Static and Dynamic Program Compilation by Interpreter Specialization

Interpretation and run-time compilation techniques are increasingly important because they can support heterogeneous architectures, evolving programming languages, and dynamically-loaded code. Interpretation is simple to implement, but yields poor performance. Run-time compilation yields better performance, but is costly to implement. One way to preserve simplicity but obtain good performance is to apply program specialization to an interpreter in order to generate an efficient implementation of the program automatically. Such specialization can be carried out at both compile time and run time.Recent advances in program-specialization technology have significantly improved the performance of specialized interpreters. This paper presents and assesses experiments applying program specialization to both bytecode and structured-language interpreters. The results show that for some general-purpose bytecode languages, specialization of an interpreter can yield speedups of up to a factor of four, while specializing certain structured-language interpreters can yield performance comparable to that of an implementation in a general-purpose language, compiled using an optimizing compiler.     

WCET可预测的Java指令集硬件实现

为能以硬件方式直接执行CISC结构的Java字节码,设计并实现适用于32位嵌入式实时Java平台的JPOR-32指令集。分析Java虚拟机规范中各Java字节码的功能和实现原理,设定执行每条指令时信号和数据在Java处理器数据通路上的变化,采用微指令方式执行复杂指令,简单指令直接执行,从而使JPOR-32的指令集具有RISC特性。实验结果验证了指令集的正确性及其最坏情况执行时间(WCET)的可预测性。     

A code compression system based on pipelined interpreters

This paper describes a system for compressed code generation. The code of applications is partioned into time‐critical and non‐time‐critical code. Critical code is compiled to native code, and non‐critical code is compiled to a very dense virtual instruction set which is executed on a highly optimized interpreter. The system employs dictionary‐based compression by means of superinstructions which correspond to patterns of frequently used base instructions. The code compression system is designed for the Philips TriMedia VLIW processor. The interpreter is pipelined to achieve a high interpretation speed. The pipeline consists of three stages: fetch, decode, and execute. While one instruction is being executed, the next instruction is decoded, and the next one after that is fetched from memory. On a TriMedia VLIW with a load latency of three cycles and a jump latency of four cycles, the interpreter achieves a peak performance of four cycles per instruction and a sustained performance of 6.27 cycles per instruction. Experiments are described that demonstrate the compression quality of the system and the execution speed of the pipelined interpreter; these were found to be about five times more compact than native TriMedia code and a slowdown of about eight times, respectively. Copyright © 1999 John Wiley & Sons, Ltd.     

Cost analysis of object-oriented bytecode programs

Cost analysis statically approximates the cost of programs in terms of their input data size. This paper presents, to the best of our knowledge, the first approach to the automatic cost analysis of object-oriented bytecode programs. In languages such as Java and C#, analyzing bytecode has a much wider application area than analyzing source code since the latter is often not available. Cost analysis in this context has to consider, among others, dynamic dispatch, jumps, the operand stack, and the heap. Our method takes a bytecode program and a cost model specifying the resource of interest, and generates cost relations which approximate the execution cost of the program with respect to such resource. We report on COSTA, an implementation for Java bytecode which can obtain upper bounds on cost for a large class of programs and complexity classes. Our basic techniques can be directly applied to infer cost relations for other object-oriented imperative languages, not necessarily in bytecode form.     

Exploiting an abstract-machine-based framework in the design of a Java ILP processor

Abstract machines bridge the gap between the high-level of programming languages and the low-level mechanisms of a real machine. The paper proposed a general abstract-machine-based framework (AMBF) to build instruction level parallelism processors using the instruction tagging technique. The constructed processor may accept code written in any (abstract or real) machine instruction set, and produce tagged machine code after data conflicts are resolved. This requires the construction of a tagging unit which emulates the sequential execution of the program using tags rather than actual values. The paper presents a Java ILP processor by using the proposed framework. The Java processor takes advantage of the tagging unit to dynamically translate Java bytecode instructions to RISC-like tag-based instructions to facilitate the use of a general-purpose RISC core and enable the exploitation of instruction level parallelism. We detailed the Java ILP processor architecture and the design issues. Benchmarking of the Java processor using SpecJVM98 and Linpack has shown the overall ILP speedup improvement between 78% and 173%.     

Interpreting computer code in a computer‐based learning system for novice programmers

Although the principles of writing compilers and interpreters are well known, we have found that the ideas needed to develop an interpreter for the express purpose of allowing direct interaction with the running code do not yet appear to have been published in an academic context. We describe a programming method that can be used for the production of an interpreter for common object‐oriented languages such as C++, Java and C#. The main purpose of the interpreter is to parse short, relatively simple programs and allow direct interaction between the user and the running code. Such a system is useful for projects such as OGRE, which is an educational tool allowing students to visualize in three‐dimensional graphics the state of a program as it runs. The interpreter works by first parsing the source code and building up a data structure capable of representing the program's source code in a form that can be used to both run the program and extract detailed information from the running program. This extraction allows for novel uses of the interpreter, such as forming the basis for a visualization system that must display and provide such information to the user as they watch their executing program. This paper considers the construction of such an interpreter specifically for C++, but the principles should be the same for other similar languages such as Java and C#. We cover the main tasks required of the programmer to create and use the data structure, highlighting areas such as its design, initial construction during parsing, and techniques required to use it for interpretation. These include the ability for the data structure to intelligently clone subsets of itself when multiple copies of one of its elements are required by the running program, how it handles C++'s complicated function overloading and overriding rules, and how inheritance and polymorphism can be supported. Copyright © 2005 John Wiley & Sons, Ltd.     

An instruction for direct interpretation of LZ77‐compressed programs

A new instruction adapts LZ77 compression for use inside running programs. The instruction economically references and reuses code fragments that are too small to package as conventional subroutines. The compressed code is interpreted directly, with neither prior nor on‐the‐fly decompression. Hardware implementations seem plausible and could benefit both memory‐constrained and more conventional systems. The method is extremely simple. It has been added to a pre‐existing, bytecoded instruction set, and it added only 10 lines of C to the bytecode interpreter. It typically cuts code size by a third; that is, typical compression ratios are roughly 0.67×. More ambitious compressors are available, but they are more complex, which retards adoption. The current method offers a useful trade‐off to these more complex systems. Copyright © 2005 John Wiley & Sons, Ltd.     

A Type System for the Java Bytecode Language and Verifier

The Java Virtual Machine executes bytecode programs that may have been sent from other, possibly untrusted, locations on the network. Since the transmitted code may be written by a malicious party or corrupted during network transmission, the Java Virtual Machine contains a bytecode verifier to check the code for type errors before it is run. As illustrated by reported attacks on Java run-time systems, the verifier is essential for system security. However, no formal specification of the bytecode verifier exists in the Java Virtual Machine Specification published by Sun. In this paper, we develop such a specification in the form of a type system for a subset of the bytecode language. The subset includes classes, interfaces, constructors, methods, exceptions, and bytecode subroutines. We also present a type checking algorithm and prototype bytecode verifier implementation, and we conclude by discussing other applications of this work. For example, we show how to extend our formal system to check other program properties, such as the correct use of object locks. This revised version was published online in August 2006 with corrections to the Cover Date.     

PicoJava: a direct execution engine for Java bytecode

Key to the central promise inherent in Java technology-“write once, run anywhere”-is the fact that Java programs run on the Java virtual machine, insulating them from any contact with the underlying hardware. Consequently, Java programs must execute indirectly through a translation layer built into the Java virtual machine. Translation essentially converts Java virtual machine instructions (called bytecodes) into corresponding machine-specific binary instructions. Bytecode is a single image of a program that will execute identically (in principle) on any system equipped with a JVM. The first step toward the development of a new class of Java processors was the creation of the bytecode execution engine itself, called the picoJava core. PicoJava directly executes Java bytecode instructions and provides hardware support for other essential functions of the JVM. Executing bytecode instructions in hardware eliminates the need for dynamic translation, thus extending the useful range of Java bytecode programs to embedded environments. By the end of 1998, Java processors like Sun's microJava 701 should be available for evaluation from several licensees of the picoJava core technology     

TinyVM: an energy‐efficient execution infrastructure for sensor networks

Energy‐efficient implementation techniques for virtual machines (VMs) have received little attention yet: conventional wisdom claims that VMs have a diametrical effect on energy consumption, and VM‐based applications are therefore short‐lived. In this paper, we argue that bytecode interpretation is affordable if we synthesize VMs specifically for energy efficiency. We present TinyVM, an execution infrastructure that seamlessly integrates with C and nesC/TinyOS‐based programming environments. TinyVM achieves high code density through the use of compressed bytecode as the primary program representation. Compressed bytecode allows rapid application deployment with low communication overhead. TinyVM executes compressed bytecode in place, which eliminates the need for a decompression stage and thereby reduces memory consumption on sensor nodes. Our infrastructure automates the creation of energy‐efficient application‐specific VMs. Applications are partitioned in machine code, bytecode, and VM instruction set extensions. Partitioning is manually controlled and/or fully guided by a discrete optimization problem that produces a partitioning with lowest energy consumption for a given program size limit. We provide experimental results for sensor network benchmarks and for selected applications on various CPU architectures including Atmega128‐based motes and the ARM‐based Intel iMote2. TinyVM has been released under the GNU General Public License. Copyright © 2011 John Wiley & Sons, Ltd.     

An empirical study of Java bytecode programs

We present a study of the static structure of real Java bytecode programs. A total of 1132 Java jar‐files were collected from the Internet and analyzed. In addition to simple counts (number of methods per class, number of bytecode instructions per method, etc.), structural metrics such as the complexity of control‐flow and inheritance graphs were computed. We believe this study will be valuable in the design of future programming languages and virtual machine instruction sets, as well as in the efficient implementation of compilers and other language processors. Copyright © 2006 John Wiley & Sons, Ltd.     

压缩技术在数据采集系统中的应用

介绍了一种通用编码的数据压缩技术－ＬＺＷ算法，并以此算法为核心，对采集的数据预先作有条件的有损压缩和自适应编码变换，在压缩数据的同时，使数据和ＬＺＷ算法要求的数字输入格式相匹配，使之能更好地发挥作用，经实际验证，数据经有压缩，自适应编码和ＬＺＷ压缩之后，达到较高的压缩率，该方法具有通用性好，易于实现的特点。     

Decompilation of Java bytecode to Prolog by partial evaluation

Reasoning about Java bytecode (JBC) is complicated due to its unstructured control-flow, the use of three-address code combined with the use of an operand stack, etc. Therefore, many static analyzers and model checkers for JBC first convert the code into a higher-level representation. In contrast to traditional decompilation, such representation is often not Java source, but rather some intermediate language which is a good input for the subsequent phases of the tool. Interpretive decompilation consists in partially evaluating an interpreter for the compiled language (in this case JBC) written in a high-level language with respect to the code to be decompiled. There have been proofs-of-concept that interpretive decompilation is feasible, but there remain important open issues when it comes to decompile a real language such as JBC. This paper presents, to the best of our knowledge, the first modular scheme to enable interpretive decompilation of a realistic programming language to a high-level representation, namely of JBC to Prolog. We introduce two notions of optimality which together require that decompilation does not generate code more than once for each program point. We demonstrate the impact of our modular approach and optimality issues on a series of realistic benchmarks. Decompilation times and decompiled program sizes are linear with the size of the input bytecode program. This demonstrates empirically the scalability of modular decompilation of JBC by partial evaluation.     

Exact and Portable Profiling for the JVM Using Bytecode Instruction Counting

This paper presents a novel profiling approach, which is entirely based on program transformation techniques in order to enable exact profiling, preserving complete call stacks, method invocation counters, and bytecode instruction counters. We exploit the number of executed bytecode instructions as profiling metric, which has several advantages, such as making the instrumentation entirely portable and generating reproducible profiles. These ideas have been implemented as the JP tool. It provides a small and flexible API to write portable profiling agents in pure Java, which are periodically activated to process the collected profiling information. Performance measurements point out that JP causes significantly less overhead than a prevailing tool for the exact profiling of Java code.     

基于混合模式的Java卡字节码优化器

Java卡是一种基于Java语言的智能卡。因为智能卡的空间和处理器速度的约束,一个应用程序在Java卡上运行时面临的最大问题是存储空间的不足和对程序执行时间的严格限制。因此,对下载到卡中的字节码进行优化是十分必要的。本文提出了一种综合使用扩展指令集和分段压缩算法的Java卡字节码优化器的设计方案,通过对字节码文件的优化,可得到占用空间较少且没有降低执行速率的字节码文件。     

一种实现仪器菜单动态配置的方法

阐述了一种实现仪器菜单动态配置的方法：主应用程序调用相应的仪器菜单解析程序;解析程序解析菜单数据配置文件,并向主应用程序返回显示代码;显示代码经编译、链接,形成可执行的二进制文件;二进制文件运行后显示相应的仪器菜单。运用该方法可以方便灵活地修改仪器菜单显示界面。     

Subroutine Inlining and Bytecode Abstraction to Simplify Static and Dynamic Analysis

In Java bytecode, intra-method subroutines are employed to represent code in “finally” blocks. The use of such polymorphic subroutines within a method makes bytecode analysis very difficult. Fortunately, such subroutines can be eliminated through recompilation or inlining. Inlining is the obvious choice since it does not require changing compilers or access to the source code. It also allows transformation of legacy bytecode. However, the combination of nested, non-contiguous subroutines with overlapping exception handlers poses a difficult challenge. This paper presents an algorithm that successfully solves all these problems without producing superfluous instructions. Furthermore, inlining can be combined with bytecode simplification, using abstract bytecode. We show how this abstration is extended to the full set of instructions and how it simplifies static and dynamic analysis.     

Mechanising a Type-Safe Model of Multithreaded Java with a Verified Compiler

This article presents JinjaThreads, a unified, type-safe model of multithreaded Java source code and bytecode formalised in the proof assistant Isabelle/HOL. The semantics strictly separates sequential aspects from multithreading features like locks, forks and joins, interrupts, and the wait-notify mechanism. This separation yields an interleaving framework and a notion of deadlocks that are independent of the language, and makes the type safety proofs modular. JinjaThreads’s non-optimising compiler translates source code into bytecode. Its correctness proof guarantees that the generated bytecode exhibits exactly the same observable behaviours as the source code, even for infinite executions and under the Java memory model. The semantics and the compiler are executable. JinjaThreads builds on and reuses the Java formalisations Jinja, Bali, \(\mu \)Java, and Java\(^{\ell ight}\) by Nipkow’s group. Being the result of more than fifteen years of studying Java in Isabelle/HOL, it constitutes a large and long-lasting case study. It shows that fairly standard formalisation techniques scale well and highlights the challenges, benefits, and drawbacks of formalisation reuse.