Array Element-wise Operations Calculator

A GPU-accelerated computation tool that performs complex element-wise mathematical operations on numeric arrays.

Capabilities

Compute combined mathematical transformations

Accepts two numeric arrays of the same shape and computes a combined transformation: for each element pair (a, b), calculate (a² + b²)^0.5 * sin(a) + cos(b). Return the result as an array of the same shape.

• Given arrays [1.0, 2.0, 3.0] and [4.0, 5.0, 6.0], returns an array where each element is computed using the formula above @test
• Given 2D arrays of shape (3, 3) with values [[1, 2, 3], [4, 5, 6], [7, 8, 9]] and [[9, 8, 7], [6, 5, 4], [3, 2, 1]], returns a 2D array with element-wise transformations applied @test

Handle automatic type promotion

When operating on arrays of different numeric types, the function automatically promotes types to preserve precision without requiring explicit type conversion.

• Given an integer array [1, 2, 3] (dtype: int32) and a float array [1.5, 2.5, 3.5] (dtype: float32), returns a result with appropriate floating-point precision @test
• Given arrays with mixed types (uint8 and float64), the computation result uses the more precise type @test

Validate input shapes

Ensure both input arrays have the same shape before processing.

• Given arrays [1, 2, 3] and [4, 5], raises a ValueError indicating shape mismatch @test

Implementation

@generates

API

def compute_transformation(array_a, array_b):
    """
    Compute element-wise transformation on two arrays.

    Formula: (a² + b²)^0.5 * sin(a) + cos(b)

    Args:
        array_a: First numeric array (any numeric dtype)
        array_b: Second numeric array (any numeric dtype, same shape as array_a)

    Returns:
        Array with same shape as inputs containing transformed values

    Raises:
        ValueError: If input arrays have different shapes
    """
    pass

Dependencies { .dependencies }

cupy-cuda101 { .dependency }

Provides GPU-accelerated array operations with NumPy-compatible API.

@satisfied-by