# Comparing Haskell and Julia for High-Performance Numerical Code

DevFeed: [Comparing Haskell and Julia for High-Performance Numerical Code](<https://devfeed.tech/articles/haskell-for-numerics-36136.md>)

Original publisher: [Read original article](<https://idontgetoutmuch.wordpress.com/2017/06/02/1090/>)

Author: Dominic Steinitz

Published: 2017-06-02T15:47:12Z

Content type: opinion

Language: en

Sources: [Maths, Stats & Functional Programming](<https://devfeed.tech/sources/maths-stats-functional-programming.md>)

Topics: [Haskell](<https://devfeed.tech/topics/haskell.md>), [The Julia Language](<https://devfeed.tech/topics/julia.md>), [Programming](<https://devfeed.tech/topics/programming.md>), [floating-point](<https://devfeed.tech/topics/floating-point.md>)

Tags: [analysis](<https://devfeed.tech/tags/analysis.md>), [code](<https://devfeed.tech/tags/code.md>), [haskell](<https://devfeed.tech/tags/haskell.md>), [llvm](<https://devfeed.tech/tags/llvm.md>), [numerical-methods](<https://devfeed.tech/tags/numerical-methods.md>), [numerics](<https://devfeed.tech/tags/numerics.md>), [programming](<https://devfeed.tech/tags/programming.md>)

## AI overview

The article compares Julia with a Haskell approach using the Accelerate domain-specific embedded language for numerical computing. It discusses the goal of combining type safety with high performance and the motivation of supporting statistical inference methods.

## Source excerpt

Introduction Summary Back in January, a colleague pointed out to me that GHC did not produce very efficient code for performing floating point abs. I have yet to produce a write-up of my notes about hacking on GHC: in summary it wasn't as difficult as I had feared and the #ghc folks were extremely helpful. ... Continue reading Haskell for Numerics?