5 ms·

You've gone from claiming reference-counting is faster than tracing GC to claiming it's even faster than hand optimized C++, which is quite honestly unbelievabl

by brabel 4y ago

You've gone from claiming reference-counting is faster than tracing GC to claiming it's even faster than hand optimized C++, which is quite honestly unbelievable - whatever the reference counting algorithm is doing can be emulated by the hand-optimised C++ code so that's just literally impossible. But anyway, it's a completely fruitless discussion here unless you provide data that we can look at and scrutinize. OP hasn't provided any. You haven't provided any (and I do believe you may think you're right, but I've been in the position of being very confident of something just to be proven completely wrong by giving all my data to others to scrutinize... it's disheartening but necessary to get to the bottom of what's real). It's like the V language saying it can do memory management magically and it's much faster than Rust or whatever when they don't even have a working system yet.

fnord123 4y ago
> to claiming it's even faster than hand optimized C++, which is quite honestly unbelievable - whatever the reference counting algorithm is doing can be emulated by the hand-optimised C++ code so that's just literally impossible. There is nuance here. They claimed that their project is faster than a specific hand optimized project. Not faster than a theoretical peak performance c++ program. I've run into similar situations where python reimplementation s are faster than java because python is easier to change and fixup algorithms. And %timeit in the ipython shell is way easier than the black magic involved in profiling and benchmarking java. You also have people on rust subreddit or discourse asking for optimization help when their rust is not as fast as a Go example they wrote. Often you get buffered IO going and it's on par. But to melt faces like ripgrep and friends you often need to drop pretences and work on Vec<u8>s.
- brabel 4y ago
  > And %timeit in the ipython shell is way easier than the black magic involved in profiling and benchmarking java Unfair criciticm... first, Java has had a REPL for several years and you can time stuff like in Python as easily... second, profiling tools in Java are some of the best available, and are not blackmagic... quite simple to use, just attach them to the running process and hit "profile". With that said: yes, I've also seen Java programs that run faster than the Rust or C counterpart. I suspect what OP saw falls into this category: a rare example that you take as a rule (maybe I misinterpreted the claim, I admit, but it does sound OP meant his Haskell program and, I assume, others which you write in the same style, cannot be beaten by the equivalent C++).
  - fnord123 4y ago
    > Unfair criciticm... first, Java has had a REPL for several years and you can time stuff like in Python as easily... second, profiling tools in Java are some of the best available, and are not blackmagic... quite simple to use, just attach them to the running process and hit "profile". You're right that the java tooling is powerful. But getting numbers out of the JVM is not the black magic. The dark arts are setting up an experiment, managing the JIT warm-up, and interpreting the results. In my experience it's just hell trying to turn those numbers into a convincing argument that we have confidence in our performance. Concrete example: convincing openjdk11 to use AES instructions Vs conscrypt using AES out of the box with no warm-up... The whole song and dance means that we just don't take it as a priority because it's a tiring sink of effort. On the other hand %timeit is so easy and remains consistent that you can use it with unit tests and offer algorithm fixes in PRs.
  - timcavel 4y ago
staticassertion 4y ago
> whatever the reference counting algorithm is doing can be emulated by the hand-optimised C++ code so that's just literally impossible. shared_ptr and unique_ptr have pretty significant overhead and are common practice, even for optimized codebases, so I wouldn't say it's impossible at all.
- deleted 4y ago
  [deleted]
- tpolzer 4y ago
  unique_ptr is nearly overhead free compared to a bare pointer (except for argument passing... because of ABI concerns). shared_ptr is expensive and easy to build leaks with, so a lot of code bases avoid it where possible. Though it's only expensive when you copy it, moving it is ~free.
  - staticassertion 4y ago
    This is just a more detailed version of my point lol you just listed the reasons they are not free.
- mbrodersen 4y ago
  You are 100% right. At some point I measured smart_ptr to be 25x slower than raw pointers. The compiler I am maintaining is not using C++ style smart pointers. It is using a global whole program optimisation pass to reduce reference manipulation to a minimum. Basically what a world class C++ programmer with years of experience optimising performance would do. It is just done automatically.
deterministic 4y ago
The code is not open source unfortunately so I can’t provide evidence. But I have nothing to sell or promote (unlike the V language guys who has a clear motivation to inflate what they are selling). What the compiler does differently is that it does a whole program optimisation pass where it minimises the code manipulating the reference counters to an absolute minimum. This is something you can’t do in a C++ compiler. Which is one of the reason why the resulting machine code is faster. Also, I am using LLVM as the backend. So I am getting the same low-level optimisations that C++ compilers get. However the most important optimisations are done before lowering the code to LLVM. Function specialisation for example is huge (generating multiple versions of the same function with the arguments unlined and optimised away). I am doing quite a few high level optimisations like that before even lowering it to LLVM.

by brabel 4y ago

fnord123 4y ago

> to claiming it's even faster than hand optimized C++, which is quite honestly unbelievable - whatever the reference counting algorithm is doing can be emulated by the hand-optimised C++ code so that's just literally impossible. There is nuance here. They claimed that their project is faster than a specific hand optimized project. Not faster than a theoretical peak performance c++ program. I've run into similar situations where python reimplementation s are faster than java because python is easier to change and fixup algorithms. And %timeit in the ipython shell is way easier than the black magic involved in profiling and benchmarking java. You also have people on rust subreddit or discourse asking for optimization help when their rust is not as fast as a Go example they wrote. Often you get buffered IO going and it's on par. But to melt faces like ripgrep and friends you often need to drop pretences and work on Vec<u8>s.

brabel 4y ago
> And %timeit in the ipython shell is way easier than the black magic involved in profiling and benchmarking java Unfair criciticm... first, Java has had a REPL for several years and you can time stuff like in Python as easily... second, profiling tools in Java are some of the best available, and are not blackmagic... quite simple to use, just attach them to the running process and hit "profile". With that said: yes, I've also seen Java programs that run faster than the Rust or C counterpart. I suspect what OP saw falls into this category: a rare example that you take as a rule (maybe I misinterpreted the claim, I admit, but it does sound OP meant his Haskell program and, I assume, others which you write in the same style, cannot be beaten by the equivalent C++).
- fnord123 4y ago
  > Unfair criciticm... first, Java has had a REPL for several years and you can time stuff like in Python as easily... second, profiling tools in Java are some of the best available, and are not blackmagic... quite simple to use, just attach them to the running process and hit "profile". You're right that the java tooling is powerful. But getting numbers out of the JVM is not the black magic. The dark arts are setting up an experiment, managing the JIT warm-up, and interpreting the results. In my experience it's just hell trying to turn those numbers into a convincing argument that we have confidence in our performance. Concrete example: convincing openjdk11 to use AES instructions Vs conscrypt using AES out of the box with no warm-up... The whole song and dance means that we just don't take it as a priority because it's a tiring sink of effort. On the other hand %timeit is so easy and remains consistent that you can use it with unit tests and offer algorithm fixes in PRs.
- timcavel 4y ago

staticassertion 4y ago

> whatever the reference counting algorithm is doing can be emulated by the hand-optimised C++ code so that's just literally impossible. shared_ptr and unique_ptr have pretty significant overhead and are common practice, even for optimized codebases, so I wouldn't say it's impossible at all.

deleted 4y ago
[deleted]
tpolzer 4y ago
unique_ptr is nearly overhead free compared to a bare pointer (except for argument passing... because of ABI concerns). shared_ptr is expensive and easy to build leaks with, so a lot of code bases avoid it where possible. Though it's only expensive when you copy it, moving it is ~free.
- staticassertion 4y ago
  This is just a more detailed version of my point lol you just listed the reasons they are not free.
mbrodersen 4y ago
You are 100% right. At some point I measured smart_ptr to be 25x slower than raw pointers. The compiler I am maintaining is not using C++ style smart pointers. It is using a global whole program optimisation pass to reduce reference manipulation to a minimum. Basically what a world class C++ programmer with years of experience optimising performance would do. It is just done automatically.

deterministic 4y ago

The code is not open source unfortunately so I can’t provide evidence. But I have nothing to sell or promote (unlike the V language guys who has a clear motivation to inflate what they are selling). What the compiler does differently is that it does a whole program optimisation pass where it minimises the code manipulating the reference counters to an absolute minimum. This is something you can’t do in a C++ compiler. Which is one of the reason why the resulting machine code is faster. Also, I am using LLVM as the backend. So I am getting the same low-level optimisations that C++ compilers get. However the most important optimisations are done before lowering the code to LLVM. Function specialisation for example is huge (generating multiple versions of the same function with the arguments unlined and optimised away). I am doing quite a few high level optimisations like that before even lowering it to LLVM.