Parallel programming
The goal of any parallel programming is to reduce the whole latency time of the operation by using all the available local resources, in terms of CPU computational power.
Two definitions of parallelism actually exist. Task parallelism happens when we execute multiple jobs all together, such as saving data against multiple database servers.
Data parallelism, instead, happens when we split a huge dataset elaboration across all available CPUs, like when we have to execute some CPU demanding method against a huge amount of objects in the memory, like hashing data.
In the .NET framework, we have the ability to use both parallel kinds. Despite that, the most widely used kind of parallelism within the .NET framework's programming is data parallelism, thanks to PLINQ being so easy to use.
The following table shows the comparison between Task parallelism and Data parallelism:
| Â |
Task parallelism |
Data parallelism |
|---|---|---|
|
What does it parallelize? |
Parallelizable functions | ... |
