResult = gather(totals)

Using cumsum with coding agents

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

FAQ

Related Math functions

Open-source implementation

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

• View the source for cumsum 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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