• International Journal of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.87-94
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• 2020

Recently, computer hardware is evolving toward increasing the number of computing cores, not increasing the clock speed. In order to use the performance of parallelized hardware to the maximum, the running program must also be parallelized. However, software developers are accustomed to sequential programs, and in most cases, write programs that operate sequentially. They also have a lot of difficulty designing and developing software in parallel. We propose a method to automatically convert a sequential C/C++ program into a parallelized program, and develop a parallelization tool that supports it. It supports open multiprocessing (OpenMP) and parallel patterns library (PPL) as a parallel framework. Perfect automatic parallelization is difficult due to dynamic features such as pointer operation and polymorphism in C/C++ language. This study focuses on verifying the conditions of parallelization rather than focusing on fully automatic parallelization, and providing advice to developers in detail if parallelization is not possible.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10a
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• pp.263-264
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• 2007

• Proceedings of the Korean Information Science Society Conference
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• 2002.04a
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• pp.601-603
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• 2002

공유메모리 병렬프로그램에서 경합은 프로그램의 비결정적인 수행을 초래하므로 디버깅을 위해서 반드시 탐지되어져야 한다. 경합을 수행 중에 디버깅하기 위한 기존의 도구들은 경합탐지 엔진과 시각화 엔진으로 구성된 경합디버깅 엔진을 특정 프로그램 모델에 의존적으로 적용하여 경합디버깅 환경을 구성한다. 이러한 도구들은 프로그램 모델의 변경 시에 경합디버깅 환경이 최적의 경합 디버깅 엔진으로 구성되지 못한다는 문제점이 있다. 본 논문에서는 OpenMP 병렬프로그랭에서 각 프로그램 모델에 따라 효과성, 효율성, 확장성 등을 고려한 경합탐지 엔진과 추상성을 고려한 시각화 엔진으로 경합디버깅 환경을 자동으로 생성하는 도구를 제안한다. 이 도구는 디버깅 대상이 되는 프로그램의 모델에 최적인 경합디버깅 엔진을 적용하므로 경합탐지 목적에 부합하는 최적의 성능과 효과적인 시각화를 제공한다. 따라서, 본 도구는 디버깅 환경을 생성하기 위한 부담을 줄여서 효과적인 디버깅을 할 수 있게 한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.3
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• pp.549-557
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• 2016

It is important for effective visualization to summarize not only a large amount of debugging information but also the mental models of abstract ideas. This paper presents an abstract visualization tool which provides effective visualization of thread structure and race information for OpenMP programs with critical sections and nested parallelism, using a partial order execution graph which captures logical concurrency among threads. This tool is supported by an on-the-fly trace-filtering technique to reduce space complexity of visualization information, and a graph abstraction technique to reduce visual complexity of nested parallelism and critical sections in the filtered trace. The graph abstraction of partial-order relation and race information is effective for understanding program execution and detecting to eliminate races, because the user can examine control flow of program and locations of races in a structural fashion.

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.3
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• pp.1-7
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• 2019

Previously, parallel computing was mainly used in areas requiring high computing performance, but nowadays, multicore CPUs and GPUs have become widespread, and parallel programming advantages can be obtained even in a PC environment. Various parallel programming frameworks using multicore CPUs such as OpenMP and PPL have been announced. Nvidia and AMD have developed parallel programming platforms and APIs for program developers to take advantage of multicore GPUs on their graphics cards. In this paper, we develop digital image transformation programs that runs on each of the major parallel programming frameworks, and measure the execution time. We analyze the characteristics of each framework through the execution time comparison. Also a constant K indicating the ratio of program execution time between different parallel computing environments is presented. Using this, it is possible to predict rough execution time without implementing a parallel program.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06c
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• pp.534-539
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• 2010

OpenMP 프로그램의 수행 중에 발생하는 자료 경합과 같은 병행성 오류는 디버깅을 위하여 반드시 탐지되어야만 한다. 그러나 이를 탐지하는 것은 어려운 일이다. 접근사건의 발생 후 관계를 기반으로하는 경합 탐지 기법은 프로그램의 수행 중에 발생하는 스레드의 병행성 정보를 식별하기위한 레이블을 생성하고, 생성된 스레드의 레이블을 기반으로 공유변수에 접근하는 사건을 접근역사를 통해 감시함으로써 경합을 탐지한다. 이러한 경합 탐지의 방법에서 레이블 생성을 위한 NR 레이블링 기법은 병행성 정보생성 시에 지역자료 구조를 사용함으로써 병목현상이 발생하지 않으며, 접근역사에 저장하는 레이블의 크기가 상수 값을 갖는 공간적 효율성을 제공한다. 또한 부모스레드의 정보역사를 정렬된 리스트 형태로 가져 병행성 정보 비교 시에 이진탐색이 가능하므로 시간적 효율성을 가지는 우수한 기법이다. 그러나, NR 레이블링은 레이블의 생성시에 부모스레드의 정보역사를 유지하기 위해서 내포 병렬성의 깊이에 의존하는 시간적 비용이 요구된다. 본 논문에서는 부모스레드의 정보역사 유지를 위해 상수적인 시간 및 공간적 복잡도를 갖도록 NR 레이블링 기법을 개선한다. 합성 프로그램을 이용하여 실험한 결과에서 개선된 기법은 최대 병렬성의 증가에 따라 레이블의 생성과 유지시 기존의 기법보다 평균 4.5배 빠르고, 레이블링을 위해 평균 3배 감소된 기억공간을 요구하며, 내포 병렬성에 의존적이지 않음을 보인다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.607-610
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• 2007

In this paper, proposes the monitoring system it will be able to trace the executed of each threads in OpenMP based a parallel program. The monitoring system of existing in uses each threads label information and the analysis technique which uses the access-history was most. This has the problem which raises the time and space complexity which is caused by with massive information creation. In this paper, only the thread which includes interest information it creates tracking information with the target. And it provides information which is intuitive to the user it provides the visualization system for to a same time. The visualization model is composed the images-information of a base. This does to be it will be able to understandable a program execute situation using an image processing technique. Therefore, this paper provides the parallel program an effective debugging environment.

• The KIPS Transactions:PartA
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• v.8A no.3
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• pp.253-260
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• 2001

It is important to detect races for debugging shared-memoy parallel programs, because the races cause unintended nondeterministic program execution. Previous on-the-fly techniques to detect races can not guarantee the first race detection in nested parallel programs. Detecting the first race is important for debugging parallel programs, since the removal of the first race may make the next occurred races disappear. In this paper, we presents an on-the-fly detection technique to detect all of the first races through the reexecution of the debugged programs. We assume that the debugged parallel program may have one-way nested parallel programs. The number of reexecution is at the least the nesting depth of the program in the worst case. The space complexity is O(VT) and the time complexity to detect race in each access of access history is O(T), where V is number of shared variables and T is the maximum parallelism of the program. This efficiency of our technique in each execution is the same with the previous on-the-fly detection techniques. Therefore, this technique makes debugging parallel programs more effective and practical.

• Journal of applied mathematics & informatics
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• v.41 no.6
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• pp.1317-1326
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• 2023

Today's computers, desktops and laptops were build with multi-core architecture. Developing and running serial programs in this multi-core architecture fritters away the resources and time. Parallel programming is the only solution for proper utilization of resources available in the modern computers. The major challenge in the multi-core environment is the designing of parallel algorithm and performance analysis. This paper describes the design and performance analysis of parallel algorithm by taking the Knight Tour problem as an example using OpenMP interface. Comparison has been made with performance of serial and parallel algorithm. The comparison shows that the proposed parallel algorithm achieves good performance compared to serial algorithm.

• Journal of the Korea Society of Computer and Information
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• v.22 no.11
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• pp.1-7
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• 2017

In this paper, we introduce three monitoring filtering techniques which reduce the overheads of dynamic data race detection. It is well known that detecting data races dynamically in multi-threaded programs is quite hard and troublesome task, because the dynamic detection techniques need to monitor all execution of a multi-threaded program and to analyse every conflicting memory and thread operations in the program. Thus, the main drawback of the dynamic analysis for detecting data races is the heavy additional time and space overheads for running the program. For the practicality, we also empirically compare the efficiency of three monitoring filtering techniques. The results using OpenMP benchmarks show that the filtering techniques are practical for dynamic data race detection, since they reduce the average runtime overhead to under 10% of that of the pure detection.