A surprisingly simple Lua compiler—Extended version

The problem of complexity of developing and supporting the software cross-cutting concern and its solution using the aspect-oriented approach is examined. The complexity of aspect-oriented programming application in object-oriented programming languages is described. The problem of dependency of the declaration syntax of aspects and the method of their integration is investigated. The architecture that will provide the independence of the syntax of declaration and introduction of aspects in object-oriented programs is proposed. For separation, an urban design pattern that unites declaration of the aspect and its integration method is used. The system displays the classical entities of AOP in the object structure, which facilitates syntax mastering. Three methods for declaring aspects are developed, namely declaration using inheritance from a base class, template class generalization and flexible aspect creation at run time. For integration at compile time, a special integration module and the Roslyn compiler modification, which ensures implementation of the aspect configuration system and introduces advice invocation points in a code are developed. For integration at run time without using the dependency injection container, helper methods for creating proxy classes are designed. Also, modules for popular dependency injection containers, which allow integration by means of these containers are developed. Testing of the developed system, which showed a significant reduction in the size of a source code is carried out. The most pronounced reduction was in large enterprise-level systems. When using introduction at compile time, performance drop of programs is not observed. When using integration at run time, performance losses do not exceed those when using a similar proxy class.

The ATLAS Error Reporting provides a service that allows experts and shift crew to track and address errors relating to the data taking components and applications. This service, called the Error Reporting Service (ERS), gives to software applications the opportunity to collect and send comprehensive data about run-time errors, to a place where it can be intercepted in real-time by any other system component. Other ATLAS online control and monitoring tools use the ERS as one of their main inputs to address system problems in a timely manner and to improve the quality of acquired data.The actual destination of the error messages depends solely on the run-time environment, in which the online applications are operating. When an application sends information to ERS, depending on the configuration, it may end up in a local file, a database, distributed middleware which can transport it to an expert system or display it to users. Thanks to the open framework design of ERS, new information destinations can be added at any moment without touching the reporting and receiving applications.The ERS Application Program Interface (API) is provided in three programming languages used in the ATLAS online environment: C++, Java and Python. All APIs use exceptions for error reporting but each of them exploits advanced features of a given language to simplify the end-user program writing. For example, as C++ lacks language support for exceptions, a number of macros have been designed to generate hierarchies of C++ exception classes at compile time. Using this approach a software developer can write a single line of code to generate a boilerplate code for a fully qualified C++ exception class declaration with arbitrary number of parameters and multiple constructors, which encapsulates all relevant static information about the given type of issues. When a corresponding error occurs at run time, the program just need to create an instance of that class passing relevant values to one of the available class constructors and send this instance to ERS.This paper presents the original design solutions exploited for the ERS implementation and describes how it was used during the first ATLAS run period. The cross-system error reporting standardization introduced by ERS was one of the key points for the successful implementation of automated mechanisms for online error recovery.

Abstract— Aim/Background This study aims to systematically evaluate the practical runtime performance of the Insertion Sort algorithm across six widely used programming languages: C, C++, Go, Java, PHP, and Python. While Insertion Sort’s theoretical time complexity is well established, real-world execution performance may differ significantly due to variations in language paradigms, compilation strategies, and runtime environments. The research focuses on identifying how language-specific characteristics influence actual execution time. Methods The Insertion Sort algorithm was implemented uniformly in all six languages using a shared pseudocode to ensure consistency. Experiments were conducted under controlled conditions using input sizes of 1K, 10K, and 100K elements. Each dataset was tested with four different data arrangements: ascending, descending, nearly sorted, and random. Runtime measurements were collected and compared. Additionally, a Relative Execution Time Ratio (RETR) metric was introduced to provide a normalized and quantitative comparison of performance across languages. Results The experimental findings show clear performance differences among the programming languages. Go demonstrated the fastest runtimes across most scenarios, benefiting from its efficient compiled execution model. Java performed second-best, leveraging just-in-time (JIT) compilation and runtime optimizations. C and C++ showed moderate performance, influenced by compiler optimization levels and memory management mechanisms. PHP and Python exhibited significantly slower execution times, particularly for large and poorly ordered datasets, mainly due to interpreter overhead and runtime inefficiencies. The RETR metric effectively highlighted these relative performance gaps. Conclusion The study confirms that programming language choice significantly impacts real-world algorithm performance, even for well-understood algorithms like Insertion Sort. Compiled languages with efficient runtime optimizations generally outperform interpreted languages, especially for larger datasets. These results emphasize the importance of selecting appropriate programming languages for performance-critical applications and aligning algorithm implementation with both data characteristics and execution environments.

In an attempt to determine if the IBM PL/I (F) programming language could be used effectively in the solution of scientific problems the following questions were raised. Is the compiler relatively error free? Are there great difficulties in training? Will the programmer who is knowledgeable in other higher languages use PL/I? Will PL/I eliminate the need for assembler language? What are the relative efficiencies of PL/I in compile and execution times as compared with Fortran IV (II)?The answers to these questions were quite favorable to PL/I except the last. It was found that PL/I compilation times were roughly twice the Fortran compilation times. The PL/I execution times of various applications ranged from 1.3 to 4.0 times the corresponding Fortran execution times. For the present, applications which use special features of PL/I will be done in PL/I but most problem solving will still be done in Fortran.

Hundreds of programming languages are in use todayscripting languages for Internet commerce, user interface programming tools, spreadsheet macros, page format specification languages, and many others. Designing a programming language is a metaprogramming activity that bears certain similarities to programming in a regular language, with clarity and simplicity even more important than in ordinary programming. This comprehensive text uses a simple and concise framework to teach key ideas in programming language design and implementation. The book's unique approach is based on a family of syntactically simple pedagogical languages that allow students to explore programming language concepts systematically. It takes as its premise and starting point the idea that when language behaviors become incredibly complex, the description of the behaviors must be incredibly simple. The book presents a set of tools (a mathematical metalanguage, abstract syntax, operational and denotational semantics) and uses it to explore a comprehensive set of programming language design dimensions, including dynamic semantics (naming, state, control, data), static semantics (types, type reconstruction, polymporphism, effects), and pragmatics (compilation, garbage collection). The many examples and exercises offer students opportunities to apply the foundational ideas explained in the text. Specialized topics and code that implements many of the algorithms and compilation methods in the book can be found on the book's Web site, along with such additional material as a section on concurrency and proofs of the theorems in the text. The book is suitable as a text for an introductory graduate or advanced undergraduate programming languages course; it can also serve as a reference for researchers and practitioners.

Go is a new programming language developed by Google. Although it is still young compared to other programming languages, it already has modern and powerful features inherited from existing programming languages, and some of these are similar to Java. Go is designed for quick time development. Concurrency is the one of the main its features. In this paper, we analyze the performance of Go, and compare it with Java from two aspects: compile time and concurrency. There are many studies about the performance analysis and comparison of programming languages, but only a few publications investigate Go. Some of Go performance evaluation are based on the experimental release of Go. To analyze concurrency features, we implement simple matrix multiplication programs in both Go and Java. Java implementation uses Java Thread, and Go implementation uses Gor-outine and Channel. From the experiment, Go derived better performance than Java in both compile time and concurrency. Moreover, Go code shows the ease of concurrent programming. Go is still young, but we are convinced that Go will become the mainstream.

The ability of the software engineering community to achieve high levels of reuse from software frameworks has been tempered by the difficulty in understanding how to reuse them properly. When written correctly, a plugin can take advantage of the framework's code and architecture to provide a rich application with relatively few lines of code. Unfortunately, doing this correctly is difficult because frameworks frequently require plugin developers to be aware of complex protocols between objects, and improper use of these protocols causes exceptions and unexpected behavior at run time. This dissertation introduces collaboration constraints, rules governing how multiple objects may interact in a complex protocol. These constraints are particularly difficult to understand and analyze because they may extend across type boundaries and even programming language boundaries. This thesis improves the state of the art through two mechanisms. First, it provides a deep understanding of these collaboration constraints and the framework designs which create them. Second, it introduces Fusion, an adoptable specification language and static analysis tool, that detects broken collaboration constraints in plugin code and demonstrates how to achieve this goal in a cost-effective manner that is practical for industry use. In this dissertation, I have done an empirical study of framework help forums which showed that collaboration constraints are burdensome for developers, as they take hours or even days to resolve. From this empirical study, I have identified several common properties of collaboration constraints. This motivated a new specification language, called Fusion, that is tailored for specifying collaboration constraints in a practical way. The specification language uses relationships to describe the abstract associations between objects and allows developers to specify collaboration constraints as logical predicates of relationships. Since a relationship is an abstraction above the code, this allows developers to easily specify constraints that cross type and language boundaries. There are three variants of the analysis: a sound variant that has false positives but no false negatives, a complete variant that has false negatives but no false positives, and a pragmatic variant that attempts to balance this tradeoff. In this dissertation, I successfully used Fusion to specify and analyze constraints from examples found in the help forums of the ASP.NET and Spring frameworks. Additionally, I ran Fusion on DaCapo, a 1.5 MLOC DaCapo benchmark for program analysis, to show that Fusion is scalable and provides precise enough results for industry with low specification cost. This dissertation examines many tradeoffs: the tradeoffs of framework designs, the tradeoffs of specification precision, and the tradeoffs of program analysis results are all featured. A central theme of this work is that there is no single right solution to collaboration constraints; there are only solutions that work better for a particular instance of the problem.

The first Futamura projection enables compilation and high performance code generation of user programs by partial evaluation of language interpreters. Previous work has shown that online partial evaluation can yield the same peak performance as a specialized JIT compiler. However, this comes with the downside of additional compile time: Online partial evaluation of language interpreters has to specialize interpreter code on the fly to the dynamic types used at run time to create efficient target code. As a result, the time spent on partial evaluation itself is a significant contributor to the overall compile time of a method. The second Futamura projection solves this problem by self-applying partial evaluation on the partial evaluation algorithm, effectively generating language-specific compilers from interpreters. This typically reduces compilation time compared to the first projection. Previous work employed the second projection to some extent, however we are not aware of any usage of the generic second Futamura projection in a state-of-the-art language runtime. To solve the problems of self-application and code-size explosion, this paper proposes CompGen, an approach based on code generation of subsets of language interpreters. It is loosely based upon the idea of the second Futamura projection. Our implementation of CompGen for GraalVM shows that our usage of a novel code-generation algorithm allows us to generate efficient compilers that emit fast target programs which easily outperform the first Futamura projection in compilation time. We evaluated our approach with the high-performance JavaScript implementation of GraalVM and standard JavaScript benchmarks, showing that our approach achieves >2X speedups of partial evaluation.

This article presents the first memory allocation scheme for embedded systems having a scratch-pad memory whose size is unknown at compile time. A scratch-pad memory (SPM) is a fast compiler-managed SRAM that replaces the hardware-managed cache. All existing memory allocation schemes for SPM require the SPM size to be known at compile time. Unfortunately, because of this constraint, the resulting executable is tied to that size of SPM and is not portable to other processor implementations having a different SPM size. Size-portable code is valuable when programs are downloaded during deployment either via a network or portable media. Code downloads are used for fixing bugs or for enhancing functionality. The presence of different SPM sizes in different devices is common because of the evolution in VLSI technology across years. The result is that SPM cannot be used in such situations with downloaded codes. To overcome this limitation, our work presents a compiler method whose resulting executable is portable across SPMs of any size. Our technique is to employ a customized installer software, which decides the SPM allocation just before the program's first run, since the SPM size can be discovered at that time. The installer then, based on the decided allocation, modifies the program executable accordingly. The resulting executable places frequently used objects in SPM, considering both code and data for placement. To keep the overhead low, much of the preprocessing for the allocation is done at compile time. Results show that our benchmarks average a 41% speedup versus an all-DRAM allocation, while the optimal static allocation scheme, which knows the SPM size at compile time and is thus an unachievable upper-bound and is only slightly faster (45% faster than all-DRAM). Results also show that the overhead from our customized installer averages about 1.5% in code size, 2% in runtime, and 3% in compile time for our benchmarks.

Compilers for current programming languages enforce rigid restrictions on changes, during the compilation of a statement, in the set of associations between names and declarations, that is, on changes in the compile-time environment of the statement. These restrictions inhibit the writing of well-structured, modular programs, because such programs tend to require frequent switching between the environments associated with different modules. With current compilers, arbitrary switching of environments must be handled by means of macros at compile time or deferred until run time. Conventional macros are difficult to use in situations where it is desirable that the macro writer and the macro user be ignorant about each other's programs. When deferred until run time, frequent switching of environments becomes costly owing to the overhead associated with subroutine calls. In order to encourage the compartmentalization of program information and at the same time to allow efficient code generation, we propose the relaxation of present restrictions on changes in the compile-time environment. Specifically, we propose that a group of declarations can be defined as an environment and that arbitrary pieces of program text can state within which environment they are to be compiled. With such a mechanism, program text can be intermixed by means of macros without having to make the relevant declarations global or having to repeat the declarations in places other than the environment's definition.

Lambda-dropping: transforming recursive equations into programs with block structure

The traditional tradeoff when performing dynamic compilation is that of fast compilation time versus fast code performance. Most dynamic compilation systems for Java perform selective compilation and/or optimization at a method granularity. This is the not the optimal granularity level. However, compiling at a sub-method granularity is thought to be too complicated to be practical. This paper describes a straightforward technique for performing compilation and optimizations at a finer, sub-method granularity. We utilize dynamic profile data to determine intra-method code regions that are rarely or never executed, and compile and optimize the code without those regions. If a branch that was predicted to be rare is actually taken at run time, we fall back to the interpreter or dynamically compile another version of the code. By avoiding compiling and optimizing code that is rarely executed, we are able to decrease compile time significantly, with little to no degradation in performance. Futhermore, ignoring rarely-executed code can open up more optimization opportunities on the common paths. We present two optimizations---partial dead code elimination and rare-path-sensitive pointer and escape analysis---that take advantage of rare path information. Using these optimizations, our technique is able to improve performance beyond the compile time improvements

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.

A class of high-performance, low-cost architectures that rely on the ability to predict, at compile time, the order in which shared resources (such as shared memories) will be accessed by the processors is discussed. If this ordering can be predicted at compile time and enforced at run time, then no hardware or software overhead is required for resolving contention for shared resources, and extremely efficient interprocessor communication is possible. Further, no overhead (such as semaphore management) is required for synchronization of the programs running on the component processors. The hardware required to enforce the ordered access of the shared resources is very simple. The result is a very lean multiprocessor architecture with very efficient access to shared resources. A specific example using Motorola DSP96002 processors and a shared memory is described in detail. >

Inline expansion is very important for high performance VLIW, especially for microprocessors with static scheduling. Optimizations in optimizing compilers for VLIW duplicate code aggressively and lead to long compile time. Our inlining algorithm is based on heuristics that takes into account compile time explicitly. This made optimization more balanced and significantly reduced code growth and compile time compared to common inlining approach based on minimization of runtime within constraints. Instead of using hard constraints we are trading run time for compilation time in some proportion. Our heuristics predicts several key optimizations in evaluation of runtime and compile time: code scheduling, global copy propagation, dead code elimination and different loop optimizations. Optimizations prediction reduces the need in profile information which is rarely available in practice. Our implementation of inlining includes cloning, partial inlining and inlining across compilation modules in whole program mode. All this factors make dramatic impact on performance: our inlining implementation in the Elbrus optimizing compiler boost SPEC CPU2006 benchmark performance by factor of 1.41 at the cost of 12% increase of compile time and 7.7% increase of code size on average.