1. Which of the following is a key objective of compiler optimizations for parallelism?
A) Minimizing the number of function calls
B) Reducing the size of the source code
C) Maximizing the utilization of available processors
D) Ensuring code portability across platforms
Answer: C) Maximizing the utilization of available processors
2. What is the primary challenge for compilers when performing optimizations for parallelism?
A) Minimizing code complexity
B) Ensuring correct and efficient parallel execution
C) Reducing the number of instructions in the program
D) Converting high-level code to assembly
Answer: B) Ensuring correct and efficient parallel execution
3. Which optimization technique is commonly used by compilers to allow independent execution of instructions across multiple processors?
A) Loop unrolling
B) Instruction pipelining
C) Loop parallelization
D) Constant folding
Answer: C) Loop parallelization
4. Which of the following parallelism techniques is aimed at minimizing the overhead of parallel execution?
A) Vectorization
B) Data parallelism
C) Task parallelism
D) Instruction-level parallelism
Answer: B) Data parallelism
5. Which of the following is a form of parallelism where the compiler splits loops into independent tasks that can run concurrently?
A) Task parallelism
B) Data parallelism
C) Loop-level parallelism
D) Function-level parallelism
Answer: C) Loop-level parallelism
6. In compiler optimizations for parallelism, what does “loop unrolling” help achieve?
A) Reduces the number of loop iterations by manually expanding the loop body
B) Reduces the size of the program
C) Increases the number of data dependencies
D) Avoids parallel execution by simplifying the loop structure
Answer: A) Reduces the number of loop iterations by manually expanding the loop body
7. What is the purpose of data dependence analysis in the context of parallelism optimizations?
A) To determine the feasibility of parallelizing loops
B) To identify sections of code that cannot be optimized
C) To increase the number of instructions
D) To minimize the memory usage during execution
Answer: A) To determine the feasibility of parallelizing loops
8. Which optimization technique allows compilers to automatically parallelize code by identifying independent tasks or instructions that can be executed in parallel?
A) Instruction reordering
B) Automatic parallelization
C) Constant propagation
D) Code motion
Answer: B) Automatic parallelization
9. Which of the following is an example of task parallelism in compiler optimizations?
A) Performing arithmetic operations simultaneously across multiple data elements
B) Dividing a program into smaller tasks that can be executed on separate processors
C) Looping through arrays in parallel
D) Reordering instructions to reduce execution time
Answer: B) Dividing a program into smaller tasks that can be executed on separate processors
10. What role does SIMD (Single Instruction, Multiple Data) play in compiler optimizations for parallelism?
A) It optimizes parallel execution of the same instruction on different processors
B) It enables execution of multiple instructions in a single cycle
C) It allows multiple data elements to be processed with a single instruction
D) It minimizes the use of memory and storage
Answer: C) It allows multiple data elements to be processed with a single instruction
11. Which of the following is a type of parallelism that involves breaking down large data structures (e.g., arrays) into smaller sections to be processed simultaneously?
A) Task-level parallelism
B) Instruction-level parallelism
C) Data-level parallelism
D) Control-level parallelism
Answer: C) Data-level parallelism
12. What is a barrier synchronization in the context of parallelism optimization?
A) A mechanism that ensures all parallel tasks have completed before proceeding
B) A technique to reduce the size of the data in memory
C) A method to check for errors in parallelized code
D) A technique that maximizes the speed of parallel execution
Answer: A) A mechanism that ensures all parallel tasks have completed before proceeding
13. Which of the following parallelism optimizations helps compilers reduce the number of threads by merging smaller tasks into one?
A) Task fusion
B) Loop unrolling
C) Data alignment
D) Instruction pipelining
Answer: A) Task fusion
14. What is speculative execution in the context of compiler optimizations for parallelism?
A) Executing tasks or instructions before their actual need to optimize performance
B) A method of dividing code into tasks based on data dependencies
C) A form of task parallelism with synchronization barriers
D) An approach to increase the memory footprint of the program
Answer: A) Executing tasks or instructions before their actual need to optimize performance
15. What is the purpose of vectorization in compiler optimizations?
A) To divide the program into tasks for parallel execution
B) To convert scalar operations into vector operations for parallel execution
C) To reduce the size of memory used
D) To increase the number of function calls
Answer: B) To convert scalar operations into vector operations for parallel execution