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CUDA Scalar Multiply Kernel

EasyCUDA Execution Model

Problem Statement

Multiply every element of a 1D array by a runtime scalar: out = x * scale.

Background

Scalars are passed as ordinary kernel arguments — no device pointer needed — and used directly inside the kernel.

Your Task

Implement mul_kernel and run(n=1024, scale=3.0) returning whether the output equals x * scale.

How it is tested

Your solution must define a top-level function run(...) that allocates the inputs, copies them to the GPU, launches your @cuda.jit kernel, and returns a Python bool from np.allclose(gpu_result, reference). The grader prints run(...); the expected output is True.

Example:

Input:

n = 1024, scale = 3.0

Output:

True

Reasoning:

The input values are n = 1024 and scale = 3.0, representing the size of the 1D array and the scalar multiplier, respectively.
A 1D array x of size n is created, and its elements are multiplied by the scale factor using the mul_kernel function, resulting in an output array out where each element is calculated as $out_i = x_i \cdot scale$ .
The resulting array out is compared to the reference array, which is also calculated as $x \cdot scale$ , using np.allclose to check for equality within a tolerance.
Since the mul_kernel function correctly multiplies each element of the array by the scale factor, the comparison returns True, indicating that the output array matches the reference array.

Constraints:

Pass scale as a plain kernel argument
out[i] = x[i] * scale
Bounds-check the global index

Editor

Python 3.13.1

GPU · T4

Test Results

0/0

Run code to see test results.