alt Hacker NewsYou'll never get the full feature set of all SIMD implementations in a single generic abstraction but I'm not sure that's the only way a SIMD abstraction could provide value. There is a pretty consistent set of narrow SIMD that behaves close enough to consistently get better performance across generic targets with high level abstractions better than auto vectorized scalar code but worse than platform tuned SIMD.
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gdwatson • today at 5:21 AM
Is the common feature set described anywhere online? I am not in the SIMD world, and I am curious about the state of it. But I am not so committed that I am wiling to learn several different platforms’ SIMD implementations in order to suss out their commonalities.
The subset of SIMD that translates is so narrow that it is almost useless in real code. That’s the problem. Additionally, it is invalid for the compiler to redesign your data structures.
The ability of compilers to use SIMD is no better than it was 20 years ago. Total stagnation for all practical purposes, and the reason is that compilers fundamentally lack the context and freedom to rewrite the code architecture appropriately for SIMD optimization (even if they could).
I think a point people miss is that highly optimized SIMD code is almost never about parallelizing loops. It is about deeply probing and manipulating arbitrarily complex data structures in parallel that involve collections of unrelated types. SIMD allows you to do this, and it is fantastically efficient, but a compiler has no conceivable way of understanding this.
For this reason, SIMD optimization has often been classified as an AI-Complete problem in computer science.