This is a short post that explains how to write a high-performance matrix multiplication program on modern processors. In this tutorial I will use a single core of the Skylake-client CPU with AVX2, but the principles in this post also apply to other processors with different instruction sets (such as AVX512).
Matrix multiplication is a mathematical operation that defines the product of
| {-# LANGUAGE MultiParamTypeClasses, ExistentialQuantification, ScopedTypeVariables, FlexibleInstances, FlexibleContexts, UndecidableInstances #-} | |
| module TransientCont where | |
| -- some imports | |
| import Control.Applicative | |
| import Control.Monad.IO.Class | |
| import Control.Monad.Trans |
| layout | post |
|---|---|
| title | A comonad of graph decompositions |
| date | 2016-04-02 13:02:54 -0400 |
| comments | true |
| author | Rúnar |
| categories | scala comonads |
| commentIssueId | 22 |
| {-# LANGUAGE GeneralizedNewtypeDeriving #-} | |
| module SliderPuzzle where | |
| import Prelude hiding (Either(..)) | |
| import Data.Vector (Vector, (//), (!?)) | |
| import qualified Data.Vector as Vec | |
| import Control.Arrow (second, (***)) | |
| import Control.Monad.State.Strict | |
| import Data.Set (Set) | |
| import qualified Data.Set as Set |
| ## 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 |
| {-# LANGUAGE DeriveFunctor, TypeFamilies #-} | |
| module Success | |
| ( Parser(..) | |
| , parse | |
| ) where | |
| import Control.Applicative | |
| import Control.Monad | |
| import Data.Bifunctor | |
| import Data.Profunctor |
| {-#LANGUAGE GADTs, KindSignatures, FlexibleInstances, FunctionalDependencies, NoMonomorphismRestriction #-} | |
| -- DOMAIN SPECIFIC LANGUAGES IN HASKELL | |
| -- Fredrik Dyrkell | |
| -- @lexicallyscoped | lexicallyscoped.com | |
