Inter-Process Communication Affects Application Response Time

When performance is a problem in such applications, my experience has been that examining data structures and algorithms isn’t the right place to look for improvements. Response time depends most strongly on the number of remote inter-process communications (IPCs) conducted in response to a stimulus. While there can be other local bottlenecks, the number of remote inter-process communications usually dominates. Each remote inter-process communication contributes some non-negligible latency to the overall response time, and these individual contributions add up, especially when they are incurred in sequence.

There are a few relatively obvious and well-known strategies for reducing the number of remote inter-process communications per stimulus. One strategy is to apply the principle of parsimony, optimizing the interface between processes so that exactly the right data for the purpose at hand is exchanged with the minimum amount of interaction. Another strategy is to parallelize the inter-process communications where possible, so that the overall response time becomes driven mainly by the longest-latency IPC. A third strategy is to cache the results of previous IPCs, so that future IPCs may be avoided by hitting local cache instead.

By Randy Stafford