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...
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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...
Element-wise subtraction is a common operation in numerical computing, signal processing, and data science, where corresponding elements of two arrays are subtracted and stored in a result array. Modern CPUs...
Normalizing data is a common preprocessing step in many scientific and machine learning applications, and one of the widely used methods for normalization is the L-infinity norm. The L-infinity norm...
Calculating the mean of an array is a common operation that involves summing all elements and dividing by the number of elements. When using SIMD, we can perform this calculation...
When working with large datasets, calculating the sum of array elements can be time-consuming if processed one element at a time. By using SIMD instructions, we can process multiple elements...
When working with large datasets in data analysis, statistics, or scientific computing, calculating metrics like standard deviation can become time-consuming. Standard deviation, which measures how spread out numbers are in...
Counting negative elements in an array is a common task that can be optimized with SIMD operations. Using SIMD allows us to process multiple elements simultaneously, enhancing performance, especially for...