Using vDSP for vector-based arithmetic

Overview

vDSP provides a suite of general-purpose, high-performance arithmetic functions that are alternatives to for loops and map when applying operations on collections of floating-point values.

For example, the code below multiplies the element-wise sum of two arrays by a scalar value:

let a: [Float] = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
let b: [Float] = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
let c: Float = 2

let d = zip(a, b).map {
    ($0.0 + $0.1) * c
}

// d = [22.0, 44.0, 66.0, 88.0, 110.0, 132.0, 154.0, 176.0, 198.0, 220.0]

The vDSP version of the same operation runs significantly faster.

let d = [Float](unsafeUninitializedCapacity: a.count) {
    buffer, initializedCount in
    
    vDSP.multiply(addition: (a, b),
                  c,
                  result: &buffer)
    
    initializedCount = a.count
}

// d = [22.0, 44.0, 66.0, 88.0, 110.0, 132.0, 154.0, 176.0, 198.0, 220.0]

Many vDSP functions have a variant that returns the result.

let a: [Float] = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
let b: [Float] = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
let c: Float = 2

let d = vDSP.multiply(addition: (a, b),
                      c)

Available arithmetic functions

The following table summarizes the basic arithmetic functions available in vDSP. All functions are available in single- and double-precision variants.

In the Operation column, a subscript (for example, a[i]) indicates a vector, and no subscript (for example, a) indicates a scalar value.

See Also

Signal Processing Essentials

• Controlling vDSP operations with stride
• Using linear interpolation to construct new data points
• Resampling a signal with decimation
• vDSP