This feature opens many doors for optimizing low-level performance in Go projects, that are already running multicore. IIRC there aren’t a lot of languages with built-in std lib support for SIMD and variants. Love the way Go is trying new stuff lately.
Besides the usual C and C++, we have Java, .NET, D, Zig, Julia, Swift, Rust.
So yeah, also appreciate have Go in the group instead of manually having to write Assembly.
However not many languages adopt ways to manually write SIMD, because most of us have no idea how to write good SIMD code in first place, I surely don't.
Even with languages that adopt ways to manually write SIMD, it’s mostly left to library maintainers rather than application developers.
I work for a C++ timeseries database startup that leverages SIMD about as much as we possibly can, and except for some extremely rare places we just use libraries.
As a first step, it might be possible to write a linter rule that rewrites suitable numeric loops to SIMD. There are already rules to rewrite several loop types, so that should be doable.
It's hard to make predictions with an open source project, but my personal guess is some flavor of it will land (including it is already demonstrating good results without an enormous level of code complexity in the compiler and without overly slowing down compile speeds), but I guess we'll see.
It's being driven by an external contributor who has landed some good changes in the past to the Go compiler. (I think the autovectorization work might be part of their PhD or other academic research, but not sure.)
The problem with Go isn't performance but with the C/C++ interop overhead, even with the "30% less overhead" from a few updates ago which isnt true for 99% of cases, it isnt enough
This feature opens many doors for optimizing low-level performance in Go projects, that are already running multicore. IIRC there aren’t a lot of languages with built-in std lib support for SIMD and variants. Love the way Go is trying new stuff lately.
Vectorizing computations has been Matlabs secret sauce.
Julia does that too.
Does matlab these days do stuff like JIT operator fusing to avoid memory roundtrips and take advantage of FMAs?
Besides the usual C and C++, we have Java, .NET, D, Zig, Julia, Swift, Rust.
So yeah, also appreciate have Go in the group instead of manually having to write Assembly.
However not many languages adopt ways to manually write SIMD, because most of us have no idea how to write good SIMD code in first place, I surely don't.
Even with languages that adopt ways to manually write SIMD, it’s mostly left to library maintainers rather than application developers.
I work for a C++ timeseries database startup that leverages SIMD about as much as we possibly can, and except for some extremely rare places we just use libraries.
With AI I'm pretty sure SIMD will be easier to integrate when necessary.
Oh this is great, it was one of my biggest bugbears about Go since you almost always have to link C/C++ code to get the appropriate performance.
The one negative I'd say is that often autovectorisation is 'good enough' and this doesn't really tackle that gap.
As a first step, it might be possible to write a linter rule that rewrites suitable numeric loops to SIMD. There are already rules to rewrite several loop types, so that should be doable.
The poor Assembler and the unsafe package forgotten in the corner.
While reaching out to CGO is the easier way, it doesn't mean it is the only tool available in Go.
FWIW, there is some pretty substantial autovectorization work that is already in-flight for the Go compiler.
There's a CL stack here:
https://go.dev/cl/791740
It's hard to make predictions with an open source project, but my personal guess is some flavor of it will land (including it is already demonstrating good results without an enormous level of code complexity in the compiler and without overly slowing down compile speeds), but I guess we'll see.
It's being driven by an external contributor who has landed some good changes in the past to the Go compiler. (I think the autovectorization work might be part of their PhD or other academic research, but not sure.)
The problem with Go isn't performance but with the C/C++ interop overhead, even with the "30% less overhead" from a few updates ago which isnt true for 99% of cases, it isnt enough