If i'm understanding the repository correctly, it looks like each language reads from a file, does some I/O printing to console, then computes the value, then some more console printing and exits.

In my opinion, the comparisons could be better if the file I/O and console printing were removed.

Some seeings:

- C++ unsurpassable king.

- There's a stark jump of times going from ~200ms to ~900ms. (Rust v1.92.0 being an in-between outlier.)

- C# gets massive boost (990->225ms) when using SIMD.

- But C++ somehow gets slower when using SIMD.

- Zig very fast*!

- Rust got big boost (630ms->230ms) upgrading v1.92.0->1.94.0.

- Nim (that compiles to C then native via GCC) somehow faster than GCC-compiled C.

- Julia keeps proving high-level languages can be fast too**.

- Swift gets faster when using SIMD but loses much accuracy.

- Go fastest language with own compiler (ie not dependent to GCC/LLVM).

- V (also compiles to C) expected it (appearing similar) be close to Nim.

- Odin (LLVM) & Ada (GCC) surprisingly slow. (Was expecting them to be close to Zig/Fortran.)

- Crystal slowest LLVM-based language.

- Pure CPython unsurpassable turtle.

Curious how D's reference compiler (DMD) compares to the LLVM/GCC front-ends, how LFortran to gfortran, and QBE to GCC/LLVM. Also would like to see Scala Native (Scala currently being inside the 900~1000ms bunch).

* Note that uses `@setFloatMode(.Optimized)` which according to docs is equivalent to `--fast-math` but only D/Fortran use this flag (C/C++ do not).

** Uses `@fastmath` AND `@simd`. The comparison supposedly is for performance on idiomatic code and for Julia SIMD is a simple annotation applied to the loop (and Julia may even auto do it) but should still be noted because (as seen in C# example) it can be big.

  > var rounds = int.Parse(File.ReadAllText("rounds.txt"));

  > var pi = 1.0D;
  > var x = 1.0D;

  > for (var i = 2; i < rounds + 2; i++) {
  >     x = -x;
  >     pi += x / (2 \* i - 1);
  > }

  > pi \*= 4;
  > Console.WriteLine(pi);

How I love pypy for certain tasks. On my laptop:

  ᐅ time uv run -p cpython-3.14 leibniz.py
  3.1415926525880504
  
  ________________________________________________________
  Executed in   38.24 secs    fish           external
     usr time   37.91 secs  158.00 micros   37.91 secs
     sys time    0.16 secs  724.00 micros    0.16 secs
  
  ᐅ time uv run -p pypy leibniz.py
  3.1415926525880504
  
  ________________________________________________________
  Executed in    1.52 secs    fish           external
     usr time    1.16 secs    0.25 millis    1.16 secs
     sys time    0.02 secs    1.29 millis    0.02 secs

It was a free 25x speedup.

After seeing Swift's result I had to look into the source to confirm that yes, it was not written by someone who knows the language.

But this is a good benchmark results that demonstrate what performance level can you expect from every language when someone not versed in it does the code porting. Fair play

Startup time doesn’t seem to be factored in correctly, so any language that uses a bytecode (e.g. Java) or is compiling from source (e.g. Ruby, Python, etc.) will look poor on this. If the kids of applications that you write are ones that exit after a fraction of a second, then sure, this will tell you something. But if you’re writing server apps that run for days/weeks/months, then this is useless.

Reading the fine print, the benchmark is not just the Leibniz formula like it says in the chart title. It also includes file I/O, startup time, and console printing:

> Why do you also count reading a file and printing the output?

> Because I think this is a more realistic scenario to compare speeds.

Which is fine, but should be noted more prominently. The startup time and console printing obviously aren't relevant for something like the Python run, but at the top of the chart where runs are a fraction of a second it probably accounts for a lot of the differences.

Running the inner loop 100 times over would have made the other effects negligible. As written, trying to measure millisecond differences between entire programs isn't really useful unless someone has a highly specific use case where they're re-running a program for fractions of a second instead of using a long-running process.

This sort of thing is pretty meaningless unless the code is all written by people who know how to get performance out of their languages (and they're allowed to do so). Did you use the right representation of the data? Did you use the right library? Did you use the right optimization options? Did you choose the fast compiler or the slow one? Did you know there was a faster or slower one? If you're using fancy stuff, did you use it right?

I did the same sort of thing with the Seive of Eratosthenes once, on a smaller scale. My Haskell and Python implementations varied by almost a factor of 4 (although you could argue that I changed the algorithm too much on the fastest Python one). OK, yes, all the Haskell ones were faster than the fastest Python one, and the C one was another 4 times faster than the fastest Haskell one... but they were still over the place.

The Clojure version is not AOT'd, so it's measuring startup + compiler time. When properly compiled it should be comparable to the Java implementation.