![]() ![]() Obscure the benefits of using Haskell in the first place. Although possible, this begins to look untidy and begins to These also, and hence lifting a lot of the program into the IO Which themselves call other functions, this will require using CPS on If countData then goes on to call several other functions, This means lifting the countData function into the IO monad. Insert x = if x addElem position newSoFar otherLines) Let valueWanted = extract position parsedLine Parts of a parsed text file, building up a sorted list using insertionĪddElem position listSoFar (parsedLine:otherLines) = (x:) )Īs an concrete example, here is some code which extracts particular f always stays on the outside, evenį p (x:xs) k = if p x then f p xs ( k. Then the continuation function and the result which is being built up ![]() Then the new type of f with a continuation function will be This function is to be applied whenever f produces a Now f has to take an extra argument, say k, the "continuationįunction". We set f as the outermost function by introducing a new argument to f,į a (\intermediateresult -> h intermediateresult b c) We need to bring f to the outside of the computation. Than the evaluated answer, this is wasteful. If these unevaluated instances of f take up much more memory G a b c = let intermediate_result = f a inĪs Haskell's evaluation order is outermost first, function f will notīe applied until necessary, and will be saved as is without being For example, consider the following code: Using CPS is a way of ensuring the evaluation order ofįunctions. ![]() Style (CPS) is one purely functional way of forcingĮvaluation. Shows that code is being detrimentally lazy, Continuation Passing And even then, the right choice of algorithm or data Times slower in my experience) and it can take a long time to get all the information Unfortunately, profiling can be slow (usually around 10 There are huge gains to be made by using better algorithms and data This pointed to using a screeningįunction first, which did a less expensive test to work out whether theįunction really did need to be called, which saved half the time. My functions was taking up all the time not because it was slow butīecause it was being called so often. Similarly, time profiling showed that one of (lists rather than prefix trees) before and getting this right speeds I've used the wrong type of sort and the wrong type of data structure Of sorting algorithms (8/10/97, "Re: heap sort or the wonder of That people post for various problems, such as Ralf Hinze's collection Handy algorithms are available in Chris Okasaki'sīook ( Okasaki1999), and from the Haskell web page Time and heap profiling are great for spotting which parts of yourĬode need changing. Or update existing algorithms are guaranteed not to interfere with lack of side effects - any new functions the user writes to replace.modularity of code - a whole module for a particular ADT or.the good support for abstract data types - users can easily define.This is not Haskell specific, though itĬould well be easier in Haskell than some other languages, given Obvious, it is worth restating since this is the solution that will Strict datatypes, seq, ($!), DeepSeq and Strategiesįirst of all change the algorithm and/or data structure.This is not usually found in the Haskell textbooks. My own reference, and for anyone else who would like similar help, as This is a review of some of the processes I've been using toĭeal with the problems caused by processing large amounts of data, for When everything needs processing and all calculations willīe used, laziness just means that programs take up extortionateĪmounts of heap space while they delay all computation until the last Where laziness has been harmful than instances when it isīeneficial. Process large data sets in Haskell I've come across more instances Is Haskell's laziness actually a Good Thing? In writing code to Making Haskell programs faster and smaller Making Haskell programs faster and smaller ![]()
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