This section describes performance issues from the point of view of insiders of the project; it should be particularly interesting if you plan to contribute in that area. One of the goals of the PyPy project is to provide a fast and compliant python interpreter. Some of the ways we achieve this are by providing a high-performance garbage collector GC and a high-performance Just-in-Time compiler JIT.
Results of comparing PyPy and CPython can be found on the speed website. Those benchmarks are not a random collection: they are a combination of real-world Python programs benchmarks originally included with the now dead Unladen Swallow project and benchmarks for which we found PyPy to be slow and improved. Consult the descriptions of each for details. The JIT, however, is not a magic bullet. The JIT is generally good at speeding up straight-forward Python code that spends a lot of time in the bytecode dispatch loop, i.
Good examples include numeric calculations or any kind of heavily object-oriented program. Bad examples include doing computations with large longs which is performed by unoptimizable support code. Tests : The ideal unit tests execute each piece of tested code once. This leaves no time for the JIT to warm up. Really short-running scripts : A rule of thumb is if something runs below 0. In general, make sure you warm up your program before running benchmarks, if you're measuring something long-running like a server.
The time required to warm up the JIT varies; give it at least a couple of seconds. PyPy's JIT takes an especially long time to warm up. Long-running runtime functions : These are the functions provided by the runtime of PyPy that do a significant amount of work.
PyPy's runtime is generally not as optimized as CPython's and we expect those functions to take somewhere between the same time as CPython to twice as long. This includes, for example, computing with longs, or sorting large lists.
A counterexample is regular expressions: although they take time, they come with their own JIT. PyPy supports C extension modules solely to provide basic functionality. If the extension module is for speedup purposes only, then it makes no sense to use it with PyPy at the moment. Instead, remove it and use a native Python implementation, which also allows opportunities for JIT optimization.
If the extension module is both performance-critical and an interface to some C library, then it might be worthwhile to consider rewriting it as a pure Python version that uses CFFI for the interface. Sometimes the JIT is able to do a good job on them, and sometimes not. In most cases like csv and cPickle , we're slower than CPython, with the notable exception of json and heapq. The PyPy interpreter is implemented in RPython which is a statically typed subset of Python the language and not the CPython interpreter.
That would depend on the implementation of these hypothetical interpreters. If one of them for example took the source, did some kind of analysis on it and converted it directly into tight target specific assembly code after running for a while, I imagine it would be quite faster than CPython.
Update: Recently, on a carefully crafted example , PyPy outperformed a similar C program compiled with gcc -O3. It's a contrived case but does exhibit some ideas. From the official site. By separating concerns in this way, our implementation of Python - and other dynamic languages - is able to automatically generate a Just-in-Time compiler for any dynamic language.
It also allows a mix-and-match approach to implementation decisions, including many that have historically been outside of a user's control, such as target platform, memory and threading models, garbage collection strategies, and optimizations applied, including whether or not to have a JIT in the first place. The reason to implement PyPy on top of Python is probably that it is simply a very productive language, especially since the JIT compiler makes the host language's performance somewhat irrelevant.
PyPy is written in Restricted Python. It does not run on top of the CPython interpreter, as far as I know. Restricted Python is a subset of the Python language. AFAIK, the PyPy interpreter is compiled to machine code, so when installed it does not utilize a python interpreter at runtime. Your question seems to expect the PyPy interpreter is running on top of CPython while executing code.
Edit: Yes, to use PyPy you first translate the PyPy python code, either to C and build with gcc, to jvm byte code, or to. Net CLI code. See Getting Started. How are we doing? Please help us improve Stack Overflow. Take our short survey. Stack Overflow for Teams — Collaborate and share knowledge with a private group. Create a free Team What is Teams? Collectives on Stack Overflow.
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For instance, you could write performance-intensive tasks in C and wrap it with Python; many machine learning libraries do exactly this. Or you could use Cython , a project that lets you sprinkle Python code with runtime type information that allows it to be compiled to C. But workarounds are never ideal. PyPy is a drop-in replacement for the stock Python interpreter, CPython. Whereas CPython compiles Python to intermediate bytecode that is then interpreted by a virtual machine, PyPy uses just-in-time JIT compilation to translate Python code into machine-native assembly language.
Depending on the task being performed, the performance gains can be dramatic. On the average, PyPy speeds up Python by about 7. The best part is that little to no effort is required on the part of the developer to unlock the gains PyPy provides.
PyPy currently supports both Python 2 and Python 3, by way of different incarnations of the project.
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