This file describes some special Python build types enabled via
compile-time preprocessor defines.

It is best to define these options in the EXTRA_CFLAGS make variable;
``make EXTRA_CFLAGS="-DPy_REF_DEBUG"``.


---------------------------------------------------------------------------
Py_WITH_INSTRUMENTATION                introduced in Unladen Swallow 2009Q2

If you pass --with-instrumentation to ./configure, Python will collect a
range of runtime data useful for optimizing Python itself. Data currently
collected:
- Stats on the size of generated LLVM IR.
- Stats on the size of emitted machine code.
- Which functions had their machine code disabled due to fatal guard failures,
  but continued to be called.
- How many machine code functions were invalidated by each change to
  globals/builtins dicts.
- Stats on how well LLVM was able to optimize CALL_FUNCTION opcodes.
- Stats on which functions were deemed hot, and how hot they were; cold
  functions will not be shown.
- How many times LLVM code bailed to the interpreter, why it bailed, and the
  opcodes it bailed from.
- Stats on how well LLVM was able to optimize conditional branches.
- Stats on how long LLVM-global gc took and how many globals it collected.
- Stats on how well the JIT was able to specialize/inline binary operators.
- How many feedback maps were created.
- Stats on how well the JIT was able to optimize import statements.
- Stats on how well the JIT was able to optimize certain builtins.
- Data on how long execution blocked for JIT compilation.

This data will be printed to stderr at interpreter-shutdown.

This build currently requires --with-llvm.
---------------------------------------------------------------------------
Py_REF_DEBUG                                              introduced in 1.4
                                                 named REF_DEBUG before 1.4

Turn on aggregate reference counting.  This arranges that extern
_Py_RefTotal hold a count of all references, the sum of ob_refcnt across
all objects.  In a debug-mode build, this is where the "8288" comes from
in

    >>> 23
    23
    [8288 refs]
    >>>

Note that if this count increases when you're not storing away new objects,
there's probably a leak.  Remember, though, that in interactive mode the
special name "_" holds a reference to the last result displayed!

Py_REF_DEBUG also checks after every decref to verify that the refcount
hasn't gone negative, and causes an immediate fatal error if it has.

Special gimmicks:

sys.gettotalrefcount()
    Return current total of all refcounts.
    Available under Py_REF_DEBUG in Python 2.3.
    Before 2.3, Py_TRACE_REFS was required to enable this function.
---------------------------------------------------------------------------
Py_TRACE_REFS                                             introduced in 1.4
                                                named TRACE_REFS before 1.4

Turn on heavy reference debugging.  This is major surgery.  Every PyObject
grows two more pointers, to maintain a doubly-linked list of all live
heap-allocated objects.  Most builtin type objects are not in this list,
as they're statically allocated.  Starting in Python 2.3, if COUNT_ALLOCS
(see below) is also defined, a static type object T does appear in this
list if at least one object of type T has been created.

Note that because the fundamental PyObject layout changes, Python modules
compiled with Py_TRACE_REFS are incompatible with modules compiled without
it.

Py_TRACE_REFS implies Py_REF_DEBUG.

Special gimmicks:

sys.getobjects(max[, type])
    Return list of the (no more than) max most-recently allocated objects,
    most recently allocated first in the list, least-recently allocated
    last in the list.  max=0 means no limit on list length.
    If an optional type object is passed, the list is also restricted to
    objects of that type.
    The return list itself, and some temp objects created just to call
    sys.getobjects(), are excluded from the return list.  Note that the
    list returned is just another object, though, so may appear in the
    return list the next time you call getobjects(); note that every
    object in the list is kept alive too, simply by virtue of being in
    the list.

envar PYTHONDUMPREFS
    If this envar exists, Py_Finalize() arranges to print a list of
    all still-live heap objects.  This is printed twice, in different
    formats, before and after Py_Finalize has cleaned up everything it
    can clean up.  The first output block produces the repr() of each
    object so is more informative; however, a lot of stuff destined to
    die is still alive then.  The second output block is much harder
    to work with (repr() can't be invoked anymore -- the interpreter
    has been torn down too far), but doesn't list any objects that will
    die.  The tool script combinerefs.py can be run over this to combine
    the info from both output blocks.  The second output block, and
    combinerefs.py, were new in Python 2.3b1.
---------------------------------------------------------------------------
PYMALLOC_DEBUG                                            introduced in 2.3

When pymalloc is enabled (WITH_PYMALLOC is defined), calls to the PyObject_
memory routines are handled by Python's own small-object allocator, while
calls to the PyMem_ memory routines are directed to the system malloc/
realloc/free.  If PYMALLOC_DEBUG is also defined, calls to both PyObject_
and PyMem_ memory routines are directed to a special debugging mode of
Python's small-object allocator.

This mode fills dynamically allocated memory blocks with special,
recognizable bit patterns, and adds debugging info on each end of
dynamically allocated memory blocks.  The special bit patterns are:

#define CLEANBYTE     0xCB   /* clean (newly allocated) memory */
#define DEADBYTE      0xDB   /* dead (newly freed) memory */
#define FORBIDDENBYTE 0xFB   /* forbidden -- untouchable bytes */

Strings of these bytes are unlikely to be valid addresses, floats, or 7-bit
ASCII strings.

Let S = sizeof(size_t). 2*S bytes are added at each end of each block of N
bytes requested.  The memory layout is like so, where p represents the
address returned by a malloc-like or realloc-like function (p[i:j] means
the slice of bytes from *(p+i) inclusive up to *(p+j) exclusive; note that
the treatment of negative indices differs from a Python slice):

p[-2*S:-S]
    Number of bytes originally asked for.  This is a size_t, big-endian
    (easier to read in a memory dump).
p[-S:0]
    Copies of FORBIDDENBYTE.  Used to catch under- writes and reads.
p[0:N]
    The requested memory, filled with copies of CLEANBYTE, used to catch
    reference to uninitialized memory.
    When a realloc-like function is called requesting a larger memory
    block, the new excess bytes are also filled with CLEANBYTE.
    When a free-like function is called, these are overwritten with
    DEADBYTE, to catch reference to freed memory.  When a realloc-
    like function is called requesting a smaller memory block, the excess
    old bytes are also filled with DEADBYTE.
p[N:N+S]
    Copies of FORBIDDENBYTE.  Used to catch over- writes and reads.
p[N+S:N+2*S]
    A serial number, incremented by 1 on each call to a malloc-like or
    realloc-like function.
    Big-endian size_t.
    If "bad memory" is detected later, the serial number gives an
    excellent way to set a breakpoint on the next run, to capture the
    instant at which this block was passed out.  The static function
    bumpserialno() in obmalloc.c is the only place the serial number
    is incremented, and exists so you can set such a breakpoint easily.

A realloc-like or free-like function first checks that the FORBIDDENBYTEs
at each end are intact.  If they've been altered, diagnostic output is
written to stderr, and the program is aborted via Py_FatalError().  The
other main failure mode is provoking a memory error when a program
reads up one of the special bit patterns and tries to use it as an address.
If you get in a debugger then and look at the object, you're likely
to see that it's entirely filled with 0xDB (meaning freed memory is
getting used) or 0xCB (meaning uninitialized memory is getting used).

Note that PYMALLOC_DEBUG requires WITH_PYMALLOC.

Special gimmicks:

envar PYTHONMALLOCSTATS
    If this envar exists, a report of pymalloc summary statistics is
    printed to stderr whenever a new arena is allocated, and also
    by Py_Finalize().

Changed in 2.5:  The number of extra bytes allocated is 4*sizeof(size_t).
Before it was 16 on all boxes, reflecting that Python couldn't make use of
allocations >= 2**32 bytes even on 64-bit boxes before 2.5.
---------------------------------------------------------------------------
Py_DEBUG                                                  introduced in 1.5
                                                     named DEBUG before 1.5

This is what is generally meant by "a debug build" of Python.

Py_DEBUG implies LLTRACE, Py_REF_DEBUG, Py_TRACE_REFS, and
PYMALLOC_DEBUG (if WITH_PYMALLOC is enabled).  In addition, C
assert()s are enabled (via the C way: by not defining NDEBUG), and
some routines do additional sanity checks inside "#ifdef Py_DEBUG"
blocks.
---------------------------------------------------------------------------
COUNT_ALLOCS                                            introduced in 0.9.9
                                             partly broken in 2.2 and 2.2.1

Each type object grows three new members:

    /* Number of times an object of this type was allocated. */
    int tp_allocs;

    /* Number of times an object of this type was deallocated. */
    int tp_frees;

    /* Highwater mark:  the maximum value of tp_allocs - tp_frees so
     * far; or, IOW, the largest number of objects of this type alive at
     * the same time.
     */
    int tp_maxalloc;

Allocation and deallocation code keeps these counts up to date.
Py_Finalize() displays a summary of the info returned by sys.getcounts()
(see below), along with assorted other special allocation counts (like
the number of tuple allocations satisfied by a tuple free-list, the number
of 1-character strings allocated, etc).

Before Python 2.2, type objects were immortal, and the COUNT_ALLOCS
implementation relies on that.  As of Python 2.2, heap-allocated type/
class objects can go away.  COUNT_ALLOCS can blow up in 2.2 and 2.2.1
because of this; this was fixed in 2.2.2.  Use of COUNT_ALLOCS makes
all heap-allocated type objects immortal, except for those for which no
object of that type is ever allocated.

Starting with Python 2.3, If Py_TRACE_REFS is also defined, COUNT_ALLOCS
arranges to ensure that the type object for each allocated object
appears in the doubly-linked list of all objects maintained by
Py_TRACE_REFS.

Special gimmicks:

sys.getcounts()
    Return a list of 4-tuples, one entry for each type object for which
    at least one object of that type was allocated.  Each tuple is of
    the form:

        (tp_name, tp_allocs, tp_frees, tp_maxalloc)

    Each distinct type object gets a distinct entry in this list, even
    if two or more type objects have the same tp_name (in which case
    there's no way to distinguish them by looking at this list).  The
    list is ordered by time of first object allocation:  the type object
    for which the first allocation of an object of that type occurred
    most recently is at the front of the list.
---------------------------------------------------------------------------
LLTRACE                                          introduced well before 1.0

Compile in support for Low Level TRACE-ing of the main interpreter loop.

When this preprocessor symbol is defined, before PyEval_EvalFrame
(eval_frame in 2.3 and 2.2, eval_code2 before that) executes a frame's code
it checks the frame's global namespace for a variable "__lltrace__".  If
such a variable is found, mounds of information about what the interpreter
is doing are sprayed to stdout, such as every opcode and opcode argument
and values pushed onto and popped off the value stack.

Not useful very often, but very useful when needed.

---------------------------------------------------------------------------
CALL_PROFILE                                      introduced for Python 2.3

Count the number of function calls executed.

When this symbol is defined, the ceval mainloop and helper functions
count the number of function calls made.  It keeps detailed statistics
about what kind of object was called and whether the call hit any of
the special fast paths in the code.

---------------------------------------------------------------------------
WITH_TSC                                          introduced for Python 2.4

Super-lowlevel profiling of the interpreter.  When enabled, the sys
module grows a new function:

settscdump(bool)
    If true, tell the Python interpreter to dump VM measurements to
    stderr.  If false, turn off dump.  The measurements are based on the
    processor's time-stamp counter.

This build option requires a small amount of platform specific code.
Currently this code is present for linux/x86 or x86_64 and any PowerPC
platform that uses GCC (i.e. OS X and linux/ppc).

On the PowerPC the rate at which the time base register is incremented
is not defined by the architecture specification, so you'll need to
find the manual for your specific processor.  For the 750CX, 750CXe
and 750FX (all sold as the G3) we find:

    The time base counter is clocked at a frequency that is
    one-fourth that of the bus clock.

This build is enabled by the --with-tsc flag to configure. This build currently
requires --with-llvm.

To do something useful with the event timings, run the Misc/tsc_stats.py
script on the output.