A range of programming languages are designed with implicit capabilities, primarily focusing on implicit parallelism. This design approach allows the language's compiler or runtime system to automatically manage the concurrent execution of code, significantly simplifying the development of parallel applications.
Understanding Implicit Parallelism
Implicit parallelism is a programming paradigm where the programmer does not explicitly write code for parallel execution. Instead, the language environment automatically identifies and exploits opportunities for parallel processing. This contrasts sharply with explicit parallelism, where developers must use specific constructs like threads, locks, or message passing interfaces to control concurrency.
The core advantage of implicit parallelism lies in its ability to abstract away the complexities of parallel hardware and synchronization. Developers can focus on the problem's logic, while the system handles the intricate details of distributing tasks, managing data dependencies, and synchronizing operations across multiple processing units. This approach aims to make parallel programming more accessible and less error-prone.
Programming Languages with Implicit Parallelism
Several languages have been developed or extended to support implicit parallelism, offering varied approaches to achieve automatic concurrent execution. These languages are instrumental in areas requiring high computational throughput, such as scientific computing, data analysis, and simulation.
Here is a list of programming languages known for their implicit parallelism features:
- Axum
- BMDFM
- HPF (High Performance Fortran)
- Id
- LabVIEW
- MATLAB M-code
- NESL
- SaC (Single Assignment C)
- SISAL (Streams and Iteration in a Single Assignment Language)
- ZPL
- pH
These languages demonstrate how different paradigms, from array-based to functional and dataflow models, can leverage implicit parallelism to enhance performance without burdening the programmer with explicit parallel programming constructs.
