Communication cost is an important consideration in parallel computing, as it can have a significant impact on the performance of the system. In a parallel machine, communication cost refers to the amount of time and resources required to move data between processors or between processors and memory.
There are several factors that can affect communication costs in a parallel machine, including:
1. Network topology: The way in which processors are connected can have a big impact on communication costs. For example, a mesh or torus network can minimize the distance data needs to travel between processors, while a tree or ring network may require more hops.
2. Bandwidth: The amount of data that can be transmitted at once can also impact communication costs. A higher bandwidth network can move data more quickly but may be more expensive.
3. Latency: The amount of time it takes for a message to be transmitted and received can also impact communication costs. A lower latency network can reduce the time spent waiting for data, but may also be more expensive.
4. Synchronization: If multiple processors need to access the same data at the same time, this can introduce synchronization overhead and increase communication cost.
5. Data movement: Finally, the amount of data that needs to be moved can impact communication cost. Moving large amounts of data can be more expensive than moving smaller amounts.
Overall, minimizing communication cost is important for achieving good performance in parallel computing. This can be achieved through careful selection of network topology, bandwidth, and latency, as well as through techniques such as data compression, data replication, and load balancing.