JamesMGreene

Initialize

gitflow	git
git flow init	git init
	git commit --allow-empty -m "Initial commit"
	git checkout -b develop master

Connect to the remote repository

rygorous

Why do compilers even bother with exploiting undefinedness signed overflow? And what are those
mysterious cases where it helps?

A lot of people (myself included) are against transforms that aggressively exploit undefined behavior, but
I think it's useful to know what compiler writers are accomplishing by this.

TL;DR: C doesn't work very well if int!=register width, but (for backwards compat) int is 32-bit on all
major 64-bit targets, and this causes quite hairy problems for code generation and optimization in some
fairly common cases. The signed overflow UB exploitation is an attempt to work around this.

AndrasKovacs

{-
At ICFP 2022 I attended a talk given by Tomasz Drab, about this paper:

    "A simple and efficient implementation of strong call by need by an abstract machine"
    https://dl.acm.org/doi/10.1145/3549822

This is right up my alley since I've implemented strong call-by-need evaluation
quite a few times (without ever doing more formal analysis of it) and I'm also
interested in performance improvements. Such evaluation is required in
conversion checking in dependently typed languages.

lexi-lambda

Monads and delimited control are very closely related, so it isn’t too hard to understand them in terms of one another. From a monadic point of view, the big idea is that if you have the computation m >>= f, then f is m’s continuation. It’s the function that is called with m’s result to continue execution after m returns.

If you have a long chain of binds, the continuation is just the composition of all of them. So, for example, if you have

m >>= f >>= g >>= h

then the continuation of m is f >=> g >=> h. Likewise, the continuation of m >>= f is g >=> h.

luc-tielen

{-# LANGUAGE TemplateHaskell #-}
{-# LANGUAGE GADTs #-}
{-# LANGUAGE TypeFamilies #-}
{-# LANGUAGE MultiParamTypeClasses #-}

module Bad where

import Prelude
import Data.Kind ( Type )
import Generics.MultiRec.TH

CodaFi

/// Playground - noun: a place where people can play
/// I am the very model of a modern Judgement General

//: # Algorithm W

//: In this playground we develop a complete implementation of the classic
//: algorithm W for Hindley-Milner polymorphic type inference in Swift.

//: ## Introduction

mb64

(* Compile with:
  $ ocamlfind ocamlc -package angstrom,stdio -linkpkg tychk.ml -o tychk
Example use:
  $ ./tychk <<EOF
  > let f = (fun x -> x) : forall a. a -> a
  > in f f
  > EOF
  input : forall a. a -> a
*)

AndrasKovacs

{-# language Strict, LambdaCase, BlockArguments #-}
{-# options_ghc -Wincomplete-patterns #-}

{-
Minimal demo of "glued" evaluation in the style of Olle Fredriksson:

  https://github.com/ollef/sixty

The main idea is that during elaboration, we need different evaluation

paf31

## Principal type-schemes for functional programs

**Luis Damas and Robin Milner, POPL '82**

> module W where

> import Data.List
> import Data.Maybe
> import Data.Function

serras

Program Analysis, a Big Happy Family

The idea behind program analysis is simple, right? You just want to know stuff about your program before it runs, usually because you don't want unexpected problems to arise (those are better in movies.) Then why looking at Wikipedia gives you headaches? Just so many approaches, tools, languages 🤯

In this article I would like to give a glimpse of an overarching approach to program analysis, based on ideas from abstract interpretation. My goal is not to pinpoint a specific technique, but rather show how they have common core concepts, the differences being due mostly to algorithmic challenges. In other words, static analysis have a shared goal, but it's a challenge to make them precise and performant.

Code is meant to be executed by a computer. Take the following very simple function:

fun cantulupe(x) = {