Parallel computing is a type of computation where multiple calculations or processes are carried out simultaneously to solve a problem more efficiently than sequential processing. By dividing tasks across multiple processors, cores, or computers, it leverages concurrency to handle large-scale data and complex computations.
Key Concepts:
– Concurrency vs. Parallelism: Concurrency involves managing multiple tasks that may not run at the same time, while parallelism executes tasks simultaneously.
– Scalability: Systems can scale by adding more resources, such as processors, to improve performance.
– Synchronization: Processes must coordinate to avoid conflicts, often using mechanisms like locks, semaphores, or message passing.
Types of Parallel Computing:
– Shared Memory Parallelism: Multiple processors access a common memory space, as seen in multicore CPUs. Examples include OpenMP for thread-based programming.
– Distributed Memory Parallelism: Each processor has its own memory, communicating via networks. MPI (Message Passing Interface) is a common framework.
– GPU Computing: Graphics processing units (GPUs) handle thousands of threads for tasks like machine learning and simulations, using libraries like CUDA or OpenCL.
– Cluster and Grid Computing: Involves networks of computers working together, often for large-scale applications.
How It Works:
Parallel computing breaks down a problem into independent subtasks that can be executed at the same time. For instance, in a matrix multiplication, elements can be calculated concurrently. Algorithms must be designed to minimize dependencies and maximize workload distribution.
Applications:
– Scientific Simulations: Weather forecasting, molecular dynamics, and climate modeling.
– Big Data Processing: Hadoop and Spark for analyzing massive datasets.
– Artificial Intelligence: Training neural networks with frameworks like TensorFlow.
– Real-Time Systems: Video rendering, financial modeling, and autonomous vehicles.
Advantages:
– Speed: Reduces computation time by processing tasks in parallel.
– Efficiency: Utilizes hardware resources more effectively.
– Cost-Effectiveness: Allows solving larger problems without proportional increases in hardware costs.
Challenges:
– Programming Complexity: Debugging and optimizing parallel code can be difficult due to issues like race conditions and deadlocks.
– Overhead: Communication between processes adds latency.
– Scalability Limits: Not all problems parallelize well, and hardware constraints can bottleneck performance.
Future Trends:
Advancements in quantum computing, edge computing, and heterogeneous architectures (combining CPUs, GPUs, and FPGAs) are expanding parallel computing’s capabilities. As data volumes grow, parallel techniques will become essential for real-time analytics and AI-driven innovations.
Table of Contents
- Part 1: OnlineExamMaker – Generate and Share Parallel Computing Quiz with AI Automatically
- Part 2: 20 Parallel Computing Quiz Questions & Answers
- Part 3: OnlineExamMaker AI Question Generator: Generate Questions for Any Topic
Part 1: OnlineExamMaker – Generate and Share Parallel Computing Quiz with AI Automatically
The quickest way to assess the Parallel Computing knowledge of candidates is using an AI assessment platform like OnlineExamMaker. With OnlineExamMaker AI Question Generator, you are able to input content—like text, documents, or topics—and then automatically generate questions in various formats (multiple-choice, true/false, short answer). Its AI Exam Grader can automatically grade the exam and generate insightful reports after your candidate submit the assessment.
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Part 2: 20 Parallel Computing Quiz Questions & Answers
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Question 1:
What is the primary goal of parallel computing?
A) To reduce the size of hardware
B) To execute tasks simultaneously using multiple processors
C) To minimize energy consumption
D) To simplify software development
Answer: B
Explanation: Parallel computing divides a large problem into smaller tasks that can be processed at the same time by multiple processors, improving overall computation speed.
Question 2:
Which of the following is an example of Flynn’s taxonomy?
A) Sequential processing
B) SISD (Single Instruction, Single Data)
C) Linear programming
D) Binary execution
Answer: B
Explanation: Flynn’s taxonomy classifies computer architectures, and SISD represents a single instruction operating on a single data stream, which is a basic form of sequential computing.
Question 3:
In parallel computing, what does Amdahl’s Law help predict?
A) The maximum theoretical speedup
B) The cost of hardware
C) The number of processors needed
D) The memory usage
Answer: A
Explanation: Amdahl’s Law calculates the potential speedup of a parallel system based on the proportion of a program that can be parallelized, highlighting limits due to serial portions.
Question 4:
What is a race condition in parallel programming?
A) When two processes access the same data without synchronization
B) When a program runs out of memory
C) When tasks are executed in a fixed order
D) When processors are idle
Answer: A
Explanation: A race condition occurs when the outcome of a program depends on the unpredictable order of execution of concurrent threads, often due to unsynchronized access to shared resources.
Question 5:
Which programming model is commonly used for shared-memory parallel computing?
A) MPI (Message Passing Interface)
B) OpenMP
C) Sequential C++
D) GPU shaders
Answer: B
Explanation: OpenMP is a standard for parallel programming in shared-memory environments, allowing developers to add parallelism to existing code with directives.
Question 6:
What is data parallelism?
A) Dividing tasks into independent subtasks
B) Performing the same operation on multiple data elements simultaneously
C) Sharing memory across networks
D) Executing programs on a single core
Answer: B
Explanation: Data parallelism involves applying the same operation to different parts of a dataset at the same time, which is efficient for tasks like matrix operations.
Question 7:
In a multiprocessor system, what is a deadlock?
A) A situation where processes wait indefinitely for resources
B) A fast execution of code
C) Overloading of memory
D) Synchronization of threads
Answer: A
Explanation: Deadlock occurs when two or more processes are unable to proceed because each is waiting for the other to release a resource, halting progress in parallel systems.
Question 8:
Which metric measures how effectively a parallel system uses its resources?
A) Speedup
B) Efficiency
C) Latency
D) Bandwidth
Answer: B
Explanation: Efficiency is calculated as speedup divided by the number of processors, indicating how well the parallel system utilizes additional resources without overhead.
Question 9:
What is SIMD (Single Instruction, Multiple Data)?
A) A type of sequential processor
B) An architecture where one instruction is executed on multiple data points simultaneously
C) A memory management technique
D) A networking protocol
Answer: B
Explanation: SIMD is a parallel processing architecture used in GPUs and vector processors, where the same instruction is applied to multiple data elements in parallel.
Question 10:
How does task parallelism differ from data parallelism?
A) Task parallelism focuses on dividing data sets
B) Task parallelism involves independent tasks running concurrently
C) Task parallelism requires shared memory
D) Task parallelism is only for single-core systems
Answer: B
Explanation: Task parallelism assigns different independent tasks to processors, allowing them to run simultaneously, unlike data parallelism which operates on the same operation across data.
Question 11:
What is the role of barriers in parallel programming?
A) To synchronize threads at specific points
B) To increase processing speed
C) To allocate memory
D) To debug code
Answer: A
Explanation: Barriers ensure that all threads reach a certain point before any proceed further, preventing issues like data inconsistencies in parallel execution.
Question 12:
In distributed memory systems, how do processes communicate?
A) Through shared variables
B) Via message passing
C) By direct memory access
D) Using sequential calls
Answer: B
Explanation: In distributed memory architectures, processes on different nodes communicate by sending messages, as they do not share a common memory space.
Question 13:
What does GPU acceleration provide in parallel computing?
A) Slower processing for complex tasks
B) Massive parallel processing for graphics and computations
C) Reduced memory usage
D) Single-threaded execution
Answer: B
Explanation: GPUs are designed for parallel workloads, with thousands of cores that can handle multiple threads simultaneously, making them ideal for tasks like rendering and AI training.
Question 14:
Which factor limits parallelism according to Amdahl’s Law?
A) The parallelizable portion of the code
B) The serial portion of the code
C) Network speed
D) Processor clock rate
Answer: B
Explanation: Amdahl’s Law shows that the non-parallelizable (serial) parts of a program limit the overall speedup, as they must be executed sequentially.
Question 15:
What is thread-level parallelism?
A) Executing multiple threads within a single process
B) Running programs on multiple machines
C) Processing data in a linear fashion
D) Using only hardware interrupts
Answer: A
Explanation: Thread-level parallelism involves creating and managing multiple threads of execution within the same program, allowing concurrent operations on a multicore processor.
Question 16:
In parallel algorithms, what is granularity?
A) The size of data elements
B) The amount of work associated with a parallel task
C) The number of processors
D) The memory capacity
Answer: B
Explanation: Granularity refers to the ratio of computation to communication in a parallel task; fine-grained tasks have more overhead, while coarse-grained ones are more efficient.
Question 17:
What is the purpose of load balancing in parallel systems?
A) To evenly distribute workload across processors
B) To minimize data transfer
C) To increase task dependency
D) To reduce processor speed
Answer: A
Explanation: Load balancing ensures that no single processor is overwhelmed, optimizing resource utilization and reducing idle time in parallel computing.
Question 18:
Which synchronization primitive is used to protect critical sections?
A) Mutex
B) Variable declaration
C) Loop unrolling
D) Function calls
Answer: A
Explanation: A mutex (mutual exclusion) lock prevents multiple threads from accessing shared resources simultaneously, avoiding conflicts in critical sections.
Question 19:
What is MIMD (Multiple Instruction, Multiple Data)?
A) Executing the same instruction on all data
B) Allowing multiple instructions on multiple data streams independently
C) A single-core architecture
D) Sequential data processing
Answer: B
Explanation: MIMD architecture supports different instructions on different data streams simultaneously, commonly used in multicore CPUs and distributed systems.
Question 20:
How does cache coherence affect parallel computing?
A) It ensures that shared data in caches is consistent across processors
B) It speeds up sequential execution
C) It reduces the need for memory
D) It handles network communication
Answer: A
Explanation: Cache coherence maintains a consistent view of shared data in multiprocessor systems, preventing errors from outdated cache values during parallel operations.
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