Parallel Computing MCQ Test: Parallel Computing MCQs - Practice Questions
1. What is Amdahl's Law used for in parallel computing?
2. In parallel computing, what does granularity refer to?
3. Which parallel programming concept involves dividing a program into small, independent threads of execution?
4. Which type of parallelism involves dividing a task into smaller, independent subtasks that can be performed concurrently?
5. Discuss the challenges and solutions associated with mitigating race conditions in parallel programming.
6. What are the main challenges in designing efficient parallel algorithms?
7. What is a race condition in parallel programming?
8. What is the role of parallel reduction operations, and how do they contribute to the efficiency of parallel algorithms?
9. What is the purpose of parallel prefix sum in parallel algorithms?
10. Which programming language is commonly used for parallel computing?
11. Evaluate the impact of Amdahl's Law on the design and scalability of parallel algorithms, considering different scenarios and levels of parallelization.
12. Describe the challenges and advantages of achieving load balancing in complex parallel computing environments.
13. Explain the concept of granularity and its impact on performance, with a focus on achieving optimal performance in parallel algorithms.
14. In parallel computing, what does Amdahl's Law express?
15. Which parallel algorithm is commonly used for searching a key in a large dataset?
16. Discuss the advantages and challenges of integrating GPU parallel computing in scientific simulations and data-intensive applications.
17. What is the purpose of a parallel reduction operation in parallel computing?
18. Examine the role of parallel computing in optimizing scientific simulations, highlighting specific scenarios where it provides significant advantages.
19. Explain how task parallelism differs from data parallelism in the context of parallel computing, and provide examples of when each is more suitable.
20. Examine the role of parallel algorithms in addressing challenges related to big data processing and analytics, highlighting key techniques and optimizations.