1. Pyjion

2. What are we going to discuss
 When you develop a Python app, what happens when you execute it
 Why does that happen and how does it work?
 How can it be improved?
 What is a JIT
 What is Pyjion

3. Abstract Syntax Trees
 Clone https://github.com/quantifiedcode/python-ast-visualizer
 pip install –r requirements.txt
 cat example.py | python astvisualizer.py
 Uses built in module ast and function compile

4. What is ByteCode
 Machine code is the lowest level but not portable between processors
 Assembly is short-hand for machine code
 C is compiled and linked into machine-code which is efficient but not portable
once compiled
 Contrast Jython to Cpython, Jython compiles the AST to JVM bytecode,
Cpython to Cpython bytecode (pyc files)

5. cPython bytecode
 The cPython compiler reads the text (command, file etc)
 Prepares Abstract Syntax Tree
 The cPython compiler then converts the AST into Bytecode
 Bytecode operations are low level operations that translate to likely
OS/machine level instructions.

6. Using DIS to inspect byte-code
def helloworld():
print("Hello world!")
dis.dis(helloworld)
2 0 LOAD_GLOBAL 0 (print)
3 LOAD_CONST 1 ('Hello world!')
6 CALL_FUNCTION 1 (1 positional, 0 keyword pair)
9 POP_TOP
10 LOAD_CONST 0 (None)
13 RETURN_VALUE
Using the standard library ‘dis’
module, you can disassemble a
Python object into byte-code in
the Python CLI.
https://github.com/python/cpython/blob/ab8b3ff250a1
3d506f572abd66c37bce0759ab7d/Lib/dis.py#L31-L65

7. Python threads and the Global
Interpreter Lock
 Python has native support for threading
 Python threads are real system threads (either POSIX or Windows)
 Those threads are managed by the host operating system
 Python threads are simple callable objects
 The GIL ensures that sure each thread gets exclusive access to the interpreter
internals when it's running (and that call-outs to C extensions play nice)
 See: http://www.dabeaz.com/python/GIL.pdf

8. How a Python thread executes
Thread created
Python creates
data structure
(PyThreadState)
for thread
The pthread is
launched
The thread calls
PyEval_CallObject
(run the callable)

9. Just-in-time compilation
 Just-in-time compilation requires an intermediate
language (IL) to allow the code to be split into
chunks (or frames)
 Ahead of time (AOT) compilers are designed to
ensure that, the CPU can understand every line in
the code before any interaction takes place.
 In the case of Python, the execution is converted to
byte-code when called. Those byte-code statements
are cached in .pyc files.
 This diagram shows the .NET MSIL to JIT compiler
flow

10. .NET core
 Microsoft.NET is a JIT framework, with a series of languages supported by
Microsoft and interopability with the common MSIL intermediate language.
The .NET runtime is the MSIL JIT compilation module, but the executable and
installer includes 100’s of modules specific for Windows and Windows
application development.
 Microsoft have redeveloped .NET into a new lightweight framework focusing
on the core elements (JIT and compilation) as well as the core data structures
and common classes. This is developed in C++ and is cross-platform.
 The JIT module itself in .NET core can be used without the MSIL purely for
executing byte-code operators on the local CPU.
 This is what Pyjion does.

11. Python CodeObjects
 The goal of the JIT is to convert Python Code Objects (either loaded via
imports, on the CLI or in a thread) to machine instructions.
 The low level data structure is called PyCodeObject, declared in Cpython.
 Pyjion introduces a new abstract type, IPythonCompiler, which takes a pointer
to a PyCodeObject and executes (compiles) the code against a target
ILGenerator
 The ILGenerator generates DotNetCore IL instructions for each Python code

12. End to end workflow
Python creates
PyCodeObject
from code
file/input
PythonCompiler
takes
PyCodeObject
and converts
Python OpCodes
into
intermediate
instructions
(Pyjion IL?)
ILGenerator
converts
abstract IL
operations to
send to the
ICorJitCompiler
ICorJitCompiler
compiles stacks
to machine code
ICorJitInfo is the main
interface that the JIT uses
to call back to the
execution engine (EE) and
get information.
DotNetCorePyjionPython
print(‘hello world!’)
010101110011

13. Proposed extensions to CPython
 In order to support Pyjion (or another JIT engine) some
changes are proposed to Cpython
 Load pyjit.dll and run ‘jitInit’ (pylifecycle.cpp)
 JitInitFunction jitinit =
(JitInitFunction)GetProcAddress(pyjit,
"JitInit"); jitinit();
 Ceval (the code that evaluates the .pyc opcode cache)
can now ask the JIT module to evaluate frames

14. Does this help me run .NET from Python
or vice versa?
 No.
 .NET is not only an intermediate-language and execution
engine, but also a huge collection of standard libraries
(similar to Python).
 Pyjion bridges the gap between the low-level function
that CPython performs executing byte-code operations on
the local CPU and replaces the last step with a 3rd party
execution engine.

15. Building the project
 You will need
 Git, SVN
 Cmake
 Visual Studio (well MSBuild)
1. Download Pyjion (github.com/Microsoft/Pyjion)
2. Install submodules
3. Apply patches to CoreCLR and Python
4. Compile Pyjion
5. Copy build output to the CPython distribution output
(pyjit.dll)

16. About me
@anthonypjshaw
@tonybaloney
anthonyshaw@apache.org
Head of Innovation at Dimension Data