Parallel Computing Questions
Task parallelism and data parallelism are two different approaches to achieving parallelism in computing.
Task parallelism involves dividing a problem into multiple independent tasks that can be executed concurrently. Each task is assigned to a separate processing unit or thread, and they can execute simultaneously. Task parallelism is suitable for problems where different tasks can be executed independently and do not require much communication or coordination between them.
On the other hand, data parallelism involves dividing a large data set into smaller chunks and processing them simultaneously using multiple processing units or threads. Each processing unit operates on a different portion of the data, performing the same operations or computations. Data parallelism is suitable for problems where the same operation needs to be performed on different parts of the data, and there is a need for communication or coordination between the processing units to synchronize their operations.
In summary, task parallelism focuses on dividing a problem into independent tasks, while data parallelism focuses on dividing a large data set into smaller chunks for simultaneous processing.