Jounce/Surge

Features

• Accelerate-Based Math Libraries - Ships a Swift library for high-performance matrix math, signal processing, and image manipulation using the Accelerate framework.
• Linear Algebra - Provides high-performance linear algebra routines and numerical computations using hardware-accelerated SIMD instructions.
• High-Performance Image - Provides tools for manipulating large image buffers using high-performance, parallel processing pipelines.
• Digital Signal Processing Libraries - Provides a comprehensive library of algorithms for digital signal processing tasks including filtering and domain transformation.
• Vectorized Math Functions - Implements hardware-accelerated trigonometric, logarithmic, and arithmetic functions across large arrays of data.
• High-Performance and Parallel Computing - Provides a framework for executing computationally intensive matrix and linear algebra tasks across parallel resources.
• Linear Algebra Libraries - Provides a system for performing matrix and vector operations and solving complex linear equation systems.
• Linear System Solvers - Implements matrix inversions, transpositions, determinants, and eigen decompositions to solve complex linear systems.
• Matrix Arithmetic Operations - Executes fundamental addition, subtraction, multiplication, and division across scalars, vectors, and matrices.
• Numerical Computation Accelerations - Provides numerical computation accelerations by offloading mathematical operations to hardware-accelerated frameworks using SIMD instructions.
• Numerical Libraries - Provides optimized algorithms for calculating statistical data and trigonometric or logarithmic values across arrays.
• SIMD Parallel Computing - Executes mathematical operations using Single Instruction Multiple Data (SIMD) to accelerate signal and image processing.
• Direct Memory Buffers - Manipulates raw pixel and signal data through direct memory buffers to minimize overhead during transformations.
• Signal Similarity Calculation - Computes cross-correlation and matching scores between signals to determine similarity.
• Hardware-Accelerated Image Processing - Performs high-speed pixel level calculations and buffer transformations for editing or analyzing visual content.
• Time-Frequency Mapping - Converts signals between time and frequency representations for efficient spectral analysis.
• Signal Convolutions - Combines two separate signals to produce a third signal representing the effect of one upon the other.
• Fourier Transforms - Implements methods for converting signals between time and frequency domains using fast Fourier transforms.
• Array Operations - Provides hardware-accelerated calculation of base-e, base-2, and base-10 logarithms and exponentials across large arrays.
• Mathematical Computing Libraries - Provides standard library functions for high-performance numerical calculations and bulk arithmetic operations.
• Array Operations - Implements ceiling, flooring, rounding, truncation, and absolute value operations across arrays of numbers.
• Statistical Analysis Libraries - Calculates descriptive statistics including mean, variance, standard deviation, minimum, and maximum values within datasets.
• Statistical Analysis Libraries - Provides specialized functions for calculating statistical metrics and performing vector operations on large datasets.
• Trigonometric Functions - Provides direct and inverse trigonometric operations for scientific calculation across sets of values.
• Vector Distance Metrics - Calculates dot products, Euclidean distances, squared distances, and summations for high-dimensional vectors.

Star-Verlauf