plb2 VS 1brc

https://github.com/attractivechaos/plb2 - limited but broad comparison across a large number of languages. Swift and Nim both compare favourably to C.

https://github.com/attractivechaos/plb2/blob/master/README.m...

Synthetic benchmarks aside, I think as far as average (spring boots of the world) code goes, Go beats Java almost every time, often in less lines than the usual pom.xml

I wouldn't be so enthusiastic. Look at other languages that have JIT now: Ruby and PHP. After years of efforts, they are still an order of magnitude slower than V8 and even PyPy [1]. It seems to me that you need to design a JIT implementation from ground up to get good performance – V8, Dart, LuaJIT and PyPy are like this; if you start with a pure interpreter, it may be difficult to speed it up later.

[1] https://github.com/attractivechaos/plb2

A curious thing about Swift: after https://github.com/attractivechaos/plb2/pull/23, the matrix multiplication example is comparable to C and Rust. However, I don’t see a way to idiomatically optimise the sudoku example, whose main overhead is allocating several arrays each time solve() is called. Apparently, in Swift there is no such thing as static array allocation. That’s very unfortunate.

There are some good ideas for this type of problem here: https://github.com/dannyvankooten/1brc

After you deal with parsing and hashes, basically you are IO limited so mmap helps. A reasonable guess is that even for the optimal CUDA implementation, because there is no compute to speak of other than a hashmap, the starting of kernels and transfer of data to the GPU would likely add a noticeable bottleneck and make the optimal CUDA code slower than this pure C code.

c dominates every other language again...https://github.com/dannyvankooten/1brc#submitting

You can run the bin/create-sample program from this C implementation here: https://github.com/dannyvankooten/1brc

It’s just the city names + averages from the official repository using a normal distribution to generate 1B random rows.