Task parallelism: Difference between revisions

Revision as of 14:35, 13 August 2017

Revision as of 16:58, 23 October 2017

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==Description==

In a multiprocessor system, task parallelism is achieved when each processor executes a different thread (or process) on the same or different data. The threads may execute the same or different code. In the general case, different execution threads communicate with one another as they work, but is not a requirement. Communication usually takes place by passing data from one thread to the next as part of a [[workflow]].<ref>{{cite book|last1=Quinn|first1=Michael J.|title=Parallel programming in C with MPI and openMP|date=2007|publisher=Tata McGraw-Hill Pub.|___location=New Delhi|isbn=0070582017|edition=Tata McGraw-Hill ed.}}</ref>

As a simple example, if a system is running code on a 2-processor system ([[CPU]]s "a" & "b") in a [[wikt:parallel|parallel]] environment and we wish to do tasks "A" and "B", it is possible to tell CPU "a" to do task "A" and CPU "b" to do task "B" simultaneously, thereby reducing the [[Run time (program lifecycle phase)|run time]] of the execution. The tasks can be assigned using [[Conditional (programming)|conditional statement]]s as described below.

Task parallelism emphasizes the distributed (parallelized) nature of the processing (i.e. threads), as opposed to the data ([[data parallelism]]). Most real programs fall somewhere on a continuum between task parallelism and data parallelism.<ref>{{cite web|last1=Hicks|first1=Michael|title=Concurrency Basics|url=http://www.cs.umd.edu/class/fall2013/cmsc433/lectures/concurrency-basics.pdf|website=University of Maryland: Department of Computer Science|accessdate=8 May 2017}}</ref>

'''Thread-level parallelism''' ('''TLP''') is the [[Parallel computing|parallelism]] inherent in an application that runs multiple [[Thread (computer science)|threads]] at once. This type of parallelism is found largely in applications written for commercial [[Server (computing)|server]]s such as databases. By running many threads at once, these applications are able to tolerate the high amounts of I/O and memory system latency their workloads can incur - while one thread is delayed waiting for a memory or disk access, other threads can do useful work.

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* C++ (Open Source/Apache 2.0): [[RaftLib]]

* C, C++, Objective-C (Apple): [[Grand Central Dispatch]]

* D: [[Task_Task (computing)|tasks]] and [[Fiber_Fiber (computer_sciencecomputer science)|fibers]]

* Go: [[goroutine]]s

* Java: [[Java concurrency]]