Efficiency Of Program Execution Research Articles

Research on the design of intelligent virtual simulation experimental teaching platform based on augmented reality

This research developed a product virtual simulation system based on augmented reality technology. The special database of product components was established, and the augmented reality product database of rice cooker was constructed and modified by the user and the enterprise design and production database. The HoloLens head-mounted device and MR Hand-eye interaction technology were used to realize the real-time simulation of the running state of the product design, and the prototype system was assembled. The Unity3D engine adopts the target method based on special features for the 3D display of products, which is a display environment with high requirements for user experience. The experimental simulation platform system has the advantages of strong portability, strong expansibility, realistic rendering effect and high program execution efficiency, which provides a good 3D panoramic display experience and usability experience for students' experiments and enterprise users after future industrialization.

Optimization of Computer Programming System Based on Deep Learning Mathematical Algorithm

With the rapid development of information technology, a series of information technologies, such as big data, artificial intelligence and cloud computing, have brought profound changes to our lives. And these, in fact, are inseparable from computer programming. In programming, data structure and algorithm constitute the program itself, and the optimization of algorithm directly determines the efficiency of program execution. Mathematical algorithm can play a good role in program optimization, which is particularly important for computer programming. Computer programming and mathematical algorithm have an inseparable close relationship, because mathematics and computer science have a very close relationship, the organic integration of the two is an effective way to carry out efficient programming. With the advent of the computer age, the influence industry of computer in people's daily life is becoming more and more important, and the connection with other disciplines is also closer, especially the proportion of cross development with the integration of mathematics discipline has become more and more, and the mathematical algorithm is indispensable for the computer. The efficiency and quality of computer programming can be effectively improved by optimizing mathematical algorithm in computer programming. Scientific data structure is closely related to the performance of the algorithm. In order to get close to the variables, it is more convenient to use the algorithm reasonably, reduce the memory space required by variables, and effectively improve the efficiency and level of mathematical algorithm. In this paper, a series of researches on the optimization and development of computer programming system are carried out by discussing mathematical algorithms.

Modeling and Optimization for Self-powered Non-volatile IoT Edge Devices with Ultra-low Harvesting Power

Energy harvesters are becoming increasingly popular as power sources for IoT edge devices. However, one of the intrinsic problems of energy harvester is that harvesting power is often weak and frequently interrupted. Therefore, energy harvesting powered edge devices have to work intermittently. To maintain execution progress, execution states need to be checkpointed into the non-volatile memory before each power failure. In this way, previous execution states can be resumed after power comes back again. Nevertheless, frequent checkpointing and low charging efficiency generate significant energy overhead. To alleviate these problems, this article conducts a thorough energy efficiency analysis and proposes three algorithms to maximize the energy efficiency of program execution. First, a non-volatile processor-aware task scheduling algorithm is proposed to reduce the size of checkpointing data. Second, a tentative checkpointing avoidance technique is proposed to avoid checkpointing for further reduction of checkpointing overhead. Finally, a dynamic wake-up strategy is proposed to wake up the edge device at proper voltages where the total hardware and software overhead is minimized for further energy efficiency maximization. The experiments on a real testbed demonstrate that, with the proposed algorithms, an edge device is resilient to the extremely weak and intermittent power supply and the energy efficiency can be achieved more than 2× higher than the fundamental baseline and 1.5× higher than the state-of-the-art technique.

Optimizing the performance of sensor network programs through estimation-based code profiling

With the development of sensor technology and embedded systems, building large-scale, low-cost sensor networks, which is a critical step to facilitating the application of pervasive sensing in the future, becomes possible. One of the major challenges in developing sensor network applications is to improve the execution efficiency of programs running on power-constrained embedded devices. While profiling-guided code optimization has been widely used as a compiler-level optimization technique for improving the performance of programs running on general-purpose computers, it has not been applied to sensor network programs due to some defects. In this paper, we overcome these defects and design a more effective profiling-guided code placement approach for sensor network programs. Specifically, we model the execution of sensor network programs taking nondeterministic inputs as discrete-time Markov processes, and propose a novel approach named Code Tomography to estimate parameters of the Markov models that reflect sensor network programs’ dynamic execution behaviors by only using end-to-end timing information measured at the start and end points of each procedure in the source code. The parameters estimated by Code Tomography are fed back to compilers to optimize the code placement. The evaluation results demonstrate that Code Tomography can achieve satisfactory estimation accuracy with low profiling overhead and the branch misprediction rate can be reduced after reorganizing the code placement based on the profiling results. Besides, Code Tomography can also be useful for purposes such as post-mortem analysis, debugging and energy profiling of sensor network programs.

The novel implicit LU-SGS parallel iterative method based on the diffusion equation of a nuclear reactor on a GPU cluster

GPU not only is used in the field of graphic technology but also has been widely used in areas needing a large number of numerical calculations. In the energy industry, because of low carbon, high energy density, high duration and other characteristics, the development of nuclear energy cannot easily be replaced by other energy sources. Management of core fuel is one of the major areas of concern in a nuclear power plant, and it is directly related to the economic benefits and cost of nuclear power. The large-scale reactor core expansion equation is large and complicated, so the calculation of the diffusion equation is crucial in the core fuel management process. In this paper, we use CUDA programming technology on a GPU cluster to run the LU-SGS parallel iterative calculation against the background of the diffusion equation of the reactor. We divide one-dimensional and two-dimensional mesh into a plurality of domains, with each domain evenly distributed on the GPU blocks. A parallel collision scheme is put forward that defines the virtual boundary of the grid exchange information and data transmission by non-stop collision. Compared with the serial program, the experiment shows that GPU greatly improves the efficiency of program execution and verifies that GPU is playing a much more important role in the field of numerical calculations.

Research of Camera Calibration Based on DSP

To take advantage of the high-efficiency and stability of DSP in the data processing and the functions of OpenCV library, this study brought forward a scheme that camera calibration in DSP embedded system calibration. An arithmetic of camera calibration based on OpenCV is designed by analyzing the camera model and lens distortion. The transplantation of EMCV to DSP is completed and the arithmetic of camera calibration is migrated and optimized based on the CCS development environment and the DSP/BIOS system. On the premise of realizing calibration function, this arithmetic improves the efficiency of program execution and the precision of calibration and lays the foundation for further research of the visual location based on DSP embedded system.

Study on Chemical Materials with Design of Chemical Molecular Structure Images Identification System Based on VC++ and MATLAB

In this paper, the system of chemical materials with design of chemical molecular structure images identification is introduced in detail. In order to reduce the algorithm complexity, increase the program execution efficiency, the system is completed based on the hybrid programming of VC++6.0 and MATLAB. The method proposed combines the advantages of VC++ and MATLAB, it shortens the time to write code algorithm, reduces the workload in programming and simultaneously increases the execution efficiency.

Parallel Implementation of Single-Source Shortest Path Algorithm Based on Haloop

As a typical problem of graph theory, Single-Source Shortest Path(SSSP) has a wide range of applications and research. The traditional MapReduce framework such as Hadoop has been applied to SSSP problem. However, in this way, it may need writing and reading the disk frequently, transmitting data largely, beacuse of SSSP’s iterative. Haloop is a parallel programming framework which makes an improvement based on Hadoop framework, and adapts itself to iterative programming. Hence, this article represents the SSSP problem with an iterative way firstly, and then we put forward the implementation mechanism of SSSP algorithm based on Haloop. Though testing and analyzing, the implementation based on Haloop obviously improves the efficiency of program execution, compared with the traditional realization mechanism.

Type-Directed Specialization of Polymorphism

Flexibility of programming and efficiency of program execution are two important features of a programming language. Unfortunately, however, these two features conflict with each other in design and implementation of a modern statically typed programming language. Flexibility is achieved by a high-degree of polymorphism , which is based on generic primitives in an abstract model of computation, while efficiency requires optimal use of low-level primitives specialized to individual data structures. The motivation of this work is to reconcile these two features by developing a mechanism for specializing polymorphic primitives based on static type information. We analyze the existing methods for compiling a record calculus and an unboxed calculus, extract their common structure, and develop a framework for type-directed specialization of polymorphism.

A new algorithm for composite hoisting and strength reduction optimisation

Unifications of optimising transformations are motivated by the desire to reduce the cost of optimisation. Two unifications using code placement techniques based on the suppression of partial redundancies have been reported in the literature [5, 8]. A common drawback of these unifications has been their failure to provide sufficient guarantees regarding improvement in execution efficiency of programs. This has tended to limit their effectiveness from a practical viewpoint. This paper proposes a new algorithm to unify the optimising transformations of hoisting, loop optimisation and strength reduction. The algorithm uses a refined framework for code placement that provides guarantees of improvement in execution efficiency of programs. The properties of this algorithm are formally proved.

An Automatic Technique for Selection of Data Representations in SETL Programs

Publisher Summary This chapter highlights an automatic technique for selection of data representations in SETL programs. SETL is a very-high-level programming language supporting set-theoretic syntax and semantics. It allows algorithms to be programmed rapidly and succinctly without requiring data-structure declarations to be supplied. The chapter describes a new technique for automatic selection of appropriate data representations during compile time for programs with omitted declarations. It also presents an efficient data representation selection algorithm, whose complexity is comparable with those of the fastest known general data-flow algorithms of Tarjan and Reif. The level of a programming language is determined by the power of its semantic primitives, which influence the ease and speed of programming in the language profoundly. The chapter also describes how the SETL compiler chooses data structure implementations. SETL supports logical quantifiers and the abstract data type set as the prominent high-level features of the language. Programmers are intended to use these features as much as possible and leave more specific data structure and control structure decisions to the SETL compiler. Most other very high-level languages start by incorporating these same features. SETL is a very-high-level programming language supporting set-theoretic syntax and semantics. It allows algorithms to be programmed rapidly and succinctly without requiring data-structure declarations to be supplied. Such declarations can be manually specified later, without recoding the program, to improve the efficiency of program execution. We describe a new technique for automatic selection of appropriate data representations during compile time for programs with omitted declarations and present an efficient data representation selection algorithm, whose complexity is comparable with those of the fastest known general data-flow algorithms of Tarjan and Reif.

Machine computation of equilibrium concentrations—some practical considerations

The calculation of equilibrium concentrations in solution by means of seven published computer programs is critically evaluated. Six equilibrium systems were chosen to illustrate the capabilities of these programs. It was found, in general, that the choice of algorithm determines the efficiency of program execution. In all cases studied the problems were most rapidly solved by a modified Newton—Raphson approach that employs Choleski factoring.