Filtering numbers quickly with SVE on Amazon Graviton 3 processors
I have had access to Amazon’s latest ARM processors (graviton 3) for a few weeks. To my knowledge, these are the first widely available processors supporting Scalable Vector Extension (SVE).
SVE is part of the Single Instruction/Multiple Data paradigm: a single instruction can operate on many values at once. Thus, for example, you may add N integers with N other integers using a single instruction.
What is unique about SVE is that you work with vectors of values, but without knowing specifically how long the vectors are. This is in contrast with conventional SIMD instructions (ARM NEON, x64 SSE, AVX) where the size of the vector is hardcoded. Not only do you write your code without knowing the size of the vector, but even the compiler may not know. This means that the same binary executable could work over different blocks (vectors) of data, depending on the processor. The benefit of this approach is that your code might get magically much more efficient on new processors.
It is a daring proposal. It is possible to write code that would work on one processor but fail on another processor, even though we have the same instruction set.
