If you have a working knowledge of Haskell, this hands-on book shows you how to use the language's many APIs and frameworks for writing both parallel and concurrent programs. You'll learn how parallelism exploits multicore processors to speed up computation-heavy programs, and how concurrency enables you to write programs with threads for multiple interactions. Author Simon Marlow walks you through the process with lots of code examples that you can run, experiment with, and extend. Divided into separate sections on Parallel and Concurrent Haskell, this book also includes exercises to help you become familiar with the concepts presented: Express parallelism in Haskell with the Eval монада and Evaluation Strategies Parallelize ordinary Haskell code with the Par монада Build parallel array-based computations, using the Repa library Use the Accelerate library to run computations directly on the GPU Work with basic interfaces for writing concurrent code Build trees of threads for larger and more complex programs Learn how to build high-speed concurrent network servers Write distributed programs that run on multiple machines in a networkChapter 1 : IntroductionTerminology: Parallelism and ConcurrencyTools and ResourcesSample CodeParallel HaskellChapter 2 : Basic Parallelism: The Eval MonadLazy Evaluation and Weak Head Normal FormThe Eval Монада, rpar, and rseqExample: Parallelizing a Sudoku SolverDeepseqChapter 3 : Evaluation StrategiesParameterized StrategiesA Strategy for Evaluating a List in ParallelExample: The K-Means ProblemGC'd Sparks and Speculative ParallelismParallelizing Lazy Streams with parBufferChunking StrategiesThe Identity PropertyChapter 4 : Dataflow Parallelism: The Par MonadExample: Shortest Paths in a GraphPipeline ParallelismExample: A Conference TimetableExample: A Parallel Type InferencerUsing Different SchedulersThe Par Монада Compared to StrategiesChapter 5 : Data Parallel Programming with RepaArrays, Shapes, and IndicesOperations on ArraysExample: Computing Shortest PathsFolding and Shape-PolymorphismExample: Image RotationSummaryChapter 6 : GPU Programming with AccelerateOverviewArrays and IndicesRunning a Simple Accelerate ComputationScalar ArraysIndexing ArraysCreating Arrays Inside AccZipping Two ArraysConstantsExample: Shortest PathsExample: A Mandelbrot Set GeneratorConcurrent HaskellChapter 7 : Basic Concurrency: Threads and MVarsA Simple Example: RemindersCommunication: MVarsMVar as a Simple Channel: A Logging ServiceMVar as a Container for Shared StateMVar as a Building Block: Unbounded ChannelsFairnessChapter 8 : Overlapping Input/OutputExceptions in HaskellError Handling with AsyncMergingChapter 9 : Cancellation and TimeoutsAsynchronous ExceptionsMasking Asynchronous ExceptionsThe bracket OperationAsynchronous Exception Safety for ChannelsTimeoutsCatching Asynchronous Exceptionsmask and forkIOAsynchronous Exceptions: DiscussionChapter 10 : Software Transactional MemoryRunning Example: Managing WindowsBlockingBlocking Until Something ChangesMerging with STMAsync RevisitedImplementing Channels with STMAn Alternative Channel ImplementationBounded ChannelsWhat Can We Do Not with STM?PerformanceSummaryChapter 11 : Higher Level Concurrency AbstractionsAvoiding Thread LeakageSymmetric Concurrency CombinatorsAdding a Functor InstanceSummary: The Async APIChapter 12 : Concurrent Network ServersA Trivial ServerExtending the Simple Server with StateA Chat ServerChapter 13 : Parallel Programming Using ThreadsHow to Achieve Parallelism with ConcurrencyExample: Searching for FilesChapter 14 : Distributed ProgrammingThe Distributed-Process Family of PackagesDistributed Concurrency or Parallelism?A First Example: PingsMulti-Node PingTyped ChannelsHandling FailureA Distributed Chat ServerExercise: A Distributed Key-Value StoreChapter 15 : Debugging, Tuning, and Interfacing with Foreign CodeDebugging Concurrent ProgramsTuning Concurrent (and Parallel) ProgramsConcurrency and the Foreign Function InterfaceColophon

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