Fearless SIMD v1.0

原文开头节选

It has come a long way since the original prototype 8 years ago . We are now confident that whatever it is you need, be it just autovectorization and multiversioning, or full-blown portable SIMD abstractions, or safe access to intrinsics and nothing more, Fearless SIMD will serve you well.

Instead of paraphrasing the changelog , I’d like to take this opportunity to reflect on the goals of Fearless SIMD, how it achieves them, and what sets it apart from other SIMD abstractions.

A common criticism leveled at portable SIMD abstractions is that they aren’t performant enough, so we’ve put a lot of effort into making sure that Fearless SIMD never holds you back.

For example, when implementing portable abstractions for operations with different behavior in edge cases on different platforms, such as swizzles or floating-point maximum , we provide both a precise variant that’s the same on all platforms, and a fast variant that returns a platform-dependent result for use when you expect the edge cases to never happen.

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