Think about it this way because the issue isn’t specific to allocators: it’s pretty good in general but can often be beaten if you have special understanding of what you need to do. That’s OK.
You can buy cars and trucks that are optimized for driving on freeways and residential streets carrying stuff people often carry. But then there are special vehicles like fork lifts and such that are kinds of large special cases. And then there are weirdo specialised vehicles that have four wheels but are rare and their users can’t live without them.
Languages like C++ let you plug in special allocators if you want. But most people don’t. Some, like HFT people do crazy headstands to avoid slow allocations. I don’t ever want to do that but if they want to, why not. I don’t think they complain that the default case doesn’t fit their needs!
For a typical program, I bet that the overall impact of the glibc allocator is well under 0.1%. If you can choose between < 0.1% and < 0.12%, I guess it matters in some sense, but not in any practical way. We almost never spend time on other sub-0.1% optimizations. You could probably squeeze a lot more by optimizing CPU branch predictor performance, minimizing CPU cache misses, or fine-tuning the scheduling strategy, but we also don't bother. 'Tis is the era of "native" apps written in Electron.
A decade ago, I worked on a simulation program that involved a C++ core with a Python wrapper and DB interface. End users cared a lot about throughout with a rather limited size, weight and power budget. We spent a lot of time optimizing the core -- but basically hit a bottleneck once we got to about 15% of the time that was spent in malloc-related functions. It turned out that was all in the Python layer. Probably there was some level of bad code in our Python code, but it was impractical to figure out where that was. I was shocked because I assumed the simulation core (which ended up almost allocation-free by the end) would always dominate CPU usage.
Both allocators and Python have probably gotten better since then, but it was a fascinatingly large and stubborn fraction of CPU time.
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That's an interesting viewpoint, but then, will the allocator's performance never matter for any use case that is not "wrong"? It doesn't feel right.
Think about it this way because the issue isn’t specific to allocators: it’s pretty good in general but can often be beaten if you have special understanding of what you need to do. That’s OK.
You can buy cars and trucks that are optimized for driving on freeways and residential streets carrying stuff people often carry. But then there are special vehicles like fork lifts and such that are kinds of large special cases. And then there are weirdo specialised vehicles that have four wheels but are rare and their users can’t live without them.
Languages like C++ let you plug in special allocators if you want. But most people don’t. Some, like HFT people do crazy headstands to avoid slow allocations. I don’t ever want to do that but if they want to, why not. I don’t think they complain that the default case doesn’t fit their needs!
For a typical program, I bet that the overall impact of the glibc allocator is well under 0.1%. If you can choose between < 0.1% and < 0.12%, I guess it matters in some sense, but not in any practical way. We almost never spend time on other sub-0.1% optimizations. You could probably squeeze a lot more by optimizing CPU branch predictor performance, minimizing CPU cache misses, or fine-tuning the scheduling strategy, but we also don't bother. 'Tis is the era of "native" apps written in Electron.
A decade ago, I worked on a simulation program that involved a C++ core with a Python wrapper and DB interface. End users cared a lot about throughout with a rather limited size, weight and power budget. We spent a lot of time optimizing the core -- but basically hit a bottleneck once we got to about 15% of the time that was spent in malloc-related functions. It turned out that was all in the Python layer. Probably there was some level of bad code in our Python code, but it was impractical to figure out where that was. I was shocked because I assumed the simulation core (which ended up almost allocation-free by the end) would always dominate CPU usage.
Both allocators and Python have probably gotten better since then, but it was a fascinatingly large and stubborn fraction of CPU time.
C++ and python are exactly where I would expect this problem to come up