Metal Programming in Julia. Leveraging the power of macOS GPUs with… | by Gabriel Sena | Dec, 2023

Leveraging the power of macOS GPUs with the Metal.jl Framework.


Just last year, we were introduced to the Metal.jl Framework, a GPU backend for Apple Hardware. This is exciting news for Julia practitioners looking to leverage the full potential of their macOS M-series chips. In particular, data scientists and ML engineers can speed up their computational workflows by tapping into the parallel processing power of GPUs, resulting in faster training and inference times. The integration of Metal.jl into the Julia ecosystem signifies an important push towards aligning the language’s capabilities with the continually evolving landscape of scientific computing and machine learning on Apple platforms.

In 2020, Apple began transitioning its Mac lineup from Intel-based processors to Apple Silicon, starting with the M1 chip. While this has been a historic and impressive achievement from Apple it did come with its fair share of criticisms and issues. Since picking up my 32-core Mac Studio M1-chip, I have been looking to fully leverage the GPU and tinker with new applications. I must say, it hasn’t been all fun and games. From ARM architecture compatibility issues to unsupported machine learning libraries — it has been a challenge at times to get a working environment. This is expected with any huge major transition and way of operating. I remain hopeful and have seen major improvements across the board for stability and features.

In this article, we will preview the Metal.jl Framework in order to understand its capabilities. We will also demonstrate a practical example using Flux, a library for machine learning in Julia, with the Metal backend.

Here is the outline for the topics covered:

I. Project Setup
i. Julia environment setup
ii. Dependency overview

II. Leveraging the Metal API
i. Kernel Functions
ii. Benchmarking
iii. Profiling

III. Working with Flux and Metal Backend
i. Dataset overview
ii. Simple neural network
iii. Model evaluation

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