Interaction overhead is a common problem in parallel computing systems, where the cost of communication between processors can limit the overall performance of the system. There are several methods for containing interaction overhead:
- Minimize communication: One way to reduce interaction overhead is to minimize the amount of communication required between processors. This can be achieved by using efficient data structures, minimizing data movement, and optimizing communication patterns.
- Asynchronous communication: Asynchronous communication can reduce interaction overhead by allowing processors to continue computing while waiting for data from other processors. This approach can improve performance by overlapping computation and communication.
- Read Also : what are characteristics of Task and Interaction ?
- Communication overlap: Communication overlap involves overlapping communication with computation to reduce the time spent waiting for data. This can be achieved by using pipelining or message buffering techniques.
- Local computation: Local computation involves performing computation on local data before communicating with other processors. This can reduce the amount of data that needs to be communicated and can improve performance by reducing communication latency.
- Data compression: Data compression can reduce the amount of data that needs to be communicated between processors by compressing the data before transmission. This approach can reduce interaction overhead and improve performance, but it requires additional computational resources to compress and decompress data.
How to replace local branch with remote branch entirely in Git? totally science gitlab
Overall, the goal of containing interaction overhead is to optimize the use of available hardware resources and minimize the impact of communication on overall performance.
The choice of method depends on the specific characteristics of the application and the available hardware resources.
Nilesh Raut Changed status to publish