Parallel Computing MCQ Test: Parallel Computing MCQs - Practice Questions
1. Evaluate the role of parallel computing in the field of artificial intelligence, emphasizing its contributions to training and inference processes in machine learning models.
2. In parallel computing, what does granularity refer to?
3. In the context of parallel algorithms, what is meant by granularity and how does it impact performance?
4. What is parallel computing?
5. Discuss the challenges and solutions associated with mitigating race conditions in parallel programming.
6. What is the significance of parallel for-loops in parallel programming, and how do they contribute to optimizing computational speed?
7. What is Amdahl's Law used for in parallel computing?
8. Discuss the concept of deadlock in parallel computing and provide strategies for preventing and resolving deadlock situations.
9. What is a race condition in parallel programming?
10. Describe the role of parallel computing in optimizing database queries and processing large datasets, emphasizing efficiency and scalability.
11. Discuss the challenges and advantages of achieving load balancing in complex parallel computing environments.
12. Define a parallel algorithm.
13. What is the primary challenge of achieving load balancing in parallel computing?
14. What is parallel processing?
15. Evaluate the impact of Amdahl's Law on the design and scalability of parallel algorithms, considering different scenarios and levels of parallelization.
16. What is the purpose of parallel prefix sum in parallel algorithms?
17. In parallel computing, what is speculative execution?
18. Describe the difference between task parallelism and data parallelism in parallel computing.
19. Which parallel computing paradigm focuses on dividing a problem into smaller, identical tasks that can be solved independently?
20. What is the significance of parallel computing in real-time systems?
21. What is the role of a parallel computing scheduler?
22. What is the role of a parallel computing cache?
23. Examine the role of parallel algorithms in addressing challenges related to big data processing and analytics, highlighting key techniques and optimizations.
24. Describe the challenges and solutions associated with achieving fault tolerance in large-scale parallel computing systems, with a focus on real-world applications.
25. In parallel computing, what does load balancing refer to?
26. Which programming language is commonly used for parallel computing?
27. Why is load balancing important in parallel computing?
28. What is the purpose of a barrier synchronization in parallel programming?
29. Evaluate the role of parallel computing schedulers in managing complex parallel processes and optimizing overall system performance.
30. Which parallel programming concept involves dividing a program into small, independent threads of execution?
31. What is the role of parallel reduction operations, and how do they contribute to the efficiency of parallel algorithms?
32. Describe the challenges and advantages of achieving load balancing in complex parallel computing environments.
33. Explain the concept of speculative execution in the context of high-performance parallel computing.
34. In parallel computing, what is a deadlock?
35. Compare and contrast SIMD and MIMD parallel computing architectures, highlighting their respective strengths and weaknesses.
36. Explain how task parallelism differs from data parallelism in the context of parallel computing, and provide examples of when each is more suitable.
37. Examine the impact of task granularity on load balancing in parallel computing, discussing strategies for achieving optimal distribution of computational tasks.
38. Which parallel algorithm is commonly used for searching a key in a large dataset?
39. Explain Amdahl's Law and its significance in parallel computing.
40. Examine the role of parallel computing in optimizing scientific simulations, highlighting specific scenarios where it provides significant advantages.