# Goals and Architecture Overview¶

## High Level Goals¶

Traditionally, language interpreters are written in a target platform language such as C/Posix, Java or C#. Each implementation provides a fundamental mapping between application source code and the target environment. One of the goals of the “all-encompassing” environments, such as the .NET framework and to some extent the Java virtual machine, is to provide standardized and higher level functionalities in order to support language implementers for writing language implementations.

PyPy is experimenting with a more ambitious approach. We are using a subset of the high-level language Python, called RPython Language, in which we write languages as simple interpreters with few references to and dependencies on lower level details. The RPython toolchain produces a concrete virtual machine for the platform of our choice by inserting appropriate lower level aspects. The result can be customized by selecting other feature and platform configurations.

Our goal is to provide a possible solution to the problem of language implementers: having to write l * o * p interpreters for l dynamic languages and p platforms with o crucial design decisions. PyPy aims at making it possible to change each of these variables independently such that:

• l: the language that we analyze can be evolved or entirely replaced;
• o: we can tweak and optimize the translation process to produce platform specific code based on different models and trade-offs;
• p: we can write new translator back-ends to target different physical and virtual platforms.

By contrast, a standardized target environment - say .NET - enforces p=1 as far as it’s concerned. This helps making o a bit smaller by providing a higher-level base to build upon. Still, we believe that enforcing the use of one common environment is not necessary. PyPy’s goal is to give weight to this claim - at least as far as language implementation is concerned - showing an approach to the l * o * p problem that does not rely on standardization.

The most ambitious part of this goal is to generate Just-In-Time Compilers in a language-independent way, instead of only translating the source interpreter into an interpreter for the target platform. This is an area of language implementation that is commonly considered very challenging because of the involved complexity.

## Architecture¶

The job of the RPython toolchain is to translate RPython Language programs into an efficient version of that program for one of the various target platforms, generally one that is considerably lower-level than Python.

The approach we have taken is to reduce the level of abstraction of the source RPython program in several steps, from the high level down to the level of the target platform, whatever that may be. Currently we support two broad flavours of target platforms: the ones that assume a C-like memory model with structures and pointers, and the ones that assume an object-oriented model with classes, instances and methods (as, for example, the Java and .NET virtual machines do).

The RPython toolchain never sees the RPython source code or syntax trees, but rather starts with the code objects that define the behaviour of the function objects one gives it as input. It can be considered as “freezing” a pre-imported RPython program into an executable form suitable for the target platform.

The steps of the translation process can be summarized as follows:

• The code object of each source functions is converted to a control flow graph by the flow graph builder.
• The control flow graphs are processed by the Annotator, which performs whole-program type inference to annotate each variable of the control flow graph with the types it may take at run-time.
• The information provided by the annotator is used by the RTyper to convert the high level operations of the control flow graphs into operations closer to the abstraction level of the target platform.
• Optionally, various transformations <optional-transformations> can then be applied which, for example, perform optimizations such as inlining, add capabilities such as stackless-style concurrency, or insert code for the garbage collector.
• Then, the graphs are converted to source code for the target platform and compiled into an executable.

This process is described in much more detail in the document about the RPython toolchain and in the paper Compiling dynamic language implementations.