Parallel efficiency

Definition

Parallel efficiency measures how effectively a parallel computing system utilizes its resources when solving a problem. It compares the speedup achieved by using multiple processors to the ideal speedup, which is the number of processors used. High parallel efficiency indicates that most of the computational resources are being effectively utilized, while low efficiency suggests that overhead or communication costs are hindering performance.

5 Must Know Facts For Your Next Test

1. Parallel efficiency is expressed as a percentage, calculated by dividing the actual speedup by the ideal speedup.
2. An ideal parallel efficiency of 100% means that all processors are working perfectly without any idle time or communication delays.
3. Factors like communication overhead, synchronization delays, and workload imbalance can decrease parallel efficiency.
4. To improve parallel efficiency, it is important to implement effective load balancing and minimize inter-process communication.
5. In domain decomposition methods, achieving high parallel efficiency is crucial for effective performance, as the domain must be divided in a way that minimizes communication between processors.

Review Questions

• How does parallel efficiency relate to the performance of computational methods?
  • Parallel efficiency directly impacts the performance of computational methods by determining how effectively multiple processors work together. If parallel efficiency is high, it means that the computational resources are being utilized efficiently, leading to faster execution times. Conversely, low parallel efficiency can indicate significant overheads or inefficiencies, reducing the overall performance of methods like domain decomposition.
• Discuss how load balancing affects parallel efficiency in computational simulations.
  • Load balancing plays a critical role in enhancing parallel efficiency because it ensures that all processors share workloads evenly. If one processor is overloaded while others remain idle, overall performance suffers due to underutilization of resources. Proper load balancing minimizes idle time and optimizes resource usage, thus improving parallel efficiency and speeding up simulations that rely on domain decomposition techniques.
• Evaluate the impact of communication overhead on parallel efficiency in domain decomposition methods.
  • Communication overhead significantly affects parallel efficiency in domain decomposition methods by introducing delays that can hinder overall performance. When solving complex problems, processors must frequently exchange information about their respective subdomains. If this communication is not managed efficiently, it can lead to increased wait times and reduced speedup. Evaluating and minimizing this overhead through careful design and implementation strategies is essential for maximizing parallel efficiency and achieving optimal performance in distributed computing environments.

Related terms

Speedup:

The ratio of the time taken to complete a task on a single processor to the time taken on multiple processors.

Scalability:

The ability of a system to handle an increasing workload or to be easily expanded by adding resources without a significant drop in performance.

Load Balancing: The process of distributing workloads evenly across multiple processors to optimize resource use and minimize response time.