Today I knew that pypy3 is faster than python3 for input() time through any algorithm problem. Performance differences were almost as much as 12 times.
Why is there such a difference?
Kindly check this, when we speak of Python programming language we often mean not just the language but also the implementation. Python is a specification for a language that can be implemented in many different ways.
The default implementation of the Python programming language is Cpython(assuming python3 you mean Cpython). As the name suggests Cpython is written in C language. Cpython compiles the python source code into intermediate bytecode, which is executed by the Cpython virtual machine.
Jython is an implementation of the Python programming language that can run on the Java platform. Jython programs use Java classes instead of Python modules. Jython compiles into Java byte code, which can then be run by Java virtual machine.
PyPy
If you want your code to run faster, you should probably just use PyPy. — Guido van Rossum (creator of Python)
Python is a dynamic programming language. Python is said to be slow as the default CPython implementation compiles the python source code in bytecode which is slow as compared to machine code(native code). Here PyPy comes in.
PyPy is an implementation of the Python programming language written in Python. The Interpreter is written in RPython (a subset of Python).
PyPy uses Just In Time (JIT) compilation. In simple terms, JIT uses compilation methods to make the interpreter system more efficient and fast. So basically JIT makes it possible to compile the source code into native machine code which makes it very fast.
PyPy also comes with default support for stackless mode, providing micro-threads for massive concurrency. It is said to be approximately 7.5 times faster than Cpython.
Hope this will help you.
CPython
It is interpreter for python language written in C and C++. Interpreter convert python code (which is written by humans and can be read by humans) to machine code (which can be read/understood by machines). This process involve various steps.
CPython is the reference implementation of Python, written in C. It compiles Python code to intermediate bytecode which is then interpreted by a virtual machine. CPython provides the highest level of compatibility with Python packages and C extension modules.
If you are writing open source Python code and want to reach the widest possible audience, targeting CPython is best. To use packages which rely on C extensions to function, CPython is your only implementation option.
All versions of the Python language are implemented in C because CPython is the reference implementation.
PyPy
It is JIT compiler for python language written in RPython. JIT compiler execute code which require compilation, i.e. JIT compile code at runtime, just before executing it.
PyPy is a Python interpreter implemented in a restricted statically-typed subset of the Python language called RPython. The interpreter features a just-in-time compiler and supports multiple back-ends (C, CLI, JVM).
PyPy aims for maximum compatibility with the reference CPython implementation while improving performance.
If you are looking to increase performance of your Python code, it’s worth giving PyPy a try. On a suite of benchmarks, it’s currently over 5 times faster than CPython.
PyPy supports Python 2.7. PyPy3, released in beta, targets Python 3.
I assume that when you say python3, you mean CPython which default and widely used implementation of python language.
CPython
It is interpreter for python language written in C and C++. Interpreter convert python code(which is written by human and can read by human) to machine code(which can read/understand by machine/computer). This process involve various steps.
PyPy
It is JIT compiler for python language written in RPython. JIT compiler execute code which require compilation, i.e. JIT compile code at runtime, just before executing it.
This different approach of handling python code of these two implementation is reason behind different int speed. Below link will give you more detail on this.
There are few more implementation of Python language which aims to achieve different goal.
CPython
PyPy
JIT
Design of CPython’s Compiler
PyPy
Alternatives
Related
So I'm investigating how to create a language compiler using Truffle. Let's just say for the purpose of this question that the language is called Emerald.
Emerald is a statically compiled language, and it runs on the JVM, just like Java.
The compiler for Emerald would be a program called emeraldc. The compiler emeraldc will compile source files like Hello.emerald to Hello.class.
I've not found any examples of using Truffle to create such a language. All language examples I've found are interpreted languages. None seem to compile to class files for example.
With GraalVM's Truffle framework languages are specifically implemented as interpreters but you can still get a compiler.
Languages are usually not intrinsically compiled or interpreted (you can interpret C and compile Javascript for example). There are even cases using a mix of both: for example your Emerald compiler compiles from emerald to Java bytecodes which can in turn be interpreted in a Java Virtual Machine and compiled Just-In-Time.
With the GraalVM's Truffle framework the typical setup is that you implement an interpreter for your language and GraalVM will give you a JIT compiler through partial evaluation of your interpreter. You might want to check this introduction.
If you want compilation Ahead-Of-Time, Truffle also has support for that.
However there is currently no configuration in which the output AOT or JIT compilation would be Java bytecodes.
This question already has answers here:
Python vs Cpython
(11 answers)
Closed 1 year ago.
I heard about python only but when I see different names on search result than I confused what the hell is this but I taught may be it is used in the part of code conversion but not sure about it what actually these are ?
Python is a language. CPython, IronPython, Jython are different implementations of this langauge. They also all happen to be implemented in different languages themselves: CPython is written in C, IronPython in .NET, and Jython in Java. Thus, it is very easy to integrate IronPython into a .NET program, and it is really easy to embed Jython into a Java program. But for the most part, when people execute Python, they are running their Python code through CPython (even if they don't know that's what it's officially called). It is the original Python implementation, it is the fastest of them, as well as being the implementation that defines what Python is. Unless you want to use Python within .NET or Java frameworks, you might never encounter the other two.
I really admire the functionality of Stackless Python, and I've been looking around for a way to emulate its syntax while still using the standard Python 3 interpreter. An article by Alex J. Champandard in a gamedev blog made it look as though the greenlet library could provide this functionality. I slightly modified his code, but the best makeshift tasklet wrapper I could come up with was a class holding a greenlet inside a variable, as such:
class tasklet():
def __init__(self,function=None,*variables):
global _scheduled
self.greenlet = greenlet.greenlet(function,None)
self.functioncall = function # Redundant backup
self.variables = variables
_scheduled.append(self)
self.blocked = False
The function then emulates Stackless' scheduling by passing the variables to the greenlet when calling its switch() method.
So far this appears to work, but I'd like to be able to call the tasklets in original Stackless syntax, e.g. tasklet(function)(*args), as opposed to the current syntax of tasklet(function,*args). I'm not sure where to look in the documentation to find out how to accomplish this. Is this even possible, or is it part of Stackless' changes to the interpreter?
According to this article from 2010-01-08 (with fixed links):
Stackless Python is an extended version of the Python language (and
its CPython reference implementation). New features include
lightweight coroutines (called tasklets), communication primitives
using message passing (called channels), manual and/or automatic
coroutine scheduling, not using the C stack Python function calls, and
serialization of coroutines (for reloading in another process).
Stackless Python could not be implemented as a Python extension module
– the core of the CPython compiler and interpreter had to be patched.
greenlet is an extension module to CPython providing coroutines and
low-level (explicit) scheduling. The most important advantage of
greenlet over Stackless Python is that greenlet could be implemented
as a Python extension module, so the whole Python interpreter doesn't
have to be recompiled in order to use greenlet. Disadvantages of
greenlet include speed (Stackless Python can be 10%, 35% or 900%
faster, depending on the workflow); possible memory leaks if
coroutines have references to each other; and that the provided
functionality is low-level (i.e. only manual coroutine scheduling, no
message passing provided).
greenstackless, the Python module I've recently developed, provides
most of the (high-level) Stackless Python API using greenlet, so it
eliminates the disadvantage of greenlet that it is low-level. See the
source code and some tests (the latter with tricky corner cases).
Please note that although greenstackless is optimized a bit, it can be
much slower than Stackless Python, and it also doesn't fix the memory
leaks. Using greenstackless is thus not recommended in production
environments; but it can be used as a temporary, drop-in replacement
for Stackless Python if replacing the Python interpreter is not
feasible.
Some other software that emulates Stackless using greenlet:
Concurrence: doesn't support stackless.main, tasklet.next,
tasklet.prev, tasklet.insert, tasklet.remove,
stackless.schedule_remove, doesn't send exceptions properly. (Because
of these features missing, it doesn't pass the unit test above.)
PyPy: doesn't support stackless.main, tasklet.next, tasklet.prev,
doesn't pass the unit test above.
Is there a programming language, having usable interactive interpreter, even as it can be compiled to machine code?
Compilation vs. "interpretation" is essentially a matter of implementation, not the language itself. For example, MRI Ruby 1.8 is interpreted, while MacRuby is compiled to native machine code. Both include an interactive REPL. All the languages I know that have at least one machine-code compiler and at least one REPL:
Ruby
Python
Almost all Lisps (Lisp was the language that pioneered this technique, AFAIK)
OCaml
Haskell
Forth
If we're counting compilation to bytecode as well as machine code, it's true of the vast majority of popular bytecode-compiled languages:
Java
Scala
Groovy
Erlang
C#
F#
Smalltalk
Haskell, using the Glasgow Haskell Compiler which has an interactive "shell" called GHCi.
Many flavors of Lisp offer both options, including Clojure.
Two come to my mind : ocaml and scala (~= java), but I'm sure there must be a lot more out there.
And here's another one to burn your house down:
x86 Assembly
Yup, there are interpreters for this as well.
Javascript x86 Assembly Interpreter
Jasmin
At this point you're really in emulator land, but it does meet the requirements you state.
I'm wondering if it's easier to name compiled languages that someone hasn't cobbled up a working interpreter for. :-)
Lua has an interactive mode for one-liners and experimentation. It normally compiles to bytecode for its VM for execution. LuaJIT is an independent implementation of a Lua VM that also does just-in-time compilation to 32-bit x86. Support for 64-bit is underway, and support for ARM is frequently requested.
Compilation to a bytecode is often a reasonable compromise between a pure interpreter and a pure compiler. The VM can be tuned to the needs of the language, and JIT techniques can analyze the VM code as it executes and concentrate on frequently executed code paths and inner loops.
As others have mentioned, OCaml.
If managed code (.NET CLI) is close enough to machine code, F# would be a candidate as well. There are probably other .NET/Mono languages which meet the requirement as well.
You may regret you asked:
C and C++.
Why?
Ch
CINT
EIC
picocc
and there are probably others out there as well.
Plenty of languages offer an implementation that both interacts and compiles to machine code, but it's rare to do both at once. Standard ML of New Jersey is one that has an interactive loop but no bytecode: it simply compiles to machine code in memory and then branches to it.
Not exactly machine code, but Java can be compiled and also used via BeanShell.
I've used Ruby with an interpreter, and there seems to be a compiler here.
Icon used to have a compiler, but it falls in and out of maintenence. It may still work.
Python can be compiled to windows executables.
C# can be compiled by using SnippetCompiler, maybe this would act as an interactive interpreter for you?
Your question is a bit vague. Even Java would fit it:
by interactive interpreter, i mean
shell-like environment, where you can
work in the runtime interactively.
Java has this, e.g. in the Eclipse "scrapbook pages", where you can enter Java expressions and have them evaluated right away. Java is of course also a compiled language (and while it's usually compiled to bytecode, there are various compilers that output machine code).
So what are you looking for? Maybe you could explain your problem or interest.
I tried using mono/.net for a bit and found random GC pauses to be disagreeable (at least on my crusty old laptop). I looked at using gambit-c an implementation of scheme that can compile to C but it seemed difficult to work with because the docs were somewhat limited and the packages where not very easy to install and use.
I usually just stick to having an interpreted language such as python bound to C/C++ which is more painful but at least I know what I am in for.
Can anyone point to programming language which has python-like syntax, but from the very beginning was designed to generate native code? I'm aware of Boo only, but it uses .net, not native code generation. Well, if nothing else than python-like languages which generate .net/java bytecode are fine too.
Cython might do -- the C code it generates is for Python extensions, but the whole thing can be packaged up and you'll be running native code throughout (after the 'import';-).
I must admit that I don't quite understand your question, for two reasons:
You are asking for a language with native code generation, but native code generation has nothing to do with the language, it is a trait of the implementation. Every language can have an implementation with native code generation. Several Python implementations have native code generation. There are C compilers that compile to JVM bytecode, CIL bytecode or even ECMAScript sourcecode. There are even C interpreters. There are also compilers that compile Java sourcecode or JVM bytecode to native code.
Why do you care about the syntax? It is probably the least important factor about choosing a programming language.
Anyway, Nim is a programming language which has an implementation which supports native code generation (or more precisely an implementation which supports C source code generation) and whose syntax is a hybrid between Wirthian style (by the looks of it the most important influences are Oberon and Delphi) and Python.
However, the fact that it has Pythonic syntax isn't going to help you at all if you don't like European style language design or Wirthian style OOP.
Also found today Delight applying Python syntax on a D back-end.
And Converge too.
Check out Cobra
It is strongly influenced by Python, C#, Eiffel, Objective-C and other programming languages. It supports both static and dynamic typing. It has first class support for unit tests and contracts. Cobra provides both rapid development and performance in the same language.
shedskin compiles Python to C++
From shedskin project page
Shed Skin is an experimental compiler,
that can translate pure, but
implicitly statically typed Python
programs into optimized C++. It can
generate stand-alone programs or
extension modules that can be imported
and used in larger Python programs.
Genie which is part of the gnome project: http://live.gnome.org/Genie
I think it's exactly what you're looking for.
If you are happy with something that compiles down to Java bytecode you could have a look at Jython. Quoting from their FAQ:
JPython is an implementation of the Python programming language which is designed to run on the Java(tm) Platform. It consists of a compiler to compile Python source code down to Java bytecodes which can run directly on a JVM, a set of support libraries which are used by the compiled Java bytecodes, and extra support to make it trivial to use Java packages from within JPython.
I've not actually used it yet but am considering it on some projects where I have to integrate with existing an Java codebase.
HTH
PyPy is a project to re-implement Python in Python. One of it's goals is to allow the use of multiple back-ends, including C. So you can take a pure Python program, convert it to C and compile it to native code. It is still a work in progress, so probably not suitable for production code.
You can find all of the previously mentioned languages, plus some more, here: http://wiki.python.org/moin/PythonImplementations
Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula.
https://nim-lang.org/
You can also investigate IronPython - a python inplementation on the .NET framework
You can try Genie. It's the same like Vala, but with Python-like syntax. If you want to develop apps for Linux with GTK, and you want to compile it to native app, Vala or Genie is really good choice.