Parallel Computing MCQ Test: Parallel Computing MCQs - Practice Questions
1. Explain the concept of speculative execution in the context of high-performance parallel computing.
2. Explain the concept of granularity and its impact on performance, with a focus on achieving optimal performance in parallel algorithms.
3. What is a race condition in parallel computing?
4. Evaluate the impact of Amdahl's Law on the design and scalability of parallel algorithms, considering different scenarios and levels of parallelization.
5. Which parallel computing paradigm focuses on dividing a problem into smaller, identical tasks that can be solved independently?
6. What is the role of a parallel computing scheduler?
7. What is the role of barriers in parallel computing, and how do they impact performance?
8. Which parallel computing architecture is characterized by processors having their own local memory and connected through a network?
9. What is parallel computing?
10. Discuss the concept of parallel computing granularity and its impact on scalability and efficiency, providing examples from real-world applications.
11. Examine the role of parallel algorithms in addressing challenges related to big data processing and analytics, highlighting key techniques and optimizations.
12. In parallel computing, what is a deadlock?
13. Examine the impact of Amdahl's Law on the scalability of parallel computing systems, especially in the presence of communication overhead.
14. Why is load balancing important in parallel computing?
15. Explain the concept of data parallelism.
16. What is Amdahl's Law used for in parallel computing?
17. Evaluate the role of parallel computing in the field of artificial intelligence, emphasizing its contributions to training and inference processes in machine learning models.
18. In parallel computing, what does granularity refer to?
19. Which parallel algorithm is commonly used for sorting large datasets?
20. Explain Amdahl's Law and its significance in parallel computing.
21. Which parallel algorithm is commonly used for searching a key in a large dataset?
22. What role does the parallel computing cache play in optimizing the performance of parallel algorithms?
23. Describe the challenges and solutions associated with achieving fault tolerance in large-scale parallel computing systems, with a focus on real-world applications.
24. What is the purpose of a parallel reduction operation in parallel computing?
25. Evaluate the role of parallel computing schedulers in managing complex parallel processes and optimizing overall system performance.
26. What is the primary goal of parallel computing?
27. What is the purpose of parallel for-loops in parallel programming?
28. What are the main challenges in designing efficient parallel algorithms?
29. Examine the impact of task granularity on load balancing in parallel computing, discussing strategies for achieving optimal distribution of computational tasks.
30. Explain how task parallelism differs from data parallelism in the context of parallel computing, and provide examples of when each is more suitable.