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Comment by pkolaczk | Hacker News Reader

I think you're exaggerating performance differences. Reasonably written Python is not 10000 times slower than reasonably written C++, nor reasonably written Java is 3-5x slower. And while I agree that creating a super-optimized tight loop of a program in C++, C or (better) assembly might be much easier to do than in higher level JITted languages, but this does not scale to huge and complex codebases. At some point you'll be struggling with overall project complexity, and architecture / high level design can affect performance more than those low-level bits. A higher level language will be more amenable to refactoring and fixing high-level performance problems than a low-level language like C or mid-level language like C++. So then, you might be much better choosing a high level fast language like Haskell or Scala or even C#/Java than C++.

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Not every Python program is slower than C++ by a factor of 10000. Some are. Some Python can be reasonably fast as long as all the heavy lifting is done in native code, e.g. numpy or scipy. Let's say it would typically be more in the x250 range for algorithmic code that uses native Python types. You get to the 10k range when you create your own types and do meta-programming and try to do algorithms over that. YMMV.

The point I was making though was that if you don't care then you don't care. I wrote some Python for a friend who wanted to scrape and process financial information from various web sites. I'd be an idiot to do that in C++. None of us cared how long it took to run (as long as it wasn't weeks). Having access to various Python libraries made this task a breeze and maintainability wasn't much of a concern either.

Andrei wouldn't have made that statement about C++ vs. Java if the difference was in the noise. I think x3-5 is a reasonable rough number to put on it but while I can point to benchmarks I can't point to a comprehensive study that shows "reasonably" written across different domains. You're welcome to take those numbers with a grain of salt and do your own investigation. Another data point there is that C++ is the dominant language in Google Code Jam and TopCoder SRMs where writing fast and correct code quickly is a competitive advantage.

EDIT: I find a lot of people will underestimate the performance advantage of native languages vs. JIT or interpreted environments. The x10000 is something I've seen in a real world system. Another thing to consider is that in a native language you can drop to assembler to optimize performance critical sections. You have 100% absolute control over your hardware. Anyone know how this: https://code.google.com/p/h264j/ compares to the original implementation?

"The point I was making though was that if you don't care then you don't care."

I disagree with this. You always care, to some degree. If I write a one time, 100-LOC script to process a 1GB of data, I don't care if it takes 1 second or 1 hour. But I would care if it took 1 week or 1 month. That's why Java performance is good enough for 99% of programs I write, Python is also good for many, but if I completely didn't care for performance and wrote sloppy code in Python (or Java) I'd get into 10000x performance penalty region and this would be unacceptable in almost all cases.

"I think x3-5 is a reasonable rough number to put on it but while I can point to benchmarks I can't point to a comprehensive study that shows "reasonably" written across different domains" YMMV. The micro-benchmarks in Great Language shootout disagree with this. Most of them are within 2x range and the one outstanding is actually a benchmark of particular regular expression engine. Ok, we should not believe microbenchmarks, so what about real, optimized applications? Compare performance of Netty vs nginx. Or Tomcat vs Apache. This is a tie. Or Hypertable vs HBase (yeah, despite huge expectations and marketing, even in Hypertable own benchmarks, Hbase comes only... 50% - 2x slower). Or Jake vs Quake2 with Jake2 again not even 50% worse (actually better in some cases).

"C++ is the dominant language in Google Code Jam" This only supports what I already wrote - when writing a very small piece of code you have full control over, like for a competition, it is much easier to achieve high performance code in asm/C/C++ than in Java/C#/Scala etc. In a competitiona like that, even a 20% overhead is not acceptable and I'd also use C or C++. But you can't extrapolate that on large-scale programming, where benefits of using a high level language matter much more.

"Another thing to consider is that in a native language you can drop to assembler to optimize performance critical sections" I can do that in Java or C# as well.

Sure, there's some transition region between "don't care" and "care" but I find a lot of people tend to be strongly in one or the other (sometimes wrongly).

Another benchmark: https://days2011.scala-lang.org/sites/days2011/files/ws3-1-H...

Keep in mind though that the JVM is a moving target. It keeps getting better (on one hand) and on some platforms it's worse (e.g. Android, though the upcoming new version looks promising). It's possible the gap is smaller now than what I remember seeing in the past.

At any rate, if x2 is a number that feels right for the stuff you're doing I can't argue with that. You need to choose the language that works for you. Maybe you choose Java because of the libraries. Maybe you have more experience writing in Java and you're a lot more productive. Maybe there is better tooling. Maybe it's just more in line with how you think.

A web server does a lot of file I/O and a lot of network I/O. The performance of a web server is more about how efficiently you can juggle those given highly concurrent loads and what mechanisms are used at the native layer to interface to those systems. It's not so much about the "raw" power of the language. In your game engine example a lot of the heavy lifting is done in OpenGL which is native code. I'm also not intimately familiar with the details there. At some point it's also about how much effort went into optimizing things and whether or not something else was traded off.

To contrast that, Google Code Jam tasks are typically algorithmic and they stress the "raw" power aspect of the language. That said it's certainly not representative of real-world product development.

If you are thinking about the Android runtime, Dalvik and now ART, those aren't JVM (I think they compile to a different bytecode). There probably is a port of JVM but I didn't get the impression you were talking about that.

Dalvik does indeed use a different VM/bytecode. Your Java Android app is compiled JVM bytecode which is translated into Dalvik. The point is/was that the program you write in Java may run faster or slower depending on your target platform and that if your target platform is Android you are taking an additional penalty vs. e.g. using the NDK...

(EDIT: Yeah, you're right, the name JVM should only be used to refer to the specific type of VM that runs specific bytecode and not to other VMs. As Android shows Java does not have to run on the JVM. Thanks for the correction.)

"In 2012, academic benchmarks confirmed the factor of 3 between HotSpot and Dalvik on the same Android board, also noting that Dalvik code was not smaller than Hotspot" (from Wikipedia)

> creating a super-optimized tight loop of a program in C++, C or (better) assembly might be much easier to do than in higher level JITted languages

Sometimes, the situation is exactly opposite, though:

http://pzemtsov.github.io/2014/05/15/how-to-make-C-as-fast-a...

I've never seen 10000x going python to c++, but I've certainly seen 1000x several times.

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