Runtime optimization of static languages: JIT for C++?

Question

Is anyone using JIT tricks to improve the runtime performance of statically compiled languages such as C++? It seems like hotspot analysis and branch prediction based on observations made during runtime could improve the performance of any code, but maybe there's some fundamental strategic reason why making such observations and implementing changes during runtime are only possible in virtual machines. I distinctly recall overhearing C++ compiler writers mutter "you can do that for programs written in C++ too" while listening to dynamic language enthusiasts talk about collecting statistics and rearranging code, but my web searches for evidence to support this memory have come up dry.

Answer 1

Profile guided optimization is different than runtime optimization. The optimization is still done offline, based on profiling information, but once the binary is shipped there is no ongoing optimization, so if the usage patterns of the profile-guided optimization phase don't accurately reflect real-world usage then the results will be imperfect, and the program also won't adapt to different usage patterns.

You may be interesting in looking for information on HP's Dynamo, although that system focused on native binary -> native binary translation, although since C++ is almost exclusively compiled to native code I suppose that's exactly what you are looking for.

You may also want to take a look at LLVM, which is a compiler framework and intermediate representation that supports JIT compilation and runtime optimization, although I'm not sure if there are actually any LLVM-based runtimes that can compile C++ and execute + runtime optimize it yet.

Answer 2

I did that kind of optimization quite a lot in the last years. It was for a graphic rendering API that I've implemented. Since the API defined several thousand different drawing modes as general purpose function was way to slow.

I ended up writing my own little Jit-compiler for a domain specific language (very close to asm, but with some high level control structures and local variables thrown in).

The performance improvement I got was between factor 10 and 60 (depended on the complexity of the compiled code), so the extra work paid off big time.

On the PC I would not start to write my own jit-compiler but use either LIBJIT or LLVM for the jit-compilation. It wasn't possible in my case due to the fact that I was working on a non mainstream embedded processor that is not supported by LIBJIT/LLVM, so I had to invent my own.