Explaining the different types in Metal and SIMD

Question

When working with Metal, I find there\'s a bewildering number of types and it\'s not always clear to me which type I should be using in which context.

In Apple\'s Metal

Answer 1

The types with vector_ and matrix_ prefixes have been deprecated in favor of those with the simd_ prefix, so the general guidance (using float4 as an example) would be:

• In C code, use the simd_float4 type. (You have to include the prefix unless you provide your own typedef, since C doesn't have namespaces.)
• Same for Objective-C.
• In C++ code, use the simd::float4 type, which you can shorten to float4 by using namespace simd;.
• Same for Objective-C++.
• In Metal code, use the float4 type, since float4 is a fundamental type in the Metal Shading Language [1].
• In Swift code, use the float4 type, since the simd_ types are typealiased to shorter names.
• Update: In Swift 5, float4 and related types have been deprecated in favor of SIMD4<Float> and related types.

These types are all fundamentally equivalent, and all have the same size and alignment characteristics so you can use them across languages. That is, in fact, one of the design goals of the simd framework.

I'll leave a discussion of packed types to another day, since you didn't ask.

[1] Metal is an unusual case since it defines float4 in the global namespace, then imports it into the metal namespace, which is also exported as the simd namespace. It additionally aliases float4 as vector_float4. So, you can use any of the above names for this vector type (except simd_float4). Prefer float4.

Answer 2

which type should you use to represent two floating values (x/y)

If you can avoid it, don't use a single SIMD vector to represent a single geometry x,y vector if you're using CPU SIMD.

CPU SIMD works best when you have many of the same thing in each SIMD vector, because they're actually stores in 16-byte or 32-byte vector registers where "vertical" operations between two vectors are cheap (packed add or multiply), but "horizontal" operations can mostly only be done with a shuffle + a vertical operation.

For example a vector of 4 x values and another vector of 4 y values lets you do 4 dot-products or 4 cross-products in parallel with no shuffling, so the overall throughput is significantly more dot-products per clock cycle than if you had a vector of [x1, y1, x2, y2].

See https://stackoverflow.com/tags/sse/info, and especially these slides: SIMD at Insomniac Games (GDC 2015) for more about planning your data layout and program design for doing many similar operations in parallel instead of trying to accelerate single operations.

---

The one exception to this rule is if you're only adding / subtracting to translate coordinates, because that's still purely a vertical operation even with an array-of-structs. And thus fine for CPU short-vector SIMD based on 16-byte vectors. (e.g. the 2nd element in one vector only interacts with the 2nd element in another vector, so no shuffling is needed.)

---

GPU SIMD is different, and I think has no problem with interleaved data. I'm not a GPU expert.

(I don't use Objective C or Metal, so I can't help you with the details of their type names, just what the underlying CPU hardware is good at. That's basically the same for x86 SSE/AVX, ARM NEON / AArch64 SIMD, or PowerPC Altivec. Horizontal operations are slower.)