How can you reduce communication overhead in parallel scientific computing algorithms?

3 Exploit communication-computation overlap

A third factor that affects communication overhead is how you schedule the communication and computation tasks among the processors or nodes. Ideally, you want to schedule the communication and computation tasks in such a way that maximizes the overlap between them. This can reduce the communication waiting time and improve the resource utilization. For example, you can use non-blocking communication to initiate a communication task and then proceed with a computation task without waiting for the communication task to finish. This can reduce the communication idle time and increase the parallelism. Alternatively, you can use pipelining to divide a large communication task into smaller chunks and then interleave them with computation tasks. This can reduce the communication burstiness and improve the throughput.

4 Optimize communication parameters

A fourth factor that affects communication overhead is how you tune the communication parameters among the processors or nodes. Ideally, you want to tune the communication parameters in such a way that optimizes the performance and scalability of the algorithm. For example, you can adjust the message size to balance the communication bandwidth and overhead. Smaller messages can reduce the communication startup time and overhead, but larger messages can increase the communication bandwidth and efficiency. Alternatively, you can adjust the number of processors or nodes to balance the communication and computation costs. More processors or nodes can increase the computation speedup and parallelism, but also increase the communication overhead and complexity.

5 Use appropriate communication libraries

A fifth factor that affects communication overhead is how you implement the communication among the processors or nodes. Ideally, you want to use appropriate communication libraries that provide high-level abstractions and low-level optimizations for the communication tasks. For example, you can use MPI (Message Passing Interface) to perform point-to-point and collective communication among the processors or nodes. MPI can provide portability, scalability, and flexibility for the communication tasks. Alternatively, you can use PGAS (Partitioned Global Address Space) to access remote data among the processors or nodes. PGAS can provide transparency, consistency, and locality for the communication tasks.

6 Evaluate communication performance

A sixth factor that affects communication overhead is how you measure and analyze the communication performance among the processors or nodes. Ideally, you want to evaluate the communication performance in such a way that identifies the communication bottlenecks and opportunities for improvement. For example, you can use communication profiling tools to collect and visualize the communication events and statistics among the processors or nodes. This can help you understand the communication behavior and patterns of the algorithm. Alternatively, you can use communication modeling tools to predict and compare the communication costs and benefits of different communication strategies and techniques. This can help you optimize the communication efficiency and scalability of the algorithm.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?