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Surge

Surge is a Swift library that uses the Accelerate framework to provide high-performance functions for matrix math, digital signal processing, and image manipulation.

Accelerate exposes SIMD instructions available in modern CPUs to significantly improve performance of certain calculations. Because of its relative obscurity and inconvenient APIs, Accelerate is not commonly used by developers, which is a shame, since many applications could benefit from these performance optimizations.

Surge aims to bring Accelerate to the mainstream, making it as easy (and nearly as fast, in most cases) to perform computation over a set of numbers as for a single member.

Though, keep in mind: Accelerate is not a silver bullet. Under certain conditions, such as performing simple calculations over a small data set, Accelerate can be out-performed by conventional algorithms. Always benchmark to determine the performance characteristics of each potential approach.

Curious about the name Surge? Back in the mid 90's, Apple, IBM, and Motorola teamed up to create AltiVec (a.k.a the Velocity Engine), which provided a SIMD instruction set for the PowerPC architecture. When Apple made the switch to Intel CPUs, AltiVec was ported to the x86 architecture and rechristened Accelerate. The derivative of Accelerate (and second derivative of Velocity) is known as either jerk, jolt, surge, or lurch, hence the name of this library.

Installation

The infrastructure and best practices for distributing Swift libraries are currently in flux during this beta period of Swift & Xcode. In the meantime, you can add Surge as a git submodule, drag the Surge.xcodeproj file into your Xcode project, and add Surge.framework as a dependency for your target.

Surge uses Swift 5. This means that your code has to be written in Swift 5 due to current binary compatibility limitations.

License

Surge is available under the MIT license. See the LICENSE file for more info.

Swift Package Manager

To use Swift Package Manager add Surge to your Package.swift file:

let package = Package(
    name: "myproject",
    dependencies: [
        .package(url: "https://github.com/mattt/Surge.git", .upToNextMajor(from: "2.0.0")),
    ],
    targets: [
        .target(
            name: "myproject",
            dependencies: ["Surge"]),
    ]
)

Then run swift build.

CocoaPods

To use CocoaPods add Surge to your Podfile:

source 'https://github.com/CocoaPods/Specs.git'
platform :ios, '10.0'
use_frameworks!

target '<Your Target Name>' do
    pod 'Surge', '~> 2.0.0'
end

Then run pod install.

Carthage

To use Carthage add Surge to your Cartfile:

github "mattt/Surge" ~> 2.0.0

Then run carthage update and use the framework in Carthage/Build/<platform>.

Usage

Computing Sum of [Double]

import Surge

let n = [1.0, 2.0, 3.0, 4.0, 5.0]
let sum = Surge.sum(n) // 15.0

Computing Product of Two [Double]s

import Surge

let a = [1.0, 3.0, 5.0, 7.0]
let b = [2.0, 4.0, 6.0, 8.0]

let product = Surge.mul(a, b) // [2.0, 12.0, 30.0, 56.0]

Inventory

• General Arithmetic Operations
  • Addition
  • Subtraction
  • Multiplication
  • Element-wise multiplication
  • Division
  • Element-wise Division
  • Modulo
  • Remainder
  • Square Root
  • Summation
  • Dot Product
  • Distance
  • Squared Distance
  • Power
  • Exponential
• Trigonometric Operations
  • Sine/Cosine/Tangent
  • Arc Sine/Cosine/Tangent
  • Hyperbolic Sine/Cosine/Tangent
  • Inverse Hyperbolic Sine/Cosine/Tangent
  • Radians ↔︎ Degrees
• Exponential Function
• Logarithm
• Statistical Operations
  • Summation
  • Minimum/Maximum
  • Mean/Variance
• Auxiliary Functions
  • Rounding Functions
  • Absolute value
  • Signum function
  • Multiplicative inverse
• Matrix-specific Operations
  • Matrix Inversion
  • Matrix Transposition
  • Matrix Determinant
  • Eigen Decomposition
• DSP-specific Operations
  • Fast Fourier Transform
  • Convolution
  • Cross-Correlation

General Arithmetic Operations

Addition

Subtraction

Multiplication

Element-wise multiplication

Division

Element-wise Division

Modulo

Remainder

Square Root

Summation

Dot Product

Distance

Squared Distance

Power

Exponential

Trigonometric Operations

Sine/Cosine/Tangent

Arc Sine/Cosine/Tangent

Hyperbolic Sine/Cosine/Tangent

Inverse Hyperbolic Sine/Cosine/Tangent

Radians ↔︎ Degrees

Exponential Function

Logarithm

Statistical Operations

Summation

Minimum/Maximum

Mean/Variance

Auxiliary Functions

Rounding Functions

Absolute value

Signum function

Multiplicative inverse

Matrix-specific Operations

Matrix Inversion

Matrix Transposition

Matrix Determinant

Eigen Decomposition

DSP-specific Operations

Fast Fourier Transform

Convolution

Cross-Correlation