Virtual Machine Design And Implementation C C++ Pdf


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In this paper, we explain, through two concrete VMs, how meta-tracing RPython VMs can be designed and optimised, and, experimentally, the performance levels one might reasonably expect from them. Introduction Every programming language that makes it beyond a paper design needs a corresponding implementation. Traditionally, most languages were compiled to machine code via assembler or, less commonly, C. For languages with high static content i. However, languages with high dynamic content i.

In short, the traditional approach is generally either too costly, or leads to slow execution. For many modern languages, it is both.

Implemented as a naive interpreter, a VM will tend to have poor performance. Instead, a VM can use information about the way the program executes to optimise it in ways that are statically impossible. However, a VM reflects the language, or group of languages, it was designed for. Thus, languages which do not fit an existing VM mould must look to a custom VM to achieve good performance levels.

However, this is not easily achieved: implementing a performant VM is justifiably seen as requiring highly specialized skills and significant manpower. In particular, the highest performing VMs rely on Just-In-Time compilers henceforth referred to as JITs , which, at run-time, take part of a program and convert it to optimised machine code. In this paper, we consider the RPython language, which allows automatic creation of JITing VMs from implementations of traditional interpreters through meta-tracing.

This allows high-performance custom VMs to be created with reasonable resources. Many of the low-level details of RPython have been described elsewhere see e. Consequently, the two have had different levels of effort put into them PyPy about 60 man months, not including the time spend to develop RPython; Converge about 3.

Our aim in this paper is to explain, using Converge and PyPy as concrete examples, how RPython VMs can be designed and optimised, and what performance levels one might reasonably expect from them.

This paper is the first to consider: specific RPython VM designs; the general lessons one can learn from them; and the effects of different man-power levels on such VMs. Though the VMs we look at in this paper are for two relatively similar languages, it should be noted that RPython VMs have wider applicability.

Most of the techniques we discuss in this paper can be applied to a wide variety of languages; a few are specific to a given language or family of languages. The experimental suite for this paper is almost fully automated to enable repeatability: it automatically downloads and builds VMs, and then runs the experiments. Python and Converge Python and Converge are two seemingly similar languages. Both are dynamically typed, object orientated, have an indentation-based syntax, and a rich collection of built-in datatypes.

Indeed, much of Converge was explicitly influenced by Python. In this paper, we assume a passing familiarity with Python pointing out differences with Converge on an as-needs basis. Two technical features in particular distinguish Converge from Python.

Python is a real-world language, used by hundreds of thousands of developers worldwide for a huge number of tasks, and for which many external libraries are available. Converge on the other hand is a research language, intended to explore research on Domain Specific Languages DSLs and compile-time meta-programming. Every RPython program is a valid Python program and can also be run using a normal Python interpreter. However, RPython is suitably restricted to allow meaningful static analysis.

In addition, extra analysis is performed e. Users can influence the analysis with assert statements, but otherwise it is fully automatic.

Unlike seemingly similar languages e. In other words, when a program written in L executes on an appropriately written RPython VM, hot loops i. As we shall see later, language implementers can influence the particular JIT that is created, using their knowledge of language semantics to allow further optimisations to occur.

RPython is able to automatically create JITs because of the particular nature of interpreters. In essence, one need only add two annotations in the form of function calls to an RPython program to add a JIT.

The first annotation informs RPython that a loop in the user program at position pc has been encountered, so it may wish to start generating machine code if that loop has been encountered often enough. The second annotation informs RPython that execution of the program at position pc is about to begin: it provides a safe-point for switching from the execution of machine code back to the interpreter.

Traces intentionally linearise control structures, naturally inlining functions. Traces are hoped to be records of commonly taken paths through a program; when that assumption holds true, the result is extremely fast execution. The left-hand column shows a user program written in a Python-like language. When it is detected to be in a hot loop, the next time the code is executed, a trace is recorded.

The middle column shows the trace recorded when x is set to 6. Once the trace has been recorded, the trace optimiser then attempts to reduce it in size, so that the resulting machine code executes as fast as possible.

In this case, two type checks which are trivially true can be removed, and the two constant integer additions can be constant-folded. The resulting optimised trace is shown in the right-hand column. The RPython translator in fact outputs two interpreters: the language interpreter is the conceptually simple translation of the RPython interpreter into C; the tracing interpreter is a second representation of the interpreter which can be run to create traces.

When a hot loop in a user program is detected, a marker is left such that the next time the loop is about to run, the VM will use the tracing interpreter instead of the language interpreter.

When the loop is next encountered, a complete execution of the loop is performed and each low-level action taken by the tracing interpreter is recorded. After the loop has finished, the trace is then analysed, optimised, and converted into machine code. All subsequent executions of the loop will then call the machine code version.

If a guard fails at any point, execution falls back to the tracing interpreter for the rest of that bytecode, and then back to the language interpreter. By tracing the actions the interpreter itself takes, a meta-tracing JIT can automatically create a JIT from the interpreter.

As we shall see, the way an RPython interpreter is written affects the performance of the resulting JIT. To obtain the highest possible performance, the interpreter often needs to be subtly rewritten in specific places to aid the resulting JIT.

The JavaScript interpreter is carefully structured so that the traces that SPUR produces for common object operations are efficient, yielding excellent performance results. This is similar in intent to RPython, though SPUR works at the C bytecode level, and has fewer ways to annotate the interpreter to produce efficient traces.

Bytecode structure Both PyPy and Converge use a bytecode based interpreter together with a compiler that translates programs into the respective bytecode set. The bytecode sets are similar in intent, being stack-based and deferring type specialization until run-time.

Compilation Both VMs store programs as bytecode for execution by the eventual interpreter. Although both PyPy and Converge use traditional compilation, the implementations differ. Both systems attempt to transparently cache bytecode output on disk to lower compiler costs, automatically recompiling any source files which are detected to be newer than their cached equivalents this means that the first run of a Python or Converge program can be significantly slower than subsequent runs.

Because the Converge VM is used to compile new versions of the Converge compiler, the latter has to obey an important restriction: neither the compiler nor any libraries it uses can perform compile-time meta-programming. In practice, this restriction is not particularly onerous, although it requires a freshly unpacked Converge system to be compiled in a specific order: first a minimal version of the standard library enough for the compiler ; then the compiler itself; then the full library which, at this point, may include compile-time meta-programming.

Interpreter structure Both PyPy and Converge split their interpreters into three major parts: the bytecode interpreter; the built-in datatypes; and the built-in libraries. The bytecode interpreters are responsible for dispatching and implementing the bytecode set and are constructed in a direct, simple fashion. The built-in datatypes realise basic concepts such as objects, classes, lists, and dictionaries.

As well as being used extensively throughout the VM, several of these datatypes require careful bootstrapping during VM initialization. Built-in libraries are provided either for performance reasons or to allow integration with low-level C libraries. In Converge, the split between the bytecode interpreter and built-in datatypes is relatively informal, as befits a simple VM. In PyPy, in contrast, the split is very clearly defined to ensure that, despite the large size of the Python language specification, the components are manageable.

The bytecode interpreter treats all Python objects that it handles as black boxes; operations on them are handled by a separate component called the object space. The object space, on the other hand, only knows about datatypes, not about executing Python code, for which it refers back to the interpreter. Because of the mismatch between the high-level language and C, FFIs are often clumsy to use.

Libraries which do not need to interface to external C libraries are more interesting in an RPython VM. Traditional VMs such as CPython implement as much functionality in C as is practical, often migrating libraries from the native language to C over time. The speed advantages of doing so are often huge, and such language communities develop careful conventions about what calculations should be done via library calls to take advantage of this.

An important goal of RPython VMs is to significantly reduce the need to write and use C modules for performance reasons. Optimising an RPython VM Optimising an RPython VM means concentrating on the two execution modes: optimising the interpreter for faster interpretation speed; and rewriting the interpreter to produce traces which can be better optimised by the JIT. The former is largely similar to the challenges faced by other interpreters, so we dwell little on it; the latter is more unique to RPython VMs and what we concentrate on in this section.

What an RPython VM author needs to concentrate on are the commonly used building blocks that are specific to the language being implemented. In the case of Converge and PyPy, the three common pinch points are instances objects , classes, and modules.

However, most programs restrict such changes to small portions. Both RPython VMs therefore aim to make the common case of non-reflective access as fast as possible. In this section we give an overview of how language-specific building blocks can be optimised; many of the techniques described will be applicable to the different building blocks found in other languages.

Both experiences are useful: Converge shows how significant optimisations are possible with little effort, while PyPy shows how RPython VMs can optimise seemingly resistant programs. The overall strategy is to expose, by rewriting the interpreter, the parts which can be made constant in traces based on that code; these parts can then be optimised away, leaving only simple guards in their place.

The tactics used to achieve this involve either using RPython-level annotations i. We now give a brief explanation of each. Promoting values In the context of a specific trace, it is often reasonable to assume that certain pieces of information are constant.

The trace optimiser can be informed of this likelihood by promoting a value. For the small cost of inserting a guard at the first point of use, all subsequent calculations based on that constant can be constant-folded or removed from the trace. Note that constants are not known at compile-time: they are run-time values that are only constant for one particular trace. An important example of this is the concrete type of an object. Even in dynamically typed languages, most variables are only ever assigned values from a small set of types.

Promoting the type of an object allows calculations to be specialized on that type. Because there is a very high likelihood that only a single type will be used at a given program point, the corresponding guard will fail only rarely.

Virtual Machine Design and Implementation C/C++

To browse Academia. Skip to main content. By using our site, you agree to our collection of information through the use of cookies. To learn more, view our Privacy Policy. Log In Sign Up. Download Free PDF. Xu Shen.

Goodreads helps you keep track of books you want to read. Want to Read saving…. Want to Read Currently Reading Read. Other editions. Enlarge cover.

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. Blunden Published Computer Science. From the Publisher: With the emergence of multiplatform production systems and network-centric computing, the software industry has witnessed the resurgence of the virtual machine. Rather than committing to any single operating system, software engineers have embraced platform-neutral development tools and run-time environments.


This book provides an in-depth look at the construction and underlying theory of a fully functional virtual machine and an entire suite of related development.


Virtual Machine Design and Implementation in C_C Plus Plus

It uses the SIL Standard Intermediate Language as an intermediate language, which consists of an operation code set for procedural and object-oriented languages. The downside of this method is its low practicality, along with its platform-dependency. Furthermore, the ease of extracting the meaning of a program, as opposed to assembly-structured SIL codes, allows much more convenience in changing the software structure and correcting it to improve performance. Unable to display preview.

The various implementations support the same language, but use different optimizations, implementation techniques, and run on different platforms. Currently, we do not have much teaching material specific to SOM. We do have however an number of related slide sets, some somewhat historical, that give a basic overview.

Virtual Machine Design and Implementation C/C++

In this paper, we explain, through two concrete VMs, how meta-tracing RPython VMs can be designed and optimised, and, experimentally, the performance levels one might reasonably expect from them. Introduction Every programming language that makes it beyond a paper design needs a corresponding implementation.

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