Task Level Parallelism

What is data parallelism in task level parallelism?

• Data parallelism is the execution of different tasks on multiple data elements.

• Data parallelism is the execution of a single task on a single data element.

• Data parallelism in task level parallelism is the simultaneous execution of the same task on multiple data elements.

• Data parallelism involves sequential execution of tasks on multiple data elements.

Explain the concept of instruction level parallelism.

• Instruction level parallelism allows multiple instructions to be executed at the same time within a single pipeline, improving performance.

• Instruction level parallelism involves executing multiple instructions simultaneously in different pipelines.

• Instruction level parallelism is a technique that focuses on optimizing memory access rather than instruction execution.

• Instruction level parallelism refers to executing instructions one after the other in a sequential manner.

How does thread level parallelism differ from data parallelism?

• Thread level parallelism and data parallelism are essentially the same concept.

• Data parallelism executes multiple threads within a single process.

• Thread level parallelism executes multiple threads within a single process, while data parallelism performs the same operation on multiple data pieces in parallel.

• Thread level parallelism executes the same operation on multiple data pieces in parallel.

Give an example of data parallelism in a real-world scenario.

• Image processing

• Weather forecasting

• Social media analytics

• Online shopping recommendations

What are the benefits of instruction level parallelism?

• Decreased performance and efficiency

• Increased cost and complexity

• No impact on performance

• Improved performance and efficiency

Discuss the challenges of implementing thread level parallelism.

• Instruction set architecture

• Memory allocation

• Data dependencies, synchronization, race conditions, workload distribution

• Hardware dependencies

How can data parallelism improve performance in computing tasks?

• Data parallelism has no impact on performance in computing tasks.

• Data parallelism improves performance by executing multiple sub-tasks simultaneously on different processing units.

• Data parallelism slows down performance by overloading processing units with multiple tasks.

• Data parallelism improves performance by executing tasks sequentially on a single processing unit.

What techniques are used to achieve instruction level parallelism?

• Loop Unrolling

• Pipelining, Superscalar Execution, Out-of-Order Execution, Speculative Execution

• Vectorization

• Register Renaming

Compare and contrast data parallelism and thread level parallelism.

• Thread level parallelism distributes data across multiple processing units.

• Data parallelism involves executing multiple threads within a single process.

• Data parallelism distributes data across multiple processing units, while thread level parallelism involves executing multiple threads within a single process.

• Data parallelism and thread level parallelism are the same concept.

What role does SIMD play in data parallelism?

• SIMD enables a single instruction to operate on multiple data elements simultaneously, enhancing data parallelism.

• SIMD allows for sequential processing of data elements, reducing data parallelism.

• SIMD can only handle one data element at a time, limiting data parallelism.

• SIMD has no impact on data parallelism, it only affects single data element operations.