Jounce/Surge
Surge
Surge is a Swift library for high-performance numerical analysis, linear algebra, digital signal processing, and accelerated image manipulation. It utilizes the Accelerate framework to provide hardware-accelerated tools for matrix mathematics and signal processing.
The library provides specialized capabilities for digital signal processing, including convolution, signal similarity analysis through cross-correlation, and domain transformations using fast Fourier transforms. It also includes a suite of tools for the rapid transformation and analysis of pixel buffers and image data.
Beyond signal and image processing, the library covers a broad range of computational mathematics. This includes linear system solving through matrix inversions and decompositions, vector metric calculations, and statistical data analysis. It also supports bulk execution of trigonometric, logarithmic, and arithmetic operations across large arrays.
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.
