To understand vectorization, let's first look at a simple calculation over the elements of an array using scalar code. Accelerate gets its performance benefits by using vectorization. And vImage, that provides a huge selection of image processing functions and integrates easily with core graphics and core video. Quadrature, that's dedicated to the numerical integration of functions. vForce that provides arithmetic and transcendental functions including trig and logarithmic routines. The four libraries we focused on are vDSP that provides digital signal processing routines including arithmetic on large vectors, Fourier transforms, biquadratic filtering, and powerful type conversion. We've looked at four libraries and created new Swift-friendly APIs to make using Acclerate in Swift projects really easy. This means your users are going to have an overall better experience.Īccelerate's libraries are immensely powerful but up until now, their interfaces weren't that friendly to Swift developers. This includes not only macOS and iOS but watchOS and tVOS as well. We provide the primitives across all of Apple's platforms. So, if you're an app developer and you use the Accelerate framework, not only will your application run faster, but you'll also use less battery life.
This means we get excellent performance and this performance translates directly into energy savings. Most of these primitives are hand tuned to the microarchitecture of the processor. The primary purpose of Accelerate is to provide thousands of low-level math primitives that run on a CPU and support image and signal processing, vector arithmetic, linear algebra, and machine learning. Before we dive into the Swift Overlay, let's recap exactly what the Accelerate framework is.
And second, measuring Accelerate's performance using the Linpack benchmark. First, our new Swift Overlay for Accelerate.
In this presentation, I'll be talking about two topics. My name is Simon Gladman and I'm with the Vector and Numerics group.
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