• Journal of Digital Convergence
• /
• v.18 no.12
• /
• pp.481-488
• /
• 2020

Symbolic regression is an analysis method that directly generates a function that can explain the relationsip between dependent and independent variables for a given data in regression analysis. Genetic Programming is the leading technology of research in this field. It has the advantage of being able to directly derive a model that can be interpreted compared to other regression analysis algorithms that seek to optimize parameters from a fixed model. In this study, we propse a symbolic regression algorithm using parallel genetic programming based on a coarse grained parallel model, and apply the proposed algorithm to PMLB data to analyze the effectiveness of the algorithm.

• International Journal of Reliability and Applications
• /
• v.12 no.2
• /
• pp.79-94
• /
• 2011

Redundancy-reliability allocation problems in multi-stage series-parallel systems are addressed in this study. Fuzzy programming techniques are proposed for finding satisfactory solutions. First, a multi-objective programming model is formulated for simultaneously maximizing system reliability and minimizing system total cost. Due to the nature of uncertainty in the problem, the fuzzy set theory and technique are used to convert the deterministic multi-objective programming model into a fuzzy nonlinear programming problem. A heuristic method is developed to get satisfactory solutions for the fuzzy nonlinear programming problem. A Pareto optimal solution is found with maximal degree of satisfaction from the interception area of fuzzy sets. A case study that is related to the electronic control unit installed on aircraft engine over-speed protection system is used to implement the developed approach. Results suggest that the developed fuzzy multi-objective programming model can effectively resolve the fuzzy and uncertain problem when design goals and constraints are not clearly confirmed at the initial conceptual design phase.

• Journal of KIISE
• /
• v.44 no.8
• /
• pp.791-802
• /
• 2017

Competition in the modern CPU market has shifted from speeding up the clock speed of a single core to increasing the number of cores. As such, there is a growing interest in parallel programming to maximize the use of resources of many core processors. In this paper, we propose parallel programming models in Haskell to find an advisable parallel programming model for many-core environments. Specifically, we used Eval monad and Cloud Haskell to develop two versions of parallel programs: plagiarism detection and K-means. Then, we evaluated the performance of the developed programs in 32-core and 120-core environments. The results of our experiment show that the Eval monad is highly efficient in an environment with a small number of cores. On the other hand, the Cloud Haskell runtime shows 37% improvement over Eval monad and the scalability shows a 134% improvement over Eval monad as the number of cores increases.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.14 no.2
• /
• pp.858-867
• /
• 2013

Hardware and software field are developed by environment of MPSOC. Also it is still working with economic world and academic world. This study focus on software side and try to classify from parallel programming design world. It can be divided by three; Data, Tasks, and Data flow model. Then we used Petri Net to CUDA and HOPES programmer and found how much they understand parallel programming for each side. We focus on two sides and what is different between their experience. Petri Net is easy to descript parallel program or parallel design pattern for Task, Data, and Hybird. This research can explain how they know and how much they know about parallel programming.

• KIPS Transactions on Computer and Communication Systems
• /
• v.4 no.10
• /
• pp.337-342
• /
• 2015

We develop a new parallelization method for Sieve of Eratosthenes algorithm, which enhances both computation speed and energy efficiency. A pipeline scheduling is included for better load balancing after proper workload partitioning. They run on multicore CPUs with hybrid parallel programming model which uses both message passing and multithreading computation. Experimental results performed on both small scale clusters and a PC with a mobile processor show significant improvement in execution time and energy consumptions.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.18 no.4
• /
• pp.165-172
• /
• 2023

Heterogeneous computing is a technology that utilizes different types of processors to perform parallel processing. It maximizes task processing and energy efficiency by leveraging various computing resources such as CPUs, GPUs, and FPGAs. On the other hand, edge computing has developed with IoT and 5G technologies. It is a distributed computing that utilizes computing resources close to clients, thereby offloading the central server. It has evolved to intelligent edge computing combined with artificial intelligence. Intelligent edge computing enables total data processing, such as context awareness, prediction, control, and simple processing for the data collected on the edge. If heterogeneous computing can be successfully applied in the edge, it is expected to maximize job processing efficiency while minimizing dependence on the central server. In this paper, experiments were conducted to verify the feasibility of various parallel programming models on high-end and low-end edge devices by using benchmark applications. We analyzed the performance of five parallel programming models on the Raspberry Pi 4 and Jetson Orin Nano as low-end and high-end devices, respectively. In the experiment, OpenACC showed the best performance on the low-end edge device and OpenSYCL on the high-end device due to the stability and optimization of system libraries.

• Journal of KIISE:Computing Practices and Letters
• /
• v.5 no.6
• /
• pp.708-718
• /
• 1999

본 논문에서 제안하는 시스템은 일반적인 병렬 시스템의 하드웨어 구조에서, 다중 스레드 계산 모델을 이용하여 객체 지향 프로그래밍 환경을 구현한 시스템이다. 제안하는 시스템을 효과적으로 구현하기 위하여 컴파일러와 실행 시간 시스템의 측면에서 여러 가지 기법을 제시한다. 컴파일러의 측면에서는 멤버 변수의 접근 분석, 메소드의 병렬성 분석 기법을 제시하고, 실행 시간 시스템에서는 실시간 스레드/메시지 결합, 프레임 공유 기법을 제시한다. 본 논문에서 제안된 프로그래밍 환경은, MPI 메시지 인터페이스를 이용하여 구현하였으며, 벤치마크 프로그램을 실행함으로써 성능 분석을 하였다. 분석의 결과는 실행시간 시스템의 여러 가지 기법들이 성능 향상에 많은 효과가 있음을 보여주며, 이러한 결과는 일반적인 병렬 시스템에서도 적용 가능하다.Abstract In this paper, we suggest an object-oriented programming environment with multithreaded computation model on general parallel processing systems. We developed many methods for our environment to be efficient : in compiler, the analysis of member variable and method parallelism, and in runtime system, thread/message merging and frame sharing. The programming environment is implemented with MPI message interface, and its performance is analyzed with executing benchmark programs. The results show that the developed methods have influence on performance improvement, and this improvement can be applied to general parallel processing systems.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.18 no.12
• /
• pp.1902-1918
• /
• 1993

Parallel architecture based on the von Neumann computation model has a limitation as a massively parallel architecture due to its inherent drawback of architectural features. The data-flow model of computation has a high programmability in software perspective and high scalability in hardware perspective. However, the practical programming and experimentaion of date-flow architectures are hardly available due to the absence of practical data-flow, we present a programming environment for performing the data-flow computation on conventional parallel machines in general, loosely compled multiprocessor system in particular. We build an emulator for tagged token data-flow architecture on the iPSC/2 hypercube, a loosely coupled multiprocessor system. The emulator is a shallow layer of software executing on an iPSC/2 system, and thus makes the iPSC/2 system work as a data-flow architecture from the programmer`s viewpoint. We implement various numerical and non-numerical algorithm in a data-flow assembler language, and then compare the performance of the program with those of the versions of conventional C language, Consequently, We verify the effectiveness of this programming environment based on the emulator in experimenting the data-flow computation on a conventional parallel machine.

• Journal of Digital Contents Society
• /
• v.13 no.2
• /
• pp.169-176
• /
• 2012

This paper presents a parallel object-oriented programming (OOP) platform for efficient visualization of three-dimensional content in CUDA environments. For this purpose, this paper discusses the features and limitations in implementing C++ object-oriented codes using CUDA and proposes the solutions. Also, it presents how to implement a 3D parallel visualization platform based on the MVC (Model/View/Controller) design pattern. Also, it provides sample implementations for integral MLS (iMLS) and signed distance fields (SDFs) based on the Marching Cubes and Raytracing. The proposed approach enables GPU parallel processing only by implementing simple interfaces. Based on this, developers can expect general benefits that are common in general OOP techniques including abstractization and inheritance. Though I implemented only two specific samples in this paper, I expect my approach can be widely applied to general computer graphics problems.

• Structural Engineering and Mechanics
• /
• v.7 no.5
• /
• pp.485-502
• /
• 1999

Using the continuous Hopfield network model as the basis to solve the general crisp and fuzzy constrained optimization problem is presented and examined. The model lies in its transformation to a parallel algorithm which distributes the work of numerical optimization to several simultaneously computing processors. The method is applied to different structural engineering design problems that demonstrate this usefulness, satisfaction or potential. The computing algorithm has been given and discussed for a designer who can program it without difficulty.