When working with Linux systems, understanding which modules are currently loaded into the kernel is important for troubleshooting, optimizing system performance, or customizing hardware support. This tutorial provides 2 methods...

When working with GPUs that supports CUDA, it can be helpful to identify devices uniquely, especially in multi-GPU setups. The UUID (Universally Unique Identifier) of a CUDA device serves as...

The Root Mean Squared Error (RMSE) is a common metric used to measure the difference between two arrays, such as observed and predicted values. It involves summing the squared differences...

The Mean Absolute Deviation (MAD) measures the average absolute difference between each array element and the mean of the array. It is a common statistical tool for analyzing variability. Using...

The floor function rounds each element of an array down to the nearest integer. For instance, applying floor to 4.7 gives 4.0, and to -2.1 gives -3.0. When processing large...

The ceil function rounds each element in an array up to the nearest integer value. For example, applying ceil to 4.3 returns 5, while applying it to -2.7 returns -2...

Multiplying each element of an array by a scalar is a common operation in various applications, from image processing to data normalization. By using SIMD, we can accelerate this operation...

The Manhattan distance is the sum of the absolute differences between corresponding elements in two arrays. SIMD is highly effective in optimizing such operations, enabling us to process multiple array...

Calculating the Euclidean distance between two arrays is a fundamental operation in many applications. The Euclidean distance provides a measure of similarity between two arrays by summing the squared differences...

Multiplying each element of an array by its index is a common operation in data processing, allowing us to scale elements based on their position within the array. A basic...