Computation-to-communication ratio

Definition

The computation-to-communication ratio is a measure that compares the amount of computation performed in a parallel or distributed system to the amount of communication required between processes. A higher ratio indicates that more computational work is being done relative to the communication overhead, which can lead to better performance and efficiency in the system. Understanding this ratio helps in optimizing performance by guiding the decomposition and mapping of tasks across processors.

5 Must Know Facts For Your Next Test

1. A high computation-to-communication ratio is desirable as it indicates efficient resource usage and reduced overhead from communication.
2. If the communication overhead becomes too high compared to computation, it can lead to bottlenecks, significantly slowing down overall performance.
3. Optimal task decomposition and mapping can improve the computation-to-communication ratio by minimizing inter-process communication.
4. In distributed systems, maintaining a balance between computation and communication is crucial for achieving scalability and responsiveness.
5. Understanding this ratio is key to designing algorithms that can effectively utilize parallel architectures without falling into communication-heavy pitfalls.

Review Questions

• How does the computation-to-communication ratio impact the design of parallel algorithms?
  • The computation-to-communication ratio directly influences the design of parallel algorithms by guiding how tasks are divided and assigned across processors. A higher ratio suggests that algorithms should focus on maximizing computational tasks while minimizing inter-process communications. This leads to designs that prioritize local computations and reduce dependency on data exchange, ultimately enhancing performance.
• What strategies can be implemented to improve the computation-to-communication ratio in a distributed system?
  • To improve the computation-to-communication ratio, strategies such as effective load balancing, task scheduling, and minimizing data dependencies among processes can be implemented. These strategies help ensure that each processor has a sufficient workload while reducing the frequency and volume of communication required. Additionally, optimizing data locality by keeping related data closer together can further decrease communication overhead.
• Evaluate the significance of maintaining an optimal computation-to-communication ratio in large-scale distributed systems and its impact on overall system performance.
  • Maintaining an optimal computation-to-communication ratio in large-scale distributed systems is crucial for achieving high performance and efficiency. A balanced ratio ensures that processes spend more time on useful computations rather than waiting for data exchanges, which can be particularly detrimental in systems with numerous nodes. When this balance is achieved, it leads to improved throughput, lower latency, and better resource utilization, which are essential for scalability in modern computing environments.

Related terms

Load Balancing:

The process of distributing workloads across multiple computing resources to ensure optimal resource utilization and minimize processing time.

Latency:

The time delay between the initiation of a request and the receipt of a response, particularly relevant in communication between distributed systems.

Parallelism: The practice of dividing a computational task into smaller subtasks that can be executed simultaneously on different processors.