Overview

The Metal.jl package provides three distinct, but related, interfaces for Metal programming:

• the MtlArray type: for programming with arrays;
• native kernel programming capabilities: for writing Metal kernels in Julia;
• Metal API wrappers: for low-level interactions with the Metal libraries.

Much of the Julia Metal programming stack can be used by just relying on the MtlArray type, and using platform-agnostic programming patterns like broadcast and other array abstractions. Only once you hit a performance bottleneck, or some missing functionality, you might need to write a custom kernel or use the underlying Metal APIs.

The MtlArray type

The MtlArray type is an essential part of the toolchain. Primarily, it is used to manage GPU memory, and copy data from and back to the CPU:

a = MtlArray{Int}(undef, 1024)

# essential memory operations, like copying, filling, reshaping, ...
b = copy(a)
fill!(b, 0)
@test b == Metal.zeros(Int, 1024)

# automatic memory management
a = nothing

Beyond memory management, there are a whole range of array operations to process your data. This includes several higher-order operations that take other code as arguments, such as map, reduce or broadcast. With these, it is possible to perform kernel-like operations without actually writing your own GPU kernels:

a = Metal.zeros(1024)
b = Metal.ones(1024)
a.^2 .+ sin.(b)