Parallel Programming Fundamentals
Medium-difficulty multiple-choice quiz on key behaviors, patterns, and trade-offs in parallel programming.
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Quiz Questions & Answers
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Question 1: What is Amdahl's Law primarily used to estimate in parallel computing?
The maximum theoretical speedup given a fixed serial fraction
How much memory each thread requires
The exact runtime on any number of processors
The energy efficiency of multicore chips
Question 2: Which strategy best reduces contention on a shared resource in a multithreaded program?
Use busy-waiting loops around the critical section
Increase the number of locks protecting the same data
Partition the data so threads work on disjoint subsets
Always use a single global lock for simplicity
Question 3: Which pattern helps hide memory latency by overlapping computation and I/O?
Reducing the number of threads to one
Serializing all accesses through a single thread
Pipelining stages so different threads handle successive tasks
Allocating more stack memory per thread
Question 4: In a shared-memory program, which approach best avoids subtle races when updating a counter by many threads?
Let each thread write its value without synchronization
Have threads sleep briefly before writing
Use atomic increment operations provided by the platform
Use separate counters and ignore aggregation
Question 5: Which mindset change most helps when converting a sequential algorithm to parallel?
Think in terms of independent units of work and dependencies
Always maximize thread count regardless of workload
Rely on the runtime to automatically parallelize everything
Eliminate all memory allocations before parallelizing
Question 6: What is the main trade-off when using fine-grained locking instead of coarse-grained locking?
It eliminates the need for atomic operations
Lower contention but higher complexity and overhead
Simpler code and fewer locks are required
Guaranteed deadlock freedom with no performance cost
Question 7: Which approach most effectively scales across many cores for data-parallel numeric work?
Favor deep recursion to express parallelism
Use bulk-synchronous operations with large, contiguous data chunks
Always prefer mutexes over array-based algorithms
Assign one element at a time to random threads
Question 8: Which statement best busts the myth: 'More CPU cores always give faster program performance'?
Performance depends on parallelizable work, synchronization, and memory bandwidth
Any program will always scale linearly with core count
Hyperthreading guarantees double the throughput
Cores are irrelevant if you have a fast single core