Design and Implementation of Java Just-in-Time Compiler

Early Java implementations relied on interpretation, leading to poor performance compared to compiled programs. Java just-in-time (JIT) compiler can compile Java programs at runtime, so it not only improves Java's performance prominently, but also preserves Java's portability. In this paper the design and implementing techniques of Java JIT compiler based on Chinese open system are discussed in detail. To enhance the portability, a translating method which combines the static simulating method and macro expansion method is adopted. The optimization technique for JIT compiler is also discussed and a way to evaluate the hotspots in Java programs is presented. Experiments have been conducted to verify JIT compilation technique as an efficient way to accelerate Java.

C-CORE:Clustering by Code Representation to Prioritize Test Cases in Compiler Testing

Edge devices,due to their limited computational and storage resources,often require the use of compilers for program optimization.Therefore,ensuring the security and reliability of these compilers is of paramount importance in the emerging field of edge AI.One widely used testing method for this purpose is fuzz testing,which detects bugs by inputting random test cases into the target program.However,this process consumes significant time and resources.To improve the efficiency of compiler fuzz testing,it is common practice to utilize test case prioritization techniques.Some researchers use machine learning to predict the code coverage of test cases,aiming to maximize the test capability for the target compiler by increasing the overall predicted coverage of the test cases.Nevertheless,these methods can only forecast the code coverage of the compiler at a specific optimization level,potentially missing many optimization-related bugs.In this paper,we introduce C-CORE(short for Clustering by Code Representation),the first framework to prioritize test cases according to their code representations,which are derived directly from the source codes.This approach avoids being limited to specific compiler states and extends to a broader range of compiler bugs.Specifically,we first train a scaled pre-trained programming language model to capture as many common features as possible from the test cases generated by a fuzzer.Using this pre-trained model,we then train two downstream models:one for predicting the likelihood of triggering a bug and another for identifying code representations associated with bugs.Subsequently,we cluster the test cases according to their code representations and select the highest-scoring test case from each cluster as the high-quality test case.This reduction in redundant testing cases leads to time savings.Comprehensive evaluation results reveal that code representations are better at distinguishing test capabilities,and C-CORE significantly enhances testing efficiency.Across four datasets,C-CORE increases the average of the percentage of faults detected(APFD)value by 0.16 to 0.31 and reduces test time by over 50% in 46% of cases.When compared to the best results from approaches using predicted code coverage,C-CORE improves the APFD value by 1.1% to 12.3% and achieves an overall time-saving of 159.1%.

基于互信息和Just-in-Time优化的回声状态网络

为了提高回声状态网络(ESN)的适应性,提出基于互信息(MI)和Just-in-Time(JIT)的优化方法,对ESN的输入伸缩参数以及输出层进行优化,所得网络称为MI-JIT-ESN.ESN的优化方法分为两部分:一是基于网络输入与输出之间的互信息,对网络的多个输入伸缩参数进行调整;二是基于JIT优化的局部输出层,对ESN的隐层输出数据进行局部重新建模,从而提升ESN输出层的回归拟合精度.将MI-JIT-ESN应用于青霉素补料分批发酵过程建模.结果显示,MI-JIT优化方法能提高模型的适应性,并优于其他比较方法.

面向VHDL语言编译器VCompiler93的研制

研制用于 VHDL语言的编译器 ,语法分析采用一便扫描和预测分析的策略 ,提出了适用于处理 VHDL语言中语法冲突、各类重载语法、静态层次确立等语法现象的具体算法 .开发出面向 VHDL93标准的 VHDL语言编译器 VCompiler93,该编译器运行正常 ,表明所采用的解决方案合理 ,算法有效 .

Calibration of soft sensor by using Just-in-time modeling and Ada Boost learning method

Soft sensor is an efficacious solution to predict the hard-to-measure target variable by using the process variables.In practical application scenarios, however, the feedback cycle of target variable is usually larger than that of the process variables, which causes the deficiency of prediction errors. Consequently soft sensor cannot be calibrated timely and deteriorates. We proposed a soft sensor calibration method by using Just-in-time modeling and Ada Boost learning method. A moving window consisting of a primary part and a secondary part is constructed.The primary part is made of history data from certain number of constant feedback cycles of target variable and the secondary part includes some coarse target values estimated initially by Just-in-time modeling during the latest feedback cycle of target variable. The data set of the whole moving window is processed by Ada Boost learning method to build an auxiliary estimation model and then target variable values of the latest corresponding feedback cycle are reestimated. Finally the soft sensor model is calibrated by using the reestimated target variable values when the target feedback is unavailable; otherwise using the feedback value. The feasibility and effectiveness of the proposed calibration method is tested and verified through a series of comparative experiments on a pH neutralization facility in our laboratory.

IDL Compiler API Design, Application and Validation Based on XML API

In order to adapt different languages and platforms, the paper discusses how to process and validate IDL symbol table and intermediate code by XML API. It puts emphasis on IDL AP1 extension towards DOM API based on the idea of combining XML with IDL compilers. At last, the IDL compiler designing framework based on XML AP! is given, in which compiler front end can be managed and validated by some XML techniques and tools, IDL API can be validated on the basis of test, so IDL intermediate code is provided with maintainability, portability and generation. IDL compiler can be developed and extended by XML-based API, which realizes versatility and portability of modern compiler.

Just-in-time learning based integrated MPC-ILC control for batch processes

Considering the two-dimension(2 D) characteristic and the unknown optimal trajectory problem of the batch processes, an integrated model predictive control-iterative learning control(MPC-ILC) for batch processes is proposed in this paper. Firstly, the batch-axis information and time-axis information are combined into one quadratic performance index. It implies the integration of ILC and MPC algorithm idea, which leads to superior tracking performance and better robustness against disturbance and uncertainty. To address the problem of the unknown optimal trajectory, both time-varying prediction horizon and end product quality control are employed. Moreover, an integrated 2 D just-in-time learning(JITL) model is used to improve the predictive accuracy. Furthermore, rigorous description and proof are presented to prove the convergence and tracking performance of the proposed MPC-ILC strategy. The simulation results show the effectiveness of the proposed method.

A New Efficient Obstacle Avoidance Control Method for Cars Based on Big Data and Just-in-Time Modeling

This paper provides a new obstacle avoidance control method for cars based on big data and just-in-time modeling. Just-in-time modeling is a new kind of data-driven control technique in the age of big data and is used in various real systems. The main property of the proposed method is that a gain and a control time which are parameters in the control input to avoid an encountered obstacle are computed from a database which includes a lot of driving data in various situations. Especially, the important advantage of the method is small computation time, and hence it realizes real-time obstacle avoidance control for cars. From some numerical simulations, it is showed that the new control method can make the car avoid various obstacles efficiently in comparison with the previous method.

Minimizing Distance Using Just-in-Time Teaching and Flipped Classroom

JiTT （Just-in-Time Teaching） and flipped classroom are two innovations in academia where high-tech and no-tech resources are blended to encourage more interaction and creativity among students. These two methods also help minimize the dependency on face-to-face lecture and make the class time more creative and effective. This study is the very first attempt of implementing these strategies in Bangladesh. It explores the prospect of JiTT and flipped classroom at tertiary level in Bangladesh. In the first part of this paper, recent studies on JiTT and flipped classroom are discussed to shed light on their effectiveness and problems. The latter part explains the data and research findings. Before collecting the data, JiTT and flipped classroom were used with 33 students of undergraduate level who were students of＂ELT Methodology＂ course. After the implementation of the methods, a questionnaire survey was conducted to collect data regarding their response to these two methods. The study reveals that JiTT and flipped classroom help cover more topics in a short time while enhancing students＇ interaction and participation. They make students come to the class prepared and reflect on their learning. Teachers can understand students need better and provide required feedback. The overall interaction among students and teachers can be improved using JiTT and flipped classroom since everyone is clear about each other＇s existing knowledge and learning objectives.

Supervised local and non-local structure preserving projections with application to just-in-time learning for adaptive soft sensor

In soft sensor field, just-in-time learning(JITL) is an effective approach to model nonlinear and time varying processes. However, most similarity criterions in JITL are computed in the input space only while ignoring important output information, which may lead to inaccurate construction of relevant sample set. To solve this problem, we propose a novel supervised feature extraction method suitable for the regression problem called supervised local and non-local structure preserving projections(SLNSPP), in which both input and output information can be easily and effectively incorporated through a newly defined similarity index. The SLNSPP can not only retain the virtue of locality preserving projections but also prevent faraway points from nearing after projection,which endues SLNSPP with powerful discriminating ability. Such two good properties of SLNSPP are desirable for JITL as they are expected to enhance the accuracy of similar sample selection. Consequently, we present a SLNSPP-JITL framework for developing adaptive soft sensor, including a sparse learning strategy to limit the scale and update the frequency of database. Finally, two case studies are conducted with benchmark datasets to evaluate the performance of the proposed schemes. The results demonstrate the effectiveness of LNSPP and SLNSPP.

Online Batch Process Monitoring Based on Just-in-Time Learning and Independent Component Analysis

A new method was developed for batch process monitoring in this paper. In the devdopad method, just-in-time learning （ JITL ） and independent component analysis （ICA） were integrated to build JITL-ICA monitoring scheme. JITL was employed to tackle with the characteristics of batch process such as inherent time- varying dynamics, multiple operating phases, and especially the case of uneven length stage. According to new coming test data, the most correlated segmentation was obtained from batch-wise unfolded training data by JITL. Then, ICA served as the principal components extraction approach. Therefore, the non.Gaussian distributed data can also be addressed under this modeling framework. The effectiveness and superiority of JITL-ICA based monitoring method was demonstrated by fed-batch penicillin fermentation.

A High Speed Signal Processing Machine -Its Architecture, Language and Compiler

A systolic array architecture computer (FXCQ) has been designed for signal processing. R can handle floating point data at very high speed. It is composed of 16 processing cells and a cache that are connected linearly and form a ring structure. All processing cells are identical and programmable. Each processing cell has the peak performance of 20 million floating-point operations per second (20MFLOPS). The machine therefore has a peak performance of 320 M FLOPS. It is integrated as an attached processor into a host system through VME bus interface. Programs for FXCQ are written in a high-level language -B language, which is supported by a parallel optimizing compiler. This paper describes the architecture of FXCQ, B language and its compiler.

The Implementation of the ASN.1-C++ Compiler and Its Application in IN

Syntax Notation One (ASN.1) has been widely used in specifications of high level communication protocol. It is also very important for Intelligent Networks Application Protocol(INAP). This paper presents the design and implementation of the ASN.1 C++ compiler. According to the ASN.1 text, this compiler can generate C++ code of functions for encoding and decoding the data types which are defined by ASN.1. These functions are based on the Basic Encoding Rules(BER) of ASN.1. They have been used in the CIN 01 and CIN 02 systems.

Application of the Just-in-Time Teaching(JiTT)Model in Bilingual Courses of Chinese-Foreign Cooperative Education

The teaching abilities of bilingual teachers have emerged as one of the major bottlenecks hindering the further development of Chinese-foreign cooperative education projects.Implementing the JiTT(Just-in-Time Teaching)model in bilingual courses of Chinese-foreign cooperative education can effectively integrate information technology with traditional classroom teaching.This integration enhances teaching quality and effectiveness,encourages university instructors to improve their diverse and collective capabilities,promotes digital development,and contributes to the enhancement of students’learning abilities and overall improvement.

Algorithms for Pre-Compiling Programs by Parallel Compilers

The paper addresses the challenge of transmitting a big number offiles stored in a data center(DC),encrypting them by compilers,and sending them through a network at an acceptable time.Face to the big number offiles,only one compiler may not be sufficient to encrypt data in an acceptable time.In this paper,we consider the problem of several compilers and the objective is tofind an algorithm that can give an efficient schedule for the givenfiles to be compiled by the compilers.The main objective of the work is to minimize the gap in the total size of assignedfiles between compilers.This minimization ensures the fair distribution offiles to different compilers.This problem is considered to be a very hard problem.This paper presents two research axes.Thefirst axis is related to architecture.We propose a novel pre-compiler architecture in this context.The second axis is algorithmic development.We develop six algorithms to solve the problem,in this context.These algorithms are based on the dispatching rules method,decomposition method,and an iterative approach.These algorithms give approximate solutions for the studied problem.An experimental result is imple-mented to show the performance of algorithms.Several indicators are used to measure the performance of the proposed algorithms.In addition,five classes are proposed to test the algorithms with a total of 2350 instances.A comparison between the proposed algorithms is presented in different tables discussed to show the performance of each algorithm.The result showed that the best algorithm is the Iterative-mixed Smallest-Longest-Heuristic(ISL)with a percentage equal to 97.7%and an average running time equal to 0.148 s.All other algorithms did not exceed 22%as a percentage.The best algorithm excluding ISL is Iterative-mixed Longest-Smallest Heuristic(ILS)with a percentage equal to 21,4%and an average running time equal to 0.150 s.

Constructing a Simple Verbal Compiler

The paper’s purpose is to design and program the four operation-calculators that receives voice instructions and runs them as either a voice or text phase. The Calculator simulates the work of the Compiler. The paper is a practical example programmed to support that it is possible to construct a verbal Compiler.

AN OBJECT ORIENTED C++ PARALLEL COMPILER SYSTEM

An object-oriented C++ parallel compiler System, called OOCPCS, is developed to facilitate programmers to write sequential programs using C++ or Annotated C++ language for parallel computahon. OOCPCS bases on an integrated object-oriented paradigm and large-grain data flow model, called OOLGDFM, and recognizes automatically parallel objects using parallel compiling techniques. The paper describes the object-oriented parallel model and realization of the System on networks.

Compiler IR-Based Program Encoding Method for Software Defect Prediction

With the continuous expansion of software applications,people’s requirements for software quality are increasing.Software defect prediction is an important technology to improve software quality.It often encodes the software into several features and applies the machine learning method to build defect prediction classifiers,which can estimate the software areas is clean or buggy.However,the current encoding methods are mainly based on the traditional manual features or the AST of source code.Traditional manual features are difficult to reflect the deep semantics of programs,and there is a lot of noise information in AST,which affects the expression of semantic features.To overcome the above deficiencies,we combined with the Convolutional Neural Networks(CNN)and proposed a novel compiler Intermediate Representation(IR)based program encoding method for software defect prediction(CIR-CNN).Specifically,our program encoding method is based on the compiler IR,which can eliminate a large amount of noise information in the syntax structure of the source code and facilitate the acquisition of more accurate semantic information.Secondly,with the help of data flow analysis,a Data Dependency Graph(DDG)is constructed on the compiler IR,which helps to capture the deeper semantic information of the program.Finally,we use the widely used CNN model to build a software defect prediction model,which can increase the adaptive ability of the method.To evaluate the performance of the CIR-CNN,we use seven projects from PROMISE datasets to set up comparative experiments.The experiments results show that,in WPDP,with our CIR-CNN method,the prediction accuracy was improved by 12%for the AST-encoded CNN-based model and by 20.9%for the traditional features-based LR model,respectively.And in CPDP,the AST-encoded DBNbased model was improved by 9.1%and the traditional features-based TCA+model by 19.2%,respectively.

A Preliminary Study of Just-in-Time Methods for a Seamless Public Transportation Information Framework

A seamless multimodal transit system has been a goal for transportation planners and users. Owing to the developments of advanced public transportation and telecom technologies such as automatic vehicle location (AVL) and real-time passenger information system, key just-in-time (JIT) concepts can now be realized in process design and coordinated scheduling to shape a seamless multimodal transit system. JIT refers to a production system that times both movements of goods during production and delivery from suppliers together. It meets the same logic for seamless multimodal services. Therefore, this study innovatively analyzes possible public transportation technologies for JIT uses;moreover, to layout the possible application frame-work of a transportation technology based JIT multimodal transit system for main station area in Taipei city. With this innovative framework, multimodal transit services can deliver to the right place at right time.

Constructing an AI Compiler for ARM Cortex-M Devices

The diversity of software and hardware forces programmers to spend a great deal of time optimizing their source code,which often requires specific treatment for each platform.The problem becomes critical on embedded devices,where computational and memory resources are strictly constrained.Compilers play an essential role in deploying source code on a target device through the backend.In this work,a novel backend for the Open Neural Network Compiler(ONNC)is proposed,which exploits machine learning to optimize code for the ARM Cortex-M device.The backend requires minimal changes to Open Neural Network Exchange(ONNX)models.Several novel optimization techniques are also incorporated in the backend,such as quantizing the ONNX model’s weight and automatically tuning the dimensions of operators in computations.The performance of the proposed framework is evaluated for two applications:handwritten digit recognition on the Modified National Institute of Standards and Technology(MNIST)dataset and model,and image classification on the Canadian Institute For Advanced Research and 10(CIFAR-10)dataset with the AlexNet-Light model.The system achieves 98.90%and 90.55%accuracy for handwritten digit recognition and image classification,respectively.Furthermore,the proposed architecture is significantly more lightweight than other state-of-theart models in terms of both computation time and generated source code complexity.From the system perspective,this work provides a novel approach to deploying direct computations from the available ONNX models to target devices by optimizing compilers while maintaining high efficiency in accuracy performance.