#Sg project pro and dropbox code#

A number of scientific packages for Python, such as Scikit-learn, draw on Cython features like this to keep operations lean and fast. That said, Cython provides several advantages for the sake of speed not available in vanilla Python, among them variable typing à la C itself.

#Sg project pro and dropbox code#

But Cython can also be used to incrementally accelerate Python functions, chiefly ones that perform math. The downside is that Cython uses its own peculiar syntax to work its magic, so porting existing code isn’t totally automatic. It’s one way to write C extensions for Python, which wrap C or C++ code and give it an easy Python interface. CythonĬython (C extensions for Python) is a superset of Python, a version of the language that compiles to C and interfaces with C/C++ code. Long-term plans for Nuitka include allowing Nuitka-compiled Python to interface directly with C code, allowing for even greater speed. Case in point: Nuitka, which converts Python to C++ code-and can automatically pack up all of the files needed from the CPython runtime to boot.

Rather than replace the Python runtime, some teams are doing away with a Python runtime entirely and seeking ways to transpile Python code to languages that run natively at high speed. Pyston’s speedups are not very dramatic yet-about 20% faster, on average-but the project is still very much in its infancy. The rewrite also uses CPython code as the basis for the project, so it’s far more compatible out-of-the-box with conventional Python.

Its original incarnation used the LLVM compiler infrastructure to do this, but the rewrite dropped LLVM in favor of a hand-rolled assembler with much lower overhead. The Pyston project, originally created by Dropbox but since relaunched and rewritten, also uses a JIT to speed up Python. And its executable has a much larger footprint than CPython. It’s best for long-running programs like servers, rather than one-and-done scripts, as its performance benefits don’t really register until after some warmup time. However, recent releases go a long way towards addressing this problem. Although PyPy used to favor Python 2 over Python 3, the most recent versions of PyPy support Python 3.6 and Python 3.7 as well as Python 2.7.Īnother long-standing drawback was that PyPy didn’t integrate well with common libraries used to accelerate Python performance, such as NumPy. PyPy uses just-in-time (JIT) compilation, the same technique used by Google Chrome’s V8 JavaScript engine to speed up that language. It also stands the best chance of becoming the default, as it’s highly compatible with existing Python code. PyPyĪmong the candidates for a drop-in replacement for CPython, PyPy is easily the most visible (Quora, for instance, uses it in production). Each uses one of these two approaches, or a combination of the two. Here are six ways the bar on Python performance is being raised.

You can rewrite existing Python code to take advantage of certain speed optimizations, which means more work for the programmer but doesn’t require changes in the runtime.

You can create a replacement for the default runtime used by the language (the CPython implementation)-a major undertaking, but the result would be a drop-in replacement for CPython.

If you want to make Python run faster on the same hardware, you have two basic options, each with a drawback: But several projects refuse to ditch all that’s good about Python and instead have decided to boost its performance from the inside out. Spiffy and convenient as Python is, most everyone who uses the language knows it’s comparatively creaky-orders of magnitude slower than C, Java, or JavaScript for CPU-intensive work.