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

2. 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

3. 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

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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

11. References and further readings
MSDN Task Parallel Library (TPL)
Joseph Albahari
Threading in C#, Part 5: Parallel Programming,
The Parallel Class
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