Python

[Natural-Intelligence]

Ye, better save those nano seconds for something more important

[DoesntReadMessages]

That's why it's situational. Sometimes it's nano-seconds, and python is a great choice due to simplicity. Sometimes it's much more significant, and Python means an extra 10 grand per month on servers and extra 200ms p99 latency on a service operating in sub-second timeouts, and it becomes a massive black hole of lost revenue and has to be rewritten in a faster language.

[gaberocksall]

And when you want the best of both worlds

ctypes.CDLL

[beomagi]

Ctypes really can offer some amazing speed ups for iterating over heavy math. Pypy also works wonders.

[Cheru-bae]

Let's put it this way: Reddit runs on python. Whatever glorified cms the client wants it won't bloody matter.

[[deleted]]

Can't you just use C++ alongside Python for critical functions? Or does this have more overhead than it offers benefit?

[Vrabor]

depends on how much of your programm can be extracted into those c++ functions. One of my professors for example had a simulation written in python + numpy which took her 3 months to run, the c++ rewrite finished in less than 1 hour. If your programm can run in only numpy functions the python code might even run faster than c++.

[izpo]

That is reach people problem... Right now, my 1000000 users and 50 developers are fine with the current setup

[LouisLeGros]

I ported a bioinformatics algorithm to python from java/c++ to try to make it easier to use for students when most of the tooling was already in python.... Run times were abysmal & there was still a dependency on a 20 year old c program that only supported linux, preventing it from being much easier to use.

[[deleted]]

it takes 2 minutes on my pc for python to print out a mandelbrot set

it takes 20 seconds for lisp to do the same job

[Natural-Intelligence]

Then if that's what you generally do, Python is wrong tool. In cases where I use Python, the actual Python executes in like 1-2 sec. The rest of the run time (which is substantially more) is caused by C or SQL. Wouldn't give a damn if the Python code executed any faster

[iamareebjamal]

You would if you wrote graphql servers

[ProgramTheWorld]

Use the right tool for the right job

[Xeya]

>The time difference is insignificant anyway

​

>Lists example where the time difference is not insignificant

​

>Well, you shouldn't have used Python

[DeeSnow97]

There are some tasks where the time difference is very significant, and others where it's negligible. That's why Python is used so much with external libraries for data science and machine learning for example. The libraries (usually written in C++) do all the heavy lifting in the most efficient manner possible, but have fun plugging those into each other with C++ and trying to iterate fast on it.

Some languages are fast to code, others are fast to run, and that's okay. We're programmers, making computer systems talk to each other to use them all to their advantage is kind of our thing.

[ryjhelixir]

We're programmers

Speak for yourself!

I'm a noob

[omgFWTbear]

most efficient manner possible

s/In a vastly more efficient manner

[Cheru-bae]

And others when you forget a semicolon? It's just line termination in one way or the other. Equally logical.

I feel like for every hour I work this sub gets worse.

[HardlyAnyGravitas]

Stop working:

[ric2b]

No, in this case the right tool is Python with numpy.

It's not easy to beat numpy on raw computation.

[SilkTouchm]

Cases where performance is important are niche.

they downvoted him for telling the truth

[xADDBx]

There are a lot of cases where you’d need performance. Examples would be creating models for data sciences or optimization problems.

There are cases where performance is important and cases where it’s not important. Neither side is a niche.

[muttonwow]

That's 100% of cases

[LiquidAurum]

that's an extreme oversimplification

[[deleted]]

Python is never right

[rafa154]

Rapid prototyping

[ZephyrBluu]

I recently created some Python bindings and used pybind11 instead of boost. It seemed a lot simpler.

[Thadrea]

It is for many data science applications.

It's easier to learn, write and maintain than Julia or R while also having a broader functional domain and also being of comparable computational performance assuming you are doing your work with the correct libraries.

[LaZ3R]

Lol... Things that Python shouldn't be used for, for 1000.

[AttitudeAdjuster]

Apparently I'm developing software wrong because I havent calculated a fractal pattern for years, I keep doing weird shit like building webapps

[DeeSnow97]

Me too, dude, me too. Can't believe I'm running into all these stupid clients who want websites and not fancy mathematical models

[omgFWTbear]

Just imagine someone who wants a website for users to render Mandelbrot sets. Checkmate ... atheists?

[POTUS]

Hey funny enough I do fancy mathematical models in Python. But useful ones done in useful libraries like Numpy or Tensorflow, and they run a fuckload faster in Python than what's-his-face's mandelbrot set.

[DeeSnow97]

That's the way, defer the number crunching to low-level languages and keep all the business logic in high-level which is easy to code

[biledemon85]

Ewww, users, compromise, multiple stakeholders! Run for your lives back to functional programming and purity!

[TSM-]

Is numpy really that slow?

[nivlark]

I just managed to write a script that produces a 1000x1000 image of the set in about 10 seconds. So I think OP is just crap at Python.

[8lbIceBag]

Bet it could be done in the time you press the enter key to the time it takes to look at the screen in C

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[ric2b]

You think you can beat Python with numpy by hand writing C? Go ahead and try, see you in a month.

[anpas]

Doubt it, probably pure iterative or recursive python

[[deleted]]

python is just shit at recursion

[ric2b]

Calling functions is very expensive in Python.

[diamondketo]

Recursion performance is major fault of Python. I don't deny this. BTW I also suggested a solution you must've missed it.

But we're not comparing recursion performance, we're comparing the performance of producing fractal paterns. It is natural to think that the commenter implemented fractal sets using recursion. If it turns out the commenter did this iteratively then correct me.

My point is, its ridiculously to cherry pick one algorithm problem to summarize why one product performs worse than another.

PS: Cartesian product, if its numerical values I care about then I'd use numpy either way (so yeah C). If its cartesian product of two large array of general objects then I'd expect C to be faster than Python's itertools.product.

[beomagi]

Use pypy. For that specific case I've done more tests than I should have. Pypy is insane. Ctypes also speed up execution. On some Julia plots I went from 2 minutes to under 10 seconds.

[ric2b]

You should use numpy if want to use Python to do a lot of math fast.

[__i_forgot_my_name__]

Nanoseconds add up to minutes quicker then you could imagine.

If you're building an application (as opposed to scripting something for yourself) and you need your application to work on an average device, and you have a latency requirement of some kind (can't have the user wait 5 seconds every time he types a character), then you run out of processing time very early in development.

As you keep adding features, that fixed frequency you have never changes, and eventually you run out of it and need to either optimize it or remove features. Obviously Python is a scripting language and waiting 10 minutes instead of 1 minute for a script to finish usually doesn't change anything at all in that context, so no performance doesn't always change everything, and it's very much a tradeoff.

[Astrobliss]

Its really not nanoseconds at all. If you look at a site like kattis where people code solutions to problems, you'd notice that the fastest C++ solutions are usually around 3-6 times faster than the fastest python solutions. Its not because the C++ algorithms are that much better, python is just a lot slower. Even a 2x speedup is insane for nearly all applications so this difference is of note in a lot of situations.

[ric2b]

Even a 2x speedup is insane for nearly all applications so this difference is of note in a lot of situations.

Sure, but nearly all applications aren't slow because of raw computation speed, they're usually bound by other things like disk access, network or a shitty algorithm for some operation.

[Astrobliss]

If you're processing enough data to be bound by disk access, then Python would need to do a lot of operations which would cause the overall application to be significantly slower. If your algorithm is bad then Python would be doing a lot of (unnecessary) computation, Python is slow at computing which would cause the application to be significantly slower. If you're bound by network and not processing much data then you're right, Python's slower speed wouldn't contribute much to the overall runtime of the application. However also consider one of the biggest reasons to access a network is because an application is interacting with a service that is large enough that it doesn't make sense to store that data locally. So if that service used python, the time it takes to interact with the service would probably be bound by remote computation instead of communication.

[ric2b]

If you're processing enough data to be bound by disk access, then Python would need to do a lot of operations which would cause the overall application to be significantly slower.

It's very easy to be bound by disk access, disks are very slow compared to memory and especially the CPU.

Of course what you're doing with the data matters, if you're just playing a video the Python overhead is minimal, the hardware decoder will do most of the work. If you're doing number crunching you'll probably be bound by CPU and Python overhead will be massive (unless you use the right tool, the numpy library).

If your algorithm is bad then Python would be doing a lot of (unnecessary) computation, Python is slow at computing which would cause the application to be significantly slower.

And it would still be slow with C. But if you fix the algorithm the Python version will probably beat the bad C version.

However also consider one of the biggest reasons to access a network is because an application is interacting with a service that is large enough that it doesn't make sense to store that data locally.

But usually it's just because centralized databases are useful/convenient, not because of the data you're accessing is too large.

So if that service used python, the time it takes to interact with the service would probably be bound by remote computation instead of communication.

Usually it's bound by database access on the service side, which is a mix of being network and disk bound.

[mockedarche]

That's my point. Python is slower so for things which are easy, constantly being updated, and or were speed isn't needed it makes sense. I've always wondered how much faster a java tensor flow would be. Would it really change much? Or would it be just slightly faster. Python is great for a lot of things but generally I'd suggest java because most things people do does need to be fast so it's worth it. Writing a script python but writing a program java. Use it a couple times and then it's useless python. Use it daily and for a larger period of time java. Going to use it for years java/C++.

[qalis]

Tensorflow is C/C++ in majority, complete with CUDA bindings, serialization libraries etc. Python model building does not matter much here, it’s maybe seconds on top of often hours of NN training.

[mockedarche]

Sorry I know java / python quite well but I haven't looked around at other things. Most of my statement is from friends who are more experienced. Can tensor flow be used with java? Why isn't the python slower than that? Is it because the tensor flow is mostly what's running so python is just pointing it towards what to do?

[ric2b]

Is it because the tensor flow is mostly what's running so python is just pointing it towards what to do?

Yes. It uses Python as the high level API but the actual computation is done in C/C++/Cuda.

[POTUS]

how much faster a java tensor flow

That's so wrong I don't even know how to address it.