David Mashburn davidmashburn

introduction

inspired by a friend’s fledgling language design and motivated by the JeanHeyd Meneide RustConf Fiasco™ and improving the story for compile-time introspection, i decided i needed a place to spew the last year’s musings on variadic generics in Rust in one mighty, less-than-understandable catharsis.

i think somewhere in here is a considered and reasonable design, so that’s neat!

perhaps i’ll make this an RFC one day. probably not. you have my express permission to do that yourself.

variadic generics?

this nugget of language jargon encapsulates the idea that we might want to bind to an arbitrarily large list of generic parameters, all at once. there are many reasons to want to do this:

we might want to implement a trait for all tuples, where each element of the tuple implements the same trait. [bevy_ecs's Bundle trait](https://github.com/bevyengine/bevy/blob/87f7d013c0d4f45a93a4c29e5d48237f4b512ca2/crates/bevy_ecs/src/bundle.

my database had 72k annotations at the time I ran these benchmarks, here's the result:

$ python scripts/batch_bench.py conf/development-app.ini dumb
Memory summary: start
      types |   # objects |   total size
=========== | =========== | ============
       dict |       13852 |     12.46 MB
  frozenset |         349 |     11.85 MB
VM: 327.29Mb


	import { buildData } from "./build-data";

	<let/data = []/>
	<let/selected = null/>

	<div class="jumbotron">
	<div class="row">
	<div class="col-md-6">
	<h1>Marko 6 (keyed)</h1>

	import asyncio
	from typing import Coroutine, List, Sequence


	def _limit_concurrency(
	coroutines: Sequence[Coroutine], concurrency: int
	) -> List[Coroutine]:
	"""Decorate coroutines to limit concurrency.

	Enforces a limit on the number of coroutines that can run concurrently in higher

	const dfd = require("danfojs-node")
	const tf = require("@tensorflow/tfjs-node")

	async function load_process_data() {
	let df = await dfd.read_csv("https://web.stanford.edu/class/archive/cs/cs109/cs109.1166/stuff/titanic.csv")

	//A feature engineering: Extract all titles from names columns
	let title = df['Name'].apply((x) => { return x.split(".")[0] }).values
	//replace in df
	df.addColumn({ column: "Name", value: title })

	"""TensorFlow 2.0 implementation of vanilla Autoencoder."""
	import numpy as np
	import tensorflow as tf

	__author__ = "Abien Fred Agarap"

	np.random.seed(1)
	tf.random.set_seed(1)
	batch_size = 128
	epochs = 10

	########################
	# UUID for SQLite hack #
	########################

	from sqlalchemy.types import TypeDecorator, CHAR
	from sqlalchemy.dialects.postgresql import UUID
	import uuid


	class GUID(TypeDecorator):

	# Download installers
	mkdir ~/Downloads/nvidia
	cd ~/Downloads/nvidia
	wget https://developer.nvidia.com/compute/cuda/8.0/Prod2/local_installers/cuda_8.0.61_375.26_linux-run
	wget http://us.download.nvidia.com/XFree86/Linux-x86_64/384.59/NVIDIA-Linux-x86_64-384.59.run
	sudo chmod +x NVIDIA-Linux-x86_64-384.59.run
	sudo chmod +x cuda_8.0.61_375.26_linux-run
	./cuda_8.0.61_375.26_linux-run -extract=~/Downloads/nvidia/
	# Uninstall old stuff
	sudo apt-get --purge remove nvidia-*

	/// given this uncompilable code
	pub fn main() {
	let vec0 = Vec::new();

	let mut vec1 = fill_vec(vec0);

	// Do not change the following line!
	println!("{} has length {} content `{:?}`", "vec0", vec0.len(), vec0);

	vec1.push(88);

	# VERY IMPORTANT! After each kernel update or dkms rebuild the modules must be signed again with the script
	# ~/.ssl/sign-all-modules.sh

	# Place all files in ~/.ssl folder
	mkdir ~/.ssl
	cd ~/.ssl

	# Generate custom keys with openssl
	openssl req -new -x509 -newkey rsa:2048 -keyout MOK.priv -outform DER -out MOK.der -nodes -subj "/CN=Owner/"