Nuitka Release 0.3.9

This is to inform you about the new stable release of Nuitka. It is the extremely compatible Python compiler, “download now”.

This is about the new release of Nuitka which some bug fixes and offers a good speed improvement.

This new release is major milestone 2 work, enhancing practically all areas of Nuitka. The main focus was on faster function calls, faster class attributes (not instance), faster unpacking, and more built-ins detected and more thoroughly optimizing them.

Bug fixes

Exceptions raised inside with statements had references to the exception and traceback leaked.
On Windows the binaries sys.executable pointed to the binary itself instead of the Python interpreter. Changed, because some code uses sys.executable to know how to start Python scripts.
There is a bug (fixed in their repository) related to C++ raw strings and C++ “trigraphs” that affects Nuitka, added a workaround that makes Nuitka not emit “trigraphs” at all.
The check for mutable constants was erroneous for tuples, which could lead to assuming a tuple with only mutable elements to be not mutable, which is of course wrong.

Optimization

This time there are so many new optimization, it makes sense to group them by the subject.

Exceptions

The code to add a traceback is now our own, which made it possible to use frames that do not contain line numbers and a code object capable of lookups.
Raising exceptions or adding to tracebacks has been made way faster by reusing a cached frame objects for the task.
The class used for saving exceptions temporarily (e.g. used in try/finally code, or with statement) has been improved.

It now doesn’t make a copy of the exception with a C++ new call, but it simply stores the exception properties itself and creates the exception object only on demand, which is more efficient.
When catching exceptions, the addition of tracebacks is now done without exporting and re-importing the exception to Python, but directly on the exception objects traceback, this avoids a useless round trip.

Function Calls

Uses of PyObject_Call provide NULL as the dictionary, instead of an empty dictionary, which is slightly faster for function calls.
There are now dedicated variants for complex function calls with * and ** arguments in all forms.

These can take advantage of easier cases. For example, a merge with star arguments is only needed if there actually were any of these.
The check for non-string values in the ** arguments can now be completely short-cut for the case of a dictionary that has never had a string added. There is now code that detects this case and skips the check, eliminating it as a performance concern.

Parameter Parsing

Reversed the order in which parameters are checked.

Now the keyword dictionary is iterated first and only then the positional arguments after that is done. This iteration is not only much faster (avoiding repeated lookups for each possible parameter), it also can be more correct, in case the keyword argument is derived from a dictionary and its keys mutate it when being compared.
Comparing parameter names is now done with a fast path, in which the pointer values are compare first. This can avoid a call to the comparison at all, which has become very likely due to the interning of parameter name strings, see below.
Added a dedicated call to check for parameter equality with rich equality comparison, which doesn’t raise an exception.
Unpacking of tuples is now using dedicated variants of the normal unpacking code instead of rolling out everything themselves.

Attribute Access

The class type (in executables, not yet for extension modules) is changed to a faster variant of our own making that doesn’t consider the restricted mode a possibility. This avoids very expensive calls, and makes accessing class attributes in compiled code and in non-compiled code faster.
Access to attributes (but not of instances) got in-lined and therefore much faster. Due to other optimization, a specific step to intern the string used for attribute access is not necessary with Nuitka at all anymore. This made access to attributes about 50% faster which is big of course.

Constants

The bug for mutable tuples also caused non-mutable tuples to be considered as mutable, which lead to less efficient code.
The constant creation with the g++ bug worked around, can now use raw strings to create string constants, without resorting to un-pickling them as a work around. This allows us to use PyString_FromStringAndSize to create strings again, which is obviously faster, and had not been done, because of the confusion caused by the g++ bug.
For string constants that are usable as attributes (i.e. match the identifier regular expression), these are now interned, directly after creation. With this, the check for identical value of pointers for parameters has a bigger chance to succeed, and this saves some memory too.
For empty containers (set, dict, list, tuple) the constants created are now are not unstreamed, but created with the dedicated API calls, saving a bit of code and being less ugly.
For mutable empty constant access (set, dict, list) the values are no longer made by copying the constant, but instead with the API functions to create new ones. This makes code like a = [] a tiny bit faster.
For slice indices the code generation now takes advantage of creating a C++ Py_ssize_t from constant value if possible. Before it was converting the integer constant at run time, which was of course wasteful even if not (very) slow.

Iteration

The creation of iterators got our own code. This avoids a function call and is otherwise only a small gain for anything but sequence iterators. These may be much faster to create now, as it avoids another call and repeated checks.
The next on iterator got our own code too, which has simpler code flow, because it avoids the double check in case of NULL returned.
The unpack check got similar code to the next iterator, it also has simpler code flow now and avoids double checks.

Built-ins

Added support for the list, tuple, dict, str, float and bool built-ins along with optimizing their use with constant parameter.
Added support for the int and long built-ins, based on a new “call spec” object, that detects parameter errors at compile time and raises appropriate exceptions as required, plus it deals with keyword arguments just as well.

So, to Nuitka it doesn’t matter now it you write int(value) ``or ``int(x = value) anymore. The base parameter of these built-ins is also supported.

The use of this call spec mechanism will the expanded, currently it is not applied to the built-ins that take only one parameter. This is a work in progress as is the whole built-ins business as not all the built-ins are covered yet.

Cleanups

In 0.3.8 per module global classes were introduced, but the IMPORT_MODULE kept using the old universal class, this got resolved and the old class is now fully gone.
Using assertObject in more cases, and in more places at all, catches errors earlier on.
Moved the addition to tracebacks into the _PythonException class, where it works directly on the contained traceback. This is cleaner as it no longer requires to export exceptions to Python, just to add a traceback entry.
Some PyLint cleanups were done, reducing the number of reports a bit, but there is still a lot to do.
Added a DefaultValueIdentifier class that encapsulates the access to default values in the parameter parsing more cleanly.
The module CodeTemplatesListContractions was renamed to CodeTemplatesContractions to reflect the fact that it deals with all kinds of contractions (also set and dict contractions), not just list contractions.
Moved the with related template to its own module CodeTemplatesWith, so its easier to find.
The options handling for g++ based compilers was cleaned up, so that g++ 4.6 and MinGW are better supported now.
Documented more aspects of the Scons build file.
Some more generated code white space fixes.
Moved some helpers to dedicated files. There is now calling.hpp for function calls, an importing.cpp for import related stuff.
Moved the manifest generation to the scons file, which now produces ready to use executables.

New Tests

Added a improved version of “pybench” that can cope with the “0 ms” execution time that Nuitka has for some if its sub-tests.
Reference counting test for with statement was added.
Micro benchmarks to demonstrate try finally performance when an exception travels through it.
Micro benchmark for with statement that eats up exceptions raised inside the block.
Micro benchmarks for the read and write access to class attributes.
Enhanced Printing test to cover the trigraphs constant bug case. Output is required to make the error detectable.
Enhanced Constants test to cover repeated mutation of mutable tuple constants, this covers the bug mentioned.

Organisational

Added a credits section to the “README.txt” where I give credit to the people who contributed to Nuitka, and the projects it is using. I will make it a separate posting to cite these.
Documented the requirements on the compiler more clearly, document the fact that we require scons and which version of Python (2.6 or 2.7).
The is now a codespeed implementation up and running with historical data for up to Nuitka 0.3.8 runs of “PyStone” and with pybench. It will be updated for 0.3.9 once I have the infrastructure in place to do that automatically.
The cleanup script now also removes .so files.
The handling of options for g++ got improved, so it’s the same for g++ and MinGW compilers, plus adequate errors messages are given, if the compiler version is too low.
There is now a --unstripped option that just keeps the debug information in the file, but doesn’t keep the assertions.

This will be helpful when looking at generated assembler code from Nuitka to not have the distortions that --debug causes (reduced optimization level, assertions, etc.) and instead a clear view.

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