泛型程序设计的研究

泛型程序设计将类型安全的任务从程序员转移给编译器,不再需要程序员编写代码来检测数据类型是否正确,而是在编译时由编译器强制使用正确的数据类型。因此,采用泛型程序设计可以减少类型强制转换的需要和运行时出现错误的可能性。     

Run-Time Validation of Speculative Optimizations using CVC.

Translation validation is an approach for validating the output of optimizing compilers. Rather than verifying the compiler itself, translation validation mandates that every run of the compiler generate a formal proof that the produced target code is a correct implementation of the source code. Speculative loop optimizations are aggressive optimizations which are only correct under certain conditions which cannot be validated at compile time. We propose using an automatic theorem prover together with the translation validation framework to automatically generate run-time tests for such speculative optimizations. This run-time validation approach must not only detect the conditions under which an optimization generates incorrect code, but also provide a way to recover from the optimization without aborting the program or producing an incorrect result. In this paper, we apply the run-time validation technique to a class of speculative reordering transformations and give some initial results of run-time tests generated by the theorem prover CVC.     

Run-time issues in program partitioning on distributed memory systems

Our earlier work reported a Threshold Scheduling Method for compile-time mapping of functional parallism on distributed-memory systems. The work reported in this paper discusses run-time issues in efficiently supporting the functional parallism with minimal overheads, through a combination of compile-time and run-time ownership analysis. At compile time, the code generation phase determines whether a local copy of a live definition of a variable needed by a task is available on a given processor, through an ownership analysis. In case ownership cannot be resolved at compile time, an appropriate code is generated to perform analysis at run time. The code generation is carried out so that all the processors carry the same copy of the compiled program with the individual processor's code being isolated and the universally owned code being replicated on all processors to minimize run-time overheads. The run-time system maintains the static and dynamic ownerships at every processor to avoid communication overhead on ownership information. We demonstrate the approach by incorporating it in the compiler for targeting a parallel functional language, Sisal (streams and iterations in single assignment language), to Intel Touchstone i860 systems. Several benchmarks demonstrate the viability of the approach.     

An integrated runtime and compile-time approach for parallelizingstructured and block structured applications

In compiling applications for distributed memory machines, runtime analysis is required when data to be communicated cannot be determined at compile-time. One such class of applications requiring runtime analysis is block structured codes. These codes employ multiple structured meshes, which may be nested (for multigrid codes) and/or irregularly coupled (called multiblock or irregularly coupled regular mesh problems). In this paper, we present runtime and compile-time analysis for compiling such applications on distributed memory parallel machines in an efficient and machine-independent fashion. We have designed and implemented a runtime library which supports the runtime analysis required. The library is currently implemented on several different systems. We have also developed compiler analysis for determining data access patterns at compile time and inserting calls to the appropriate runtime routines. Our methods can be used by compilers for HPF-like parallel programming languages in compiling codes in which data distribution, loop bounds and/or strides are unknown at compile-time. To demonstrate the efficacy of our approach, we have implemented our compiler analysis in the Fortran 90D/HPF compiler developed at Syracuse University. We have experimented with a multi-bloc Navier-Stokes solver template and a multigrid code. Our experimental results show that our primitives have low runtime communication overheads and the compiler parallelized codes perform within 20% of the codes parallelized by manually inserting calls to the runtime library     

Translation and Run-Time Validation of Loop Transformations

This paper presents new approaches to the validation of loop optimizations that compilers use to obtain the highest performance from modern architectures. Rather than verify the compiler, the approach of translation validationperforms a validation check after every run of the compiler, producing a formal proof that the produced target code is a correct implementation of the source code. As part of an active and ongoing research project on translation validation, we have previously described approaches for validating optimizations that preserve the loop structure of the code and have presented a simulation-based general technique for validating such optimizations. In this paper, for more aggressive optimizations that alter the loop structure of the code—such as distribution, fusion, tiling, and interchange—we present a set of permutation ruleswhich establish that the transformed code satisfies all the implied data dependencies necessary for the validity of the considered transformation. We describe the extensions to our tool voc-64 which are required to validate these structure-modifying optimizations. This paper also discusses preliminary work on run-time validation of speculative loop optimizations. This involves using run-time tests to ensure the correctness of loop optimizations whose correctness cannot be guaranteed at compile time. Unlike compiler validation, run-time validation must not only determine when an optimization has generated incorrect code, but also recover from the optimization without aborting the program or producing an incorrect result. This technique has been applied to several loop optimizations, including loop interchange and loop tiling, and appears to be quite promising. This research was supported in part by NSF grant CCR-0098299, ONR grant N00014-99-1-0131, and the John von Neumann Minerva Center for Verification of Reactive Systems.     

An optimizing compiler for the icon programming language

Compiling code for the Icon programming language presents several challenges, particularly in dealing with types and goal-directed expression evaluation. In order to produce optimized code, it is necessary for the compiler to know much more about operations than is necessary for the compilation of most programming languages. This paper describes the organization of the Icon compiler and the way it acquires and maintains information about operations. The Icon compiler generates C code, which makes it portable to a wide variety of platforms and also allows the use of existing C compilers for performing routine optimizations on the final code. A specially designed implementation language, which is a superset of C, is used for writing Icon's run-time system. This language allows the inclusion of information about the abstract semantics of Icon operations and their type-checking and conversion requirements. A translator converts code written in the run-time language to C code to provide an object library for linking with the code produced by the Icon compiler. The translation process also automatically produces a database that contains the information the Icon compiler needs to generate and optimize code. This approach allows easy extension of Icon's computational repertoire, alternate computational extensions, and cross compilation.     

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.     

STL中Generic算法的扩展

Generic程序设计是实现软件重用的重要方法之一，文章介绍了generic程序设计与C＋＋标准模板库（STL），探讨了STL中generic算法的扩展方法。     

Compiling global name-space parallel loops for distributedexecution

Compiler support required to allow programmers to express their algorithms using a global name-space is discussed. A general method for the analysis of a high-level source program and its translation into a set of independently executing tasks that communicate using messages is presented. It is shown that if the compiler has enough information, the translation can be carried out at compile time; otherwise; run-time code is generated to implement the required data movement. The analysis required in both situations is described, and the performance of the generated code on the Intel iPSC/2 hypercube is presented     

Generic components for petascale adaptive unstructured mesh-based simulations

In the traditional programming paradigm, data structures and algorithms are developed for specific data types and requirements. This leads to code redundancy and inflexibility, thus not allowing effective code reuse for similar applications. One effective approach to increase code reuse is generic programming, which focuses on the development of efficient, reusable software libraries through suitable abstractions for the common requirements. In this paper, we present how we applied generic programming to an ongoing effort for mesh-based adaptive simulations on massively parallel computers. Three generic components, iterator, set and tag, were developed using design pattern, C++ template programming and the standard template library. The scaling studies on petascale supercomputers demonstrate the efficiency of the reusable, generic components which do not sacrifice the performance of the previous tools developed in the traditional object-oriented programming paradigm.     

基于边缘轮廓特征的实时车型分类

采用面向对象技术设计可视化程序设计语言的编译系统,通过对复杂的可视化图片语法进行分析,将各语法现象以语法单元类的形式进行抽象分类,提出了编译器-编译器的设计策略,给出了从可视化图片设计语言到伪指令代码的解决方法.主要研究在面向对象的编辑环境中如何实现满足硬件设备控制器的稳定、高效的编译系统.通过将可视化程序语言分析器(VPLPG)和小型的类C编译(LCC)器配合使用最终实现可视化程序设计语言的编译系统.     

基于可视化程序设计语言的编译系统

采用面向对象技术设计可视化程序设计语言的编译系统,通过对复杂的可视化图片语法进行分析,将各语法现象以语法单元类的形式进行抽象分类,提出了编译器－编译器的设计策略,给出了从可视化图片设计语言到伪指令代码的解决方法。主要研究在面向对象的编辑环境中如何实现满足硬件设备控制器的稳定、高效的编译系统。通过将可视化程序语言分析器(VPLPG)和小型的类C编译(LCC)器配合使用最终实现可视化程序设计语言的编译系统。     

Mumbo: A Rule-Based Implementation of a Run-time Program Generation Language

We describe our efforts to use rule-based programming to produce a model of Jumbo, a run-time program generation (RTPG) system for Java. Jumbo incorporates RTPG following the simple principle that the regular compiler — or, rather, its back-end — can be used both for ordinary, static compilation and for run-time compilation. This tends to produce a run-time compiler that is inefficient but potentially subject to improvement by partial evaluation. However, the complexity of the language and compiler have made it difficult for us to achieve actual optimization. The model, written in Maude, preserves all the essential ingredients of Jumbo, but operates on a simplified language, called Mumbo. The simplification in the language together with Maude's support for code rewriting has allowed us to make rapid progress. We discuss the model in detail, the kinds of optimizations we have obtained, and the impact on the Jumbo project.     

动态二进制翻译中动态优化的成本与收益分析

传统的静态编译器优化存在着各种限制,为此,提出了一种运行期动态优化的对策。在程序的执行过程中,持续检测程序运行的profile信息,并根据这些信息对程序代码进行优化变换,创建并运行程序代码的优化版本。这种运行期动态优化操作是直接针对程序的二进制代码的,不针对程序语言或编译器。这不仅带来优化的透明性,还使得老版本的源代码即遗留代码也可以从优化技术中获得性能提升。     

On the reification of Java wildcards

Providing runtime information about generic types–that is, reifying generics–is a challenging problem studied in several research papers in the last years. This problem is not tackled in current version of the Java programming language (Java 6), which consequently suffers from serious safety and coherence problems. The quest for finding effective and efficient solutions to this problem is still open, and is further made more complicated by the new mechanism of wildcards introduced in Java J2SE 5.0: its reification aspects are currently unexplored and pose serious semantics and implementation issues.In this paper, we discuss an implementation support for wildcard types in Java. We first analyse the problem from an abstract viewpoint, discussing the issues that have to be faced in order to extend an existing reification technique so as to support wildcards, namely, subtyping, capture conversion and wildcards capture in method calls. Secondly, we present an implementation in the context of the EGO compiler. EGO is an approach for efficiently supporting runtime generics at compile-time: synthetic code is automatically added to the source code by the extended compiler, so as to create generic runtime type information on a by need basis, store it into object instances, and retrieve it when necessary in type-dependent operations. The solution discussed in this paper makes the EGO compiler the first reification approach entirely dealing with the present version of the Java programming language.     

Do you trust your compiler?

As our society becomes more technologically complex, computer systems are finding an alarming number of uses in safety-critical applications. In many such systems, the software component's reliability is essential to the system's safe operation, so it becomes natural to ask, “How can software be made to behave correctly when executed?” Using program transformations to produce trusted software simplifies verification. Program transformations use proven laws to manipulate programs in a manner analogous to algebraic transformations. The authors sketch how a formal method based on program transformations can be used to construct a verified compiler. Such a compiler has been proved to correctly compile any correct program into assembly language. While the compiler itself may not execute efficiently-after all, you need only use the verified compiler the last time you compile a program-the transformational approach should enable the verified compiler to produce efficient assembly code     

Extensions to the C programming language for enhanced fault detection

The acceptance of the C programming language by academia and industry is partially responsible for the ‘software crisis’. The simple, trusting semantics of C mask many common faults, such as range violations, which would be detected and reported at run-time by programs coded in a robust language such as Ada.

1 Ada is a registered trademark of the U.S. Government (Ada Joint Program Office)

This needlessly complicates the debugging of C programs. Although the assert macro lets programmers add run-time consistency checks to their programs, the number of instantiations of this macro needed to make a C program robust makes it highly unlikely that any programmer could correctly perform the task. We make some unobtrusive extensions to the C language which support the efficient detection of faults at run-time without reducing the readability of the source code. Examples of the extensions are automatic checking of error codes returned by library routines, constrained subtypes and detection of references to uninitialized and/or non-existent array elements.     

Generic Programming and High-Performance Libraries

Generic programming is an especially attractive paradigm for developing libraries for high-performance computing because it simultaneously emphasizes generality and efficiency. In the generic programming approach, interfaces are based on sets of specified requirements on types, rather than on any particular types, allowing algorithms to inter-operate with any data types meeting the necessary requirements. These sets of requirements, known as concepts, can specify syntactic as well as semantic requirements. Besides providing a powerful means of describing interfaces to maximize software reuse, concepts provide a uniform mechanism for more closely coupling libraries with compilers and for effecting domain-specific library-based compiler extensions. To realize this goal however, programming languages and their associated tools must support concepts as first-class constructs. In this paper we advocate better syntactic and semantic support to make concepts first-class and present results demonstrating the kinds of improvements that are possible with static checking, compiler optimization, and algorithm correctness proofs for generic libraries based on concepts.     

Compiler support for general-purpose computation on GPUs

In recent years, the GPU (graphics processing unit) has evolved into an extremely powerful and flexible processor, with it now representing an attractive platform for general-purpose computation. Moreover, changes to the design and programmability of GPUs provide the opportunity to perform general-purpose computation on a GPU (GPGPU). Even though many programming languages, software tools, and libraries have been proposed to facilitate GPGPU programming, the unusual and specific programming model of the GPU remains a significant barrier to writing GPGPU programs. In this paper, we introduce a novel compiler-based approach for GPGPU programming. Compiler directives are used to label code fragments that are to be executed on the GPU. Our GPGPU compiler, Guru, converts the labeled code fragments into ISO-compliant C code that contains appropriate OpenGL and Cg APIs. A native C compiler can then be used to compile it into the executable code for GPU. Our compiler is implemented based on the Open64 compiler infrastructure. Preliminary experimental results from selected benchmarks show that our compiler produces significant performance improvements for programs that exhibit a high degree of data parallelism.