Intel C/C++ compiler performance versus Julia

[Elrod]

@johnh

Moving the discussion along a little, in the past on this forum we have seen benchmarking reports with Julia code which in the end turn out to be dominated by the BLAS library performance.

Multithreaded Julia matrix multiply (specifically, Octavian.jl) “dominates” MKL <100x100, and hangs on in performance

Simply putting LoopVectorization.@tturbo on three for loops manages to do well until we run out of L2 cache. Results will differ by CPU of course. That particular CPU has much more L2 cache than most (18 cores, 1 MiB/core).

MKL starts to run away from the competition around 3000x3000 on that computer.
On AMD systems, the small size performance of Julia-matrix multiply is unmatched:

This was on a Ryzen 4900HS.

@lmiq

I wander what Chris Elrod would have to say about this. He seems to be writing stuff in Julia that is beating the performance of everything else. A good understanding of the hardware and the interface of the code with the compiler appears to be enough to (someone like him) write code that performs in the limits of the processador capacity. I read somewhere that part of that performance comes from generated code at runtime for the types and sizes of the variables in case, something more natural to be done in a language like Julia.

The DifferentialEquations ecosystem doesn’t really do any hardware-specific optimizations itself yet, but it (and ModelingToolkit in particular) is another great example of how code generation can be leveraged for better problem solving in Julia.
A long-term goal of mine is to work on an SLP vectorizer it can use, as well as a SPMD compiler like ISPC. DifferentialEquations would be the target for both of these, but they should be usable by interested Julia projects more generally.

But for now, I still have a lot of loop work ahead of me (in particular, modeling much more complicated loops so they can be optimized and still produce correct results).