Do inline functions improve performance?

Yes and no. Sometimes. Maybe.

There are no simple answers. inline functions might make the code faster, they might make it slower. They might make the executable larger, they might make it smaller. They might cause thrashing, they might prevent thrashing. And they might be, and often are, totally irrelevant to speed.

inline functions might make it faster:  procedural integration might remove a bunch of unnecessary instructions, which might make things run faster.

inline functions might make it slower: Too much inlining might cause code bloat, which might cause “thrashing” on demand-paged virtual-memory systems. In other words, if the executable size is too big, the system might spend most of its time going out to disk to fetch the next chunk of code.

inline functions might make it larger: This is the notion of code bloat, as described above. For example, if a system has 100 inline functions each of which expands to 100 bytes of executable code and is called in 100 places, that’s an increase of 1MB. Is that 1MB going to cause problems? Who knows, but it is possible that that last 1MB could cause the system to “thrash,” and that could slow things down.

inline functions might make it smaller: The compiler often generates more code to push/pop registers/parameters than it would by inline-expanding the function’s body. This happens with very small functions, and it also happens with large functions when the optimizer is able to remove a lot of redundant code through procedural integration — that is, when the optimizer is able to make the large function small.

inline functions might cause thrashing: Inlining might increase the size of the binary executable, and that might cause thrashing.

inline functions might prevent thrashing: The working set size (number of pages that need to be in memory at once) might go down even if the executable size goes up. When f() calls g(), the code is often on two distinct pages; when the compiler procedurally integrates the code of g() into f(), the code is often on the same page.

inline functions might increase the number of cache misses: Inlining might cause an inner loop to span across multiple lines of the memory cache, and that might cause thrashing of the memory-cache.

inline functions might decrease the number of cache misses: Inlining usually improves locality of reference within the binary code, which might decrease the number of cache lines needed to store the code of an inner loop. This ultimately could cause a CPU-bound application to run faster.

inline functions might be irrelevant to speed: Most systems are not CPU-bound. Most systems are I/O-bound, database-bound or network-bound, meaning the bottleneck in the system’s overall performance is the file system, the database or the network. Unless your “CPU meter” is pegged at 100%, inline functions probably won’t make your system faster. (Even in CPU-bound systems, inline will help only when used within the bottleneck itself, and the bottleneck is typically in only a small percentage of the code.)

There are no simple answers: You have to play with it to see what is best. Do not settle for simplistic answers like, “Never use inline functions” or “Always use inline functions” or “Use inline functions if and only if the function is less than N lines of code.” These one-size-fits-all rules may be easy to write down, but they will produce sub-optimal results.


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