By Simon Marlow
If you could have a operating wisdom of Haskell, this hands-on e-book indicates you the way to exploit the language’s many APIs and frameworks for writing either parallel and concurrent courses. You’ll find out how parallelism exploits multicore processors to hurry up computation-heavy courses, and the way concurrency helps you to write courses with threads for a number of interactions.
writer Simon Marlow walks you thru the method with plenty of code examples so you might run, test with, and expand. Divided into separate sections on Parallel and Concurrent Haskell, this ebook additionally contains routines that can assist you familiarize yourself with the innovations presented:
• show parallelism in Haskell with the Eval monad and assessment concepts
• Parallelize traditional Haskell code with the Par monad
• construct parallel array-based computations, utilizing the Repa library
• Use the speed up library to run computations without delay at the GPU
• paintings with easy interfaces for writing concurrent code
• construct bushes of threads for greater and extra complicated courses
• easy methods to construct high-speed concurrent community servers
• Write disbursed courses that run on a number of machines in a community
Read Online or Download Parallel and Concurrent Programming in Haskell: Techniques for Multicore and Multithreaded Programming PDF
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Extra info for Parallel and Concurrent Programming in Haskell: Techniques for Multicore and Multithreaded Programming
Granularity Looking back at Figure 3-5, I remarked earlier that the parallel section didn’t finish evenly, with two cores running a bit longer than the others. Ideally, we would have all the cores running until the end to maximize our speedup. As we saw in “Example: Parallelizing a Sudoku Solver” on page 19, having too few work items in our parallel program can impact the speedup, because the work items can vary in cost. To get a more even run, we want to create fine-grained work items and more of them.
ToList vec) , count > 0 ] 38 | Chapter 3: Evaluation Strategies Here we make a new Cluster, using pointSumToCluster, from each PointSum produced by assign. There is a slight complication in that we have to avoid creating a cluster with no points, because it cannot have a centroid. Finally step combines assign and makeNewClusters to implement one complete iter‐ ation: step :: Int -> [Cluster] -> [Point] -> [Cluster] step nclusters clusters points = makeNewClusters (assign nclusters clusters points) To complete the algorithm, we need a loop to repeatedly apply the step function until convergence.
The goal is to parallelize the solving of multiple puzzles. We aren’t interested in the details of how the solver works; for the purposes of this discussion, the solver will be treated as a black box. It’s just an example of an expensive computation that we want to perform on multiple data sets, namely the Sudoku puzzles. We will use a module Sudoku that provides a function solve with type: solve :: String -> Maybe Grid The String represents a single Sudoku problem. , or contains a digit 1–9. The function solve returns a value of type Maybe Grid, which is either Nothing if a problem has no solution, or Just g if a solution was found, where g has type Grid.