Task Parallel Library (TPL) Higher level abstraction API for concurrency in C# Task parallel library

Data parallelism vs. task parallelism Two ways to partition a “problem” into tasks Data parallelism (master-slave) Task parallelism (pipelining) Partition the data, and give each task a part of the data set. Works well if data are independent Master Coordinates the work ONE master Slaves do the work Many slaves Partition the algorithm, and give each task a part the algorithm. Works well if each part of the algorithm takes about the same time to execute Task parallel library

Example: Password cracking, dictionary attack A brute force algorithm We need lots of computer resources to make it run fast We have a list of usernames + encrypted passwords We know which encryption algorithm was used There is no known decryption algorithm We want to find (some of) the passwords in clear text. We assume that (some) users have a password that is present in a dictionary Or is a variation of a word from a dictionary General algorithm Read a word from the dictionary Make a lot of variations from the word, like 123word, word22, Word, etc. Encrypt all the variations Compare each encrypted word to all the encrypted passwords from the password file If there is a match, we have found a password Task parallel library

Example: Password cracking, dictionary attack Data parallelism Task parallelism (pipelining) The dictionary is divided into a number of sub-sets. Each sub-set is give to a task that performs all steps of the algorithm No communication between tasks The algorithm is divided into a number of steps. Each task executes a step in the algorithm Each step sends data to the next step Lots of communication between tasks Task parallel library

Task Parallel Library (TPL) The “Task Parallel Library (TPL)” is part of the C# API Namespace System.Threading.Task Some interesting TPL classes Task For Task parallelism We have used Task.run(Action action) to ask the thread pool to run a thread But there is much more to the Task class … Parallel For Data parallelism (and Task parallelism) Task parallel library

Class Task, some methods and properties Task task = Task.run(Action action) Action is a void method, no parameters: void M() { … } Task task = new Task(Action action) taskObject.start() Runs the previously created taskObject in the thread pool Properties IsCompleted, IsFaulted, IsCancelled Property Id Task.CurrentId Task parallel library

Task with return values Task<TResult> task = Task.run( Func<TResult> function) Runs the action in the thread pool Func (function) is a method that returns TResult, no parameters Task<TResult> task = new Task(Func<TResult> function) Constructor The Result property Contains the result of the task Blocks until the result is ready Task parallel library

The class Parallel Very high level of abstraction Level 1: Thread Level 2: Task Level 3: Parallel Parallel.Invoke(action, action, …, action) Actions are invoked in parallel. Returns when the last action has finished Task parallelism: Different tasks run in parallel. Tasks are generally not similar Usually a few (large) tasks Efficient when actions need almost same amount of time to complete Example: Gaston Hillar: Professional Parallel Programming with C#, example 2_3 Task parallel library

Parallel for loop Parallel.For(fromValue, toValue, Action<int>) Similar to an ordinary for loop – but parallel, of course. Action is invoked for each value between fromValue (inclusive) and toValue (not inclusive) Data parallelism: Each task is given a part of the data set . Tasks are similar. Usually a lot of (small) tasks. Efficient even if the tasks needs different times to complete. Example: Gaston Hillar: Professional Parallel Programming with C# example 2_5 Task parallel library

Parallel foreach loop Parallel.ForEach(IEnumrable list, Action) Similar to ordinary foreach loop – but parallel. Action is invoked for each element in the IEnumerable (think “list” or “array”) Example: Gaston Hillar: Professional Parallel Programming with C# example 2_11 Often used with a Partitioner Partitioner divides a range of data into a number of sub-ranges Task parallel library

References and further readings MSDN Task Parallel Library (TPL) http://msdn.microsoft.com/en-us/library/dd460717(v=vs.110).aspx Joseph Albahari Threading in C#, Part 5: Parallel Programming, The Parallel Class http://www.albahari.com/threading/part5.aspx#_The_Parallel_Class Task Parallelism http://www.albahari.com/threading/part5.aspx#_Task_Parallelism Gaston C. Hillar Professional Parallel Programming with C#, Master Parallel Extensions with .NET 4, Wrox/Wiley 2011 Chapter 2: Imperative Data Parallelism, page 29-72 Chapter 3: Imperative Task Parallelism, page 73 – 102. Task parallel library