BenchmarkDotNet VS plb2

The name of the library is BenchmarkDotNet and the documentation can be found here. The only thing you have to do is:

Benchmarking Tool: Uses BenchmarkDotNet for accurate and repeatable measurements.

Through Professional tools like Visual studio Performance tool BenchmarkDotNet DotTrace Profiler by Rider, Dynatrace Datadog

BenchmarkDotNet helps you measure performance with ease.

The benchmark test was conducted using the BenchmarkDotNet library in C#. The classes tested include:

Let’s look at the first example you see, when you open up BenchmarkDotnet’s website, or Github page.

Or use BenchmarkDotNet which, among other things to get an accurate benchmark, does JIT warmup outside of measurement.

( https://github.com/dotnet/BenchmarkDotNet ).

You can also do proper statistically correct benchmarking by using - https://github.com/dotnet/BenchmarkDotNet. This will run warmup the jit, gauge the overheads, and run your function many times to give you proper data.

take a look at: https://benchmarkdotnet.org/

> Truffle Ruby is as fast as V8 already and MRI yJIT and jRuby are catching up fast

According to which benchmark? At my hand [1], node is ~60% faster than TruffleRuby and over an order of magnitude faster than yjit v3.3.0.

[1] https://github.com/attractivechaos/plb2?tab=readme-ov-file#a...

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.