Time to BOTEC

About

This repository contains example of very simple code to manipulate samples in various programming languages. It implements this platonic estimate:

p_a = 0.8
p_b = 0.5
p_c = p_a * p_b

dists = [0, 1, 1 to 3, 2 to 10]
weights = [(1 - p_c), p_c/2, p_c/4, p_c/4 ]

result = mixture(dists, weights) # should be 1M samples
mean(result)

As of now, it may be useful for checking the validity of simple estimations. The title of this repository is a pun on two meanings of "time to": "how much time does it take to do x", and "let's do x".

Current languages

• C
• Javascript (NodeJS)
• Squiggle
• R
• Python
• Nim

Performance table

With the time tool, using 1M samples:

Language	Time
Nim	0m0.068s
C	0m0.292s
Javascript (NodeJS)	0m0,732s
Squiggle	0m1,536s
R	0m7,000s
Python (CPython)	0m16,641s

Notes

I was really happy trying Nim, and as a result the Nim code is a bit more optimized and engineered:

1. It is using the fastest "danger" compilation mode.
2. It has some optimizations: I don't compute 1M samples for each dist, but instead pass functions around and compute the 1M samples at the end
3. I define the normal function from scratch, using the Box–Muller transform.
4. I also have a version in which I define the logarithm and sine functions themselves in nim to feed into the Box-Muller method. But it is much slower.

Without 1. and 2., the nim code takes 0m0.183s instead. But I don't think that these are unfair advantages: I liked trying out nim and therefore put in more love into the code, and this seems like it could be a recurring factor.

For C, I enabled the -Ofast compilation flag. Without it, it instead takes ~0.4 seconds. Initially, before I enabled the -Ofast flag, I was surprised that the Node and Squiggle code were comparable to the C code. Using bun instead of node is actually a bit slower.

For the Python code, it's possible that the lack of speed is more a function of me not being as familiar with Python. It's also very possible that the code would run faster with PyPy.

Languages I may add later

• Julia (TuringML)
• Rust
• Lisp
• Stan
• Go
• Zig
• Forth
• ... and suggestions welcome

Roadmap

The future of this project is uncertain. In most words, I simply forget about this repository.

To do:

• Check whether the Squiggle code is producing 1M samples. Still not too sure.
• Differentiate between initial startup time (e.g., compiling, loading environment) and runtime. This matters because startup time could be ~constant, so for larger projects only the runtime matters.

Other similar projects

• Squigglepy: https://github.com/rethinkpriorities/squigglepy
• Simple Squiggle: https://github.com/quantified-uncertainty/simple-squiggle