-
-
InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
The part about data dependencies across loop iterations is fascinating to me, becuase it's mostly invisible even when you look at the generated assembly. There's a related optimization that comes up in implementations of ChaCha/BLAKE, where we permute columns around in a kind of weird order, because it breaks a data dependency for an operation that's about to happen: https://github.com/sneves/blake2-avx2/pull/4#issuecomment-50...
I think it's worth pointing out that the reason why these two examples execute at different speed is due to how compiler translated code AND because CPU was able to parallelize work. Compilers take knowledge about target platform (e.g. instruction set) and code and translate it into executable code. Compiler CAN (but doesn't have to) rewrite code only if it ALWAYS produces the same result as input code.
I feel like last 110-15 years (majority of) people have stopped thinking about specific CPU and only think about ISA. That works for a lot of workloads but in recent years I have observed that there is more and more interest in how specific CPU can execute code as efficiently as possible.
If you're interested in the kind of optimizations performed in the example you should check out polyhedral compilation (https://polyhedral.info/) and halide (https://halide-lang.org/). Both can be used to speed up certain workloads significantly.