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A smart C compiler can probably optimize your loop away by recognizing that at the end,
a will always be 1. Python can't do that because when iterating over
xrange, it needs to call
__next__ on the
xrange object until it raises
StopIteration. python can't know if
__next__ will have side-effect until it calls it, so there is no way to optimize the loop away. The take-away message from this paragraph is that it is MUCH HARDER to optimize a Python "compiler" than a C compiler because python is such a dynamic language and requires the compiler to know how the object will behave in certain circumstances. In
C, that's much easier because C knows exactly what type every object is ahead of time.
Of course, compiler aside, python needs to do a lot more work. In
C, you're working with base types using operations supported in
hardware instructions. In python, the interpreter is interpreting the byte-code one line at a time in software. Clearly that is going to take longer than machine level instructions. And the data model (e.g. calling
__next__ over and over again) can also lead to a lot of function calls which the C doesn't need to do. Of course, python does this stuff to make it much more flexible than you can have in a compiled language.
The typical way to speed up python code is to use libraries or intrinsic functions which provide a high level interface to low-level compiled code.
numpy are excellent examples this kind of library. Other things you can look into are using
which includes a JIT compiler -- you probably won't reach native speeds, but it'll probably beat Cpython (the most common implementation), or writing extensions in C/fortran using the Cpython-API, cython or f2py for performance critical sections of code.
Learning Python: Powerful Object-Oriented Programming, Now the need to call a user-defined function for the map call makes it slower than the fact that the looping statements version is larger in terms of code—or equivalently, On Python 3.3: c:\code> c:\python33\python timeseqs2.py 3.3.0 + may also be slower than a trivial abs), and that list comprehensions run quickest in The running times of individual operations within the inner loop are pretty much the same as the running times of analogous operations elsewhere in the code. Note how breaking the code down increased the total running time. The inner loop now takes 99.9% of the running time. The dumber your Python code, the slower it gets. Interesting, isn’t it?
Simply because Python is a more high level language and has to do more different things on every iteration (like acquiring locks, resolving variables etc.)
"How to optimise" is a very vague question. There is no "general" way to optimise any Python program (everythng possible was already done by the developers of Python). Your particular example can be optimsed this way:
a = 1
That's what any C compiler will do, by the way.
If your program works with numeric data, then using
numpy and its vectorised routines often gives you a great performance boost, as it does everything in pure C (using C loops, not Python ones) and doesn't have to take interpreter lock and all this stuff.
Cython: A Guide for Python Programmers, Even the in-place sum is a C-only operation, because total_mom is a 7 microseconds to run on the same list as before, indi‐cating a tenfold speedup over the The result isn't pretty: 71 microseconds, which is slower than the all-Python version! Typing the particle loop variable here yields the most significant performance @chentingpc -- As I stated in the second paragraph, a for loop in python does a lot of stuff that the C/C++ compiler doesn't. The extra work/time makes it so that python objects can be iterable -- Something very useful that you don't get in C (I'm not sure about C++).
Python is (usually) an interpreted language, meaning that the script has to be read line-by-line at runtime and its instructions compiled into usable bytecode at that point.
C is (usually) a compiled language, so by the time you're running it you're working with pure machine code.
Python will never be as fast as C, for that reason.
Edit: In fact, python compiles INTO C code at run time, that's why you get those .pyc files. High Performance Python: Practical Performant Programming for Humans, Since the data is laid out in a contiguous block, it is trivial to calculate the address asking CPython to calcu‐late the same result, which would involve a slow call back into the virtual machine. Cython annotations (that is, if you just run it as a plain Python script), it'll take 176 | Chapter 7: Compiling to C Cython and numpy. So, why is Python so much slower than both Java and C# in the benchmarks if they all use a virtual machine and some sort of Bytecode? Firstly, .NET and Java are JIT-Compiled. JIT or Just-in-time compilation requires an intermediate language to allow the code to be split into chunks (or frames).
As you go more abstract the speed will go down. The fastest code is assembly code which is written directly.
Read this question Why are Python Programs often slower than the Equivalent Program Written in C or C++?
If you have slow loops in Python, you can fix it…until you can't, You are given a knapsack of capacity C and a collection of N items. The basic idea is to start from a trivial problem whose solution we know and then within the inner loop are pretty much the same as the running times of Create a Python object c. 4a. set c->PyObject_HEAD->typecode to integer; 4b. set c->val to result; The dynamic typing means that there are a lot more steps involved with any operation. This is a primary reason that Python is slow compared to C for operations on numerical data. Is it possible that Python can run faster than C? Why?, Is it true that using Python over a faster language like Java will only slow most websites by to run fast, on the other hand you would like to control them in non-trivial way. The same programming problem was given to different programmers with is built on C) will run many orders faster than a conventional python loop. Of Python’s built-in tools, list comprehension is faster than map(), which is significantly faster than for. For deeply recursive algorithms, loops are more efficient than recursive function calls. How slow is Python really? (Or how fast is your language?), 0.84ms with simple RNG, 1.67ms with c++11 std::knuth So this is 9488 to 4772 times faster depending on what RNG you choose. When using the simple random generator the loops in convolve() run without any memory the constant divide by m1 , I cut the run-time to the same as the C++ answer by Guy Sirton. These benchmarks always reveal that Python is slower than Java, and faster than PHP, and I wonder why that's the case. Java, Python, and PHP run inside a virtual machine All three languages convert their programs into their custom byte codes that run on top of the OS -- so none is running natively
[PDF] Python is only slow, A line of python code is 80-100x slower than a line of C python's standard C API, so C modules don't work or I/O bound processes (particularly disk) are okay for threads because python releases the GIL while they run. the same thing in, say, just about any thread-enabled times in a loop, throwing it away each time. In addition, the slow way of doing things got slower in Python 2.0 with the addition of rich comparisons to the language. It now takes the Python virtual machine a lot longer to figure out how to concatenate two strings. (Don't forget that Python does all method lookup at runtime.) Loops. Python supports a couple of looping constructs.
Comments Related : stackoverflow.com/questions/3033329/… Could the C code be optimised away? Try
volatile int a; in the C version, to prevent the loop being removed.
tried volatile, the same in a += 1, hundreds of times faster than python... The C code will be significantly faster even if compiled completely unoptimized. Because python is a higher level language, there's a lot of computational and representational baggage in python that isn't present in C. A loop void of content exposes a lot of that baggage. @DavidHammen -- I thought I explained that in my second paragraph ... :) I made a bad example here, I don't really mean that a=1 so that compiler could optimize that, I mean the loop itself consumed a lot of resource(maybe I should use a+=1 as example).. And what I mean by optimize is that if the for loop is just that simple, how could it be run in the similar speed as C/C++? In my practice, I used numpy so I can't use pypy anymore, is there some general methods for making loop far more quickly (such as generator in generating list)? @chentingpc -- As I stated in the second paragraph, a for loop in python does a lot of stuff that the C/C++ compiler doesn't. The extra work/time makes it so that python objects can be iterable -- Something very useful that you don't get in C (I'm not sure about C++). Unfortunately, you need to pay for the convenience in performance. As far as optimizing your loop, there's no way to do that in general.
numpy provides a lot of things which could make it so the loop is pushed into C code, but without seeing the code you want to optimize, we can't help any more than this.
@mgilson thank you for your great answers, I have learned a lot. Do you have any books recommended (something like how to use python more efficiently, or how to use python to deal with big data, etc)? oh, I made a bad example here... In my experiment, I use a += 1, python is still far more slower than C/C++. I knew python is high level lang, but I dont know why exactly this code will run slower here? That's not correct, Python code is compiled to bytecode when it's first read, so it has no direct effect on performance of
How C is usually a compiled language? Isn't it always compiled language?