Methods for containing interaction overheads

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Interaction overheads, such as communication and synchronization, can significantly impact the performance of parallel algorithms. Therefore, it is essential to containing these overheads to achieve high-performance parallel computing. Here are some methods for containing interaction overheads:

1. Minimize Communication: The most effective way to contain communication overhead is to minimize the amount of communication required between processors. This can be achieved by using algorithms that minimize the amount of data that needs to be transmitted or by reducing the frequency of communication.

2. Overlap Communication with Computation: Overlapping computation with communication can reduce the impact of communication overheads. This can be achieved by using non-blocking communication, which allows a processor to initiate a communication operation and continue with other computations without waiting for the communication operation to complete.

3. Use Data Compression: Data compression can be used to reduce the amount of data that needs to be transmitted between processors. Compression algorithms can be applied to data before transmission and decompressed at the receiving end.

4. Use Hierarchical Communication: Hierarchical communication involves dividing the processors into groups and limiting the communication between processors in different groups. This can reduce the amount of communication required and improve performance.

5. Use Local Computation: Local computation involves performing computations locally on a processor without communicating with other processors. This can reduce the amount of communication required and improve performance.

6. Use Asynchronous Communication: Asynchronous communication involves allowing processors to communicate independently without waiting for other processors to complete their communication operations. This can reduce the synchronization overhead and improve performance.

7. Use Load Balancing Techniques: Load balancing techniques can be used to distribute the workload evenly among processors, reducing the communication overhead and improving performance.

By using these methods, interaction overheads can be effectively contained, leading to improved performance in parallel computing.


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