Levels of parallelism ?
Parallelism refers to the ability of a computing system to execute multiple tasks or processes simultaneously. There are several different levels of parallelism that can be exploited in computing systems, including:
Instruction-level parallelism (ILP): ILP involves the execution of multiple instructions at the same time within a single processor. This is typically achieved through techniques such as pipelining, where different stages of an instruction’s execution are overlapped to improve performance.
Thread-level parallelism (TLP): TLP involves the use of multiple threads or processes to perform different tasks simultaneously within a single processor or across multiple processors. This is typically achieved through techniques such as multi-threading or multi-processing.
Data-level parallelism (DLP): DLP involves the simultaneous execution of the same operation on different sets of data. This is typically achieved through techniques such as SIMD (single instruction, multiple data) processing, where a single instruction is executed on multiple data elements at the same time.
Task-level parallelism (TALP): TALP involves the division of a large task into smaller sub-tasks that can be executed in parallel by different processors or threads. This is typically achieved through techniques such as task parallelism or data parallelism.
Bit-level parallelism (BLP): BLP involves the processing of multiple bits of data simultaneously. This is typically achieved through techniques such as parallel adders and multipliers.
Overall, the different levels of parallelism can be combined to achieve even greater performance gains in computing systems. The optimal level(s) of parallelism to use will depend on the specific application and the underlying hardware architecture.