Earticle

본 논문은 호스트(PC) 기반의 직렬처리 방식으로 도로영역 추출 방식에 디바이스(Graphic Card) 기반의 병렬 처리 방식을 추가함으로써 보다 향상된 처리 속도를 가지는 도로영역검출을 제안하였다. OpenCV CUDA는 기존의 OpenCV와 CUDA를 연동하여 병렬 처리 방식의 많은 함수들을 지원한다. 또한 OpenCV와 CUDA 연동 시 환경 설정이 완료된 OpenCV CUDA 함수들은 사용자의 디바이스(Graphic Card) 사양에 최적화된다. 따라서 OpenCV CUDA 사용은 알고리즘 검증 및 시뮬레이션 결과 도출의 용이성을 제공한다. 제안된 방법은 OpenCV CUDA 와 NVIDIA GeForce GTX 560 Ti 모델의 그래픽 카드를 사용하여 기존 방식보다 3.09배 빠른 처리 속도를 가짐을 실험을 통해 검증한다.

In this paper, we propose a processing speed improvement by adding a parallel processing based on device(graphic card) into a road region extraction by host(PC) based serial processing. The OpenCV CUDA supports the many functions of parallel processing method by interworking a conventional OpenCV with CUDA. Also, when interworking the OpenCV and CUDA, OpenCV functions completed a configuration are optimized the User’s device(Graphic Card) specifications. Thus, OpenCV CUDA usage provides an algorithm verification and easiness of simulation result deduction. The proposed method is verified that the proposed method has a about 3.09 times faster processing speed than a conventional method by using OpenCV CUDA and graphic card of NVIDIA GeForce GTX 560 Ti model through experimentation.

영상 분할은 의료 영상 처리에서 중요한 역할을 수행하지만 영상 분할 알고리즘의 많은 연 산량은 실생활에서의 사용 을 제약하는 요인이 된다. 따라서 본 논문에서는 본 논문에서는 고해상도 의료 영상 분할 알 고리즘의 실시간 처리를 위해 GPGPU(general purpose graphics processing unit)상에서 영상 분할 알고리즘인 Fuzzy C-Means (FCM)의 병렬구현 방법을 제안한다. 본 논문에서는 GPU상에서 FCM 알고리즘의 고속화를 위해 대상 영상을 재 구성하고, 단일 쉐이더 코어에서 필요한 레지스터 개수를 바탕으로 FCM 알고리즘을 위한 최적의 쓰레드(thread) 와 블록(block)의 크기를 계산한다. 성능 평가를 위해 본 논문에서는 총 5가지 크기의 의료 영상(64×64, 128×128, 256×256, 512×512, 1,024×1,024)을 이용하여 단일 CPU대비 향상된 실행 시간을 측정하였고, 모 의실험 결과 64x64 영상을 제외한 나머지 영상에서 단일 CPU 보다 약 4배 높은 실행 시간 의 향상을 나타내었다.

Image segmentation plays a crucial role in numerous biomedical imaging applications. However, its high computational complexities require substantial amount of time and have limited its use in real applications. With this reason, this paper implements a fuzzy c-means (FCM) clustering algorithm on general purpose graphics processing unit (GPGPU) in order to run it in real time. This paper first reconfigures a target medical image for real-time processing of FCM, and calculates optimal sizes of threads and blocks for FCM based on the number of registers required by a shader core. To evaluate the proposed approach on GPGPU, this paper measures execution times to complete FCM on GPGPU by utilizing medical images at different resolutions (64×64, 128×128, 256×256, 512×512, 1,024×1,024). Experimental results indicate that execution times on GPGPU are approximately 4-fold faster than those on a single CPU for all of medical images except an 64×64 medical image.

GPU는 높은 연산 성능에 비해 그래픽 처리를 하지 않을 경우에는 유휴상태에 놓여있다. 최근 유휴상태인 GPU를 이용하여 그래프 문제, 대용량 탐색 및 정렬, 행렬 연산 등 다양한 문제들을 병렬처리 하는 GPGPU가 각광받고 있고 NVIDIA사의 CUDA플랫폼의 등장으로 이러한 흐름은 더욱 가속화 되고 있다. GPGPU의 발전에 맞추어 GPU상에서 몇몇의 암호 알고리즘들이 구현되었지만, 스트림 암호의 경우에는 최근에서야 ECRYPT에 의해 표준이 제정되었기 때문에 아직 연구 결과가 미비하다. 이에 본 논문에서는 ECRYPT의 소프트웨어 기반 암호인 Salsa20을 GPU상에서 구현하고 이를 최적화하였으며, 파이프라이닝 기법을 사용하여 성능을 극대화하였다. 본 논문의 실험 결과에 따르면 CUDA를 사용한 Salsa20은 6.2Gbps의 암호화 속도를 보여주어 138.7Mbps의 성능을 보인 CPU상에서의 구현에 비해 무려 45배 이상의 성능 향상을 보여주었다.

Recent performance improvements in GPU enabled optimized parallel implementation of various algorithmsincluding graph algorithms, searching and sorting of massive data, matrix operations, and so on. After theadvent of CUDA platform of NVIDIA, more algorithms including several cryptographic algorithms are beingimplemented over GPUs. However, little work has been done on the parallel implementation of stream ciphersbecause it hasn’t been long since the first standard for stream cipher were published by ECRYPT. In thispaper, we provide a parallel implementation of Salsa20 stream cipher, one of the four software-based streamciphers in the ECRYPT standard, over a GPU, and optimize its performance using a pipelining technique.According to our experimental results, the throughput of Salsa20 on the CUDA platform is up to 6.2Gbps,which is approximately 45 times faster than the implementation over a CPU with 138.7Mbps.

This paper suggests a visual debugging plaftorm based on the game engine, Unity3D for massive parallel processing routines implemented in CUDA. In general, it is tiresome to debug or check the accuracy of numerical geometry information results calculated in a parallel way by GPU; usually, developers would pick and check each numerical value by rummaging overwhelming lines of seemingly meaningless numbers. This manual process is less productive and time-consuming. Also, it is not easy to produce some continuous movements of geometry information to check the validity of implemented CUDA codes for realtime geometry processing applications. To solve those problems, this paper presents a way to use Unity3D game engine to visually and interactively debug CUDA implementations. Also, some practical test results are presented with discussions on limitations of Unity3D as a CUDA debugging platform.

기존 하드웨어 기반의 소프트웨어 검증 플랫폼이 가지는 제한 조건을 해결하는 방안으로 위성 개발 초기부터 소프트웨어 기반의 위성 시뮬레이터 개발이 함께 시작되며, 위성 시뮬레이터를 활용할 경우 탑재소프트웨어 개발이 지속적으로 이루어 질 수 있는 큰 장점을 가지게 된다. 위성 시뮬레이터는 탑재컴퓨터, 위성의 전자장비 그리고 탑재체까지 모두 모사해주며 소프트웨어 개발자들이 사용할 수 있도록 쉽게 복제, 배포가 가능하며 위성 하드웨어 형상이 변경되더라도 적용 및 변경이 용이하다. 그리고 실제 하드웨어 에서 동작하는 탑재소프트웨어를 별도의 수정 없이 로딩할 수 있으며, 개발자를 위한 디버깅 채널과 테스트 환경을 제공하며 실제 수행시간 보다 빠르게 가속화 할 수 있는 기능을 제공한다. 본 논문에서는 현재 개발 중인 정지궤도복합위성의 특징인 Hot-Standby 잉여구조를 지원하기 위한 위성 시뮬레이터의 구조와 개발방안을 제시하고, 시뮬레이터 기반에서 탑재소프트웨어 개발 및 테스트 방안을 제시한다.

The software-based satellite simulator has been developed from the start of the project to resolve the restriction and limitation of using hardware-based software development platform. It enables the development of flight software to be performed continuously since initial phase. The satellite simulator emulates the on-board computer, I/O modules, electronics and payloads, and it can be easily adapted and changed on hardware configuration change. It supports the debugging and test facilities for software engineers to develop flight software. Also the flight software can be loaded without any modification and can be executed as faster than real-time. This paper presents the architecture and design of software-based GEO satellite simulator which has hot-standby redundancy mechanism, and flight software development and test under this environment.

최근 페이스북, 트위터 등의 SNS(Social Networking Service)가 발전함에 따라, 사용자가 생성하는 데이터가 급격히 증가하고 있다. 사용자 데이터는 민감한 개인정보를 포함하기 때문에, 원본 데이터를 공격자로부터 보호하기 위해서는 데이터를 암호화하는 것이 필요하다. 따라서 암호화된 데이터의 복호화 없이 질의를 처리하는 암호화 질의 처리 기법이 제안되었다. 그러나 기존의 질의처리 기법은 암호화 데이터에 대한 색인 구조를 구축하고 이를 순차적 으로 탐색하기 때문에, 데이터의 크기가 증가함에 따라 질의탐색 비용이 증가하는 문제점이 존재한다. 이를 위해, P.B.Volk, et al.은 prefix 트리 기반 병렬 질의처리 알고리즘을 제안하였다. 제안하는 알고리즘은 암호화된 데이 터를 위해 prefix 트리 구조를 구축하고, 트리를 부분 트리로 분할하여 생성된 모든 부분 트리를 병렬적으로 탐색한 다. 그러나 이 알고리즘은 모든 부분 트리를 탐색하기 때문에, 트리 깊이에 따라 연산 비용이 급격히 증가하는 문제 점이 존재한다. 아울러, 이 알고리즘은 범위 질의나 부분 매칭 등의 다양한 질의를 지원하지 못하는 문제점이 존재 한다. 이러한 문제를 해결하기 위해, 본 논문에서는 prefix 트리 및 해시 테이블을 사용하는 GPU 기반 병렬 질의처 리 알고리즘을 제안한다. 제안하는 알고리즘은 prefix 트리 loop-up 테이블을 사용하여 범위 질의 및 부분매칭 질 의를 지원한다. 아울러 제안하는 알고리즘이 기존 P.B.Volk, et al. 의 알고리즘보다 검색 시간 측면에서 약 30% 우수한 성능을 나타냄을 보인다.

Recently, social networking services, such as Facebook and Twitter, have been widely used, so the amount of the data created by users has been dramatically increased. Because the user-created data can contain privacy information, it is required to encrypt the data for protecting the original data from adversaries. Thus, an encrypted query processing scheme has been proposed to process the query without the decryption of the encrypted data. The existing schemes construct an index for the encrypted data, so they can process the query by sequentially accessing the index. As a result, the query processing cost increases as the amount of the data is increased. For this, P.B.Volk, et al. proposed a prefix-tree based parallel query processing algorithm. The algorithm constructs a prefix-tree structure for the encrypted data and searches all sub-trees on GPU in parallel by dividing the tree into sub-trees. However, the algorithm has a problem that its computational cost is highly increased according to the depth of the tree because it searches all sub-trees. In addition, the algorithm does not support the various types of queries, such as a range query and a partial matching query. To solve these problems, we, in this paper, propose a GPU-based parallel query processing algorithm using both a prefix-tree and a hash table. By using the prefix-tree look-up table, the proposed algorithm can support both a range query and a partial matching query. In addition, we show that the proposed algorithm is about 30% better on retrieval performance than the existing algorithm by P.B.Volk, et al.

Cloud computing is a technology in which the Cloud Service Providers (CSP) provide many virtual servers to the users to store their information in the cloud. The faults occurring on the assignment and dismission of the virtual machines, the processing cost in the allocation of resources must also be considered. The parallel processing of the information on the virtual machines must be done effectively and in an efficient manner. A variety of systems were developed to facilitate Many Task Computing (MTC). These systems aim to hide the issues of parallelism and fault tolerant and they are used in many applications. In this paper, we introduced Nephele, a data processing framework to exploit dynamic resource provisioning offered by IaaS clouds. The performance evaluation of the virtual machines has been evaluated and the allocation and de-allocation of job tasks to the specific virtual machines has also been considered. A performance comparison with the well known data processing framework hadoop has been done. Thus this paper tells about the effective and efficient manner of processing the data by parallel processing and allocating the correct resources for the desired task. It also helps to reduce the cost of resource utilization by exploiting the dynamic resource utilization.

Processing very large graphs efficiently is a challenging task. Distributed graph processing systems process the billion-scale graphs efficiently but incur overheads of partitioning and distribution of the large graph over a cluster of nodes. In order to overcome these problems a disk-based engine, GraphChi was proposed recently that processes the graph in chunks on a single PC. GraphChi significantly outperformed all the representative distributed processing frameworks. Still, we observe that GraphChi incurs some serious degradation in performance due to 1) high number of non-sequential I/Os for processing every chunk of the graph; and 2) limited parallelism to process the graph. In this paper, we propose a novel engine named BiShard Parallel Processor (BSPP) to efficiently process billions-scale graphs on a single PC. We introduce a new storage structure BiShard. BiShard divides the large graph into subgraphs and maintains the in and out edges separately. This storage mechanism significantly reduces the number of non-sequential I/Os. We implement a new processing model named BiShard Parallel (BSP) on top of Bishard. BSP exploits the properties of Bishard to enable full CPU parallelism for processing the graph. Our experiments on real large graphs show that our solution significantly outperforms GraphChi.

The technological demand for graphically generating natural plants in real time recently have been more and more increasing in a variety of interactive content-creating areas such as computer games. In this paper, we propose a GPU-driven high-speed parallel processing algorithm for generating trees and their branches and leaves in real time. The method that we propose ensures the realistic generation of a multitude of trees and motions of their branches and leaves triggered by external force while it maintains the stability of the system.

3D Graphic Rendering has been used to express realistic, 3-dimensional, and emphasized effects in the graphics. As 3D Graphic Rendering developed and became more prevalent, the need for acceleration in data processing grew as well, leading to a development of GPU (Graphic Processing Unit) and shading language used for GPU such as GLSL (OpenGL Shading Language) and HLSL (Higher Language Shading Language). 3D Graphic Rendering based on GPU, however, clearly has its limitation in processing complicated calculations such as calculating curvatures of the surface or ray tracing method, especially as the greater magnitude of the 3D polygonal model data is being used. The following paper will discuss the new method of 3D graphic rendering that is based on faster GPU parallel processing system called CUDA (Compute Unified Device Architecture) to administer 3D polygonal model data and process calculations. In the paper, we will discuss about the characteristics of CUDA and test for graphic rendering of 3D polygonal model according to those characteristics. We will also examine whether it is possible to accelerate the graphic rendering process using CUDA for 3D graphic rendering.

This paper proposes a method for optimizing and parallelizing a pedestrian detection algorithm based on CENTRIST in the embedded environment. Pedestrian detection is applied to various areas, but there is a difficulty in the real-time processing of pedestrian detection in the embedded environment. In this paper, a pedestrian detection algorithm that has improved performance by reducing unnecessary memory access due to duplicate computations for real-time processing. The proposed algorithm was implemented in the ALDEBARAN embedded processor(300MHz) which can perform parallel processing. For efficient parallel processing, images were divided into halves which were then processed separately in two CPU cores and the volume of computations was balanced between the two CPU cores. For input images, 512x360 sized images were used. The single core showed the performance of 3.1 frames per second. In the dual core for which the wait time in the process of parallelizing was reduced, the performance improved by about 55% to 4.8 frames per second.

Hadoop–a popular open-source implementation of MapReduce is widely used for the analysis of large datasets. The current Hadoop implementation assumes that computing nodes in a cluster are homogeneous in nature. In this paper we evaluate performance of Hadoop Platform and Oracle for Distributed Parallel Processing in large datasets. For evaluation, we implement a prototype of a virtual datacenter using distributed and parallel computing technology. The purpose of this paper is to reduce datacenter implementation cost using commodity hardware and provide high performance. Hadoop is installed on a commodity Linux cluster the distributed processing of large data sets across clusters of computers using distributed and parallel computing architecture. This paper also helps to explain about some new technology and framework which are open source; that can easily utilize those technologies for our complex data analysis which resembling structured, semi structured and non-structured data. Here we tried to demonstrate a performance comparison by executing some queries between distributed parallel computing system and traditional single computing system. For the simulation of the infrastructure Hadoop cluster has been used for distributed parallel processing and Oracle 11g is used for traditional single processing system. We prepare three virtual host for Hadoop cluster and a high-end hardware for Oracle 11g.

Intelligent Transportation Systems (ITS) is one of the important application areas of the Internet of Things (IoT). The key issue is how to process the huge events generated by IoT system to support ITS. In this paper a proactive parallel complex event processing method is proposed for congestion control in large-scale ITS. A Bayesian model averaging method is used to obtain accurate predictions under different event context. Based on the predictive analysis, a parallel Markov decision processes model is designed to support decision making for large-scale ITS. An optimized parallel policy iteration algorithm is proposed based on state partition and policy decomposition. The experimental evaluations show that this method has good accuracy and scalability when used to process congestion control in large-scale ITS.

This paper discusses an effective synchronization algorithm. It is different from the existing synchronization methods by inserting appropriate synchronization instructions between statements according to different kinds of data dependences. The overhead caused by too many synchronization instructions in a loop can be a critical problem. Synchronization optimization is a method which discriminates and eliminates the redundant synchronization instructions in a loop. In this paper, a new synchronization optimization algorithm - ICPG (Improved Controlled Path Graph) - is developed, and the algorithm is extended to the case of nested loops. In case of nested loops, the existing methods were not safe because of the incomplete management in hyper-rectangle areas, etc. But, the errorneous methods are completely corrected in this paper. Finally, performance analysis using simulation on the UNIX operating system is carried out. It is shown that the ICPG algorithm is much more effective than the existing methods.

도로이동 오염원 대기환경 빅데이터는 상시 교통량 조사장비인 AVC, VDS, WIM, DTG를 활용한 차종, 속도, 하중 등 실시간 교통류 데이터와 GIS를 활용한 도로형상(오르막, 내리막, 회전구간) 데이터를 연계한 교통류 데이터로 구성되어 있다. 또한, 일반적인 데이터와 달리 단위시간 당 데이터가 많이 발생하고, 다양한 포맷을 가지고 있다. 특히, 이들 상세 교통류 정보로 수집되는 대용량의 실시간 데이터들은 약 총 740만 건/시간 이상이 수집되어 저장 및 가공되 기 때문에 효율적으로 데이터를 처리할 수 있는 시스템이 필요하다. 따라서 본 연구에서는 도로이동 오염뭔 대기환경 빅데이터 시각화를 위한 오픈소스 기반의 데이터 병렬처리 성능 최적화 연구를 수행한다.

Road movement pollutant air environment big data is a link between real-time traffic data such as vehicle type, speed, and load using AVC, VDS, WIM, and DTG, which are always traffic volume survey equipment, and road shape (uphill, downhill, turning section) data using GIS. It consists of traffic flow data. Also, unlike general data, a lot of data per unit time is generated and has various formats. In particular, since about 7.4 million cases/hour or more of large-scale real-time data collected as detailed traffic flow information are collected, stored and processed, a system that can efficiently process data is required. Therefore, in this study, an open source-based data parallel processing performance optimization study is conducted for the visualization of big data in the air environment of road transport pollution.

Circuit testing process is very important in IC Manufacturing there are two ways in research for circuit testing improvement. These are ATPG Tool Design and Test simulation application. We are interested in how to use parallel technique such as one-side communication, parallel IO and dynamic Process with data partition for circuit testing improvement and we use one-side communication technique in this paper. The parallel ATPG Tool can reduce the test pattern sets of the circuit that is designed in laboratory for make sure that the fault is not occur. After that, we use result for parallel circuit test simulation to find fault between designed circuit and tested circuit. From the experiment, We use less execution time than non-parallel Process. And we can set more parameter for less test size. Previous experiment we can't do it because some parameter will affect much waste time. But in the research, if we use the best ATPG Tool can optimize to least test sets and parallel circuit testing application will not work. Because there are too little test set for circuit testing application. In this paper we use a standard sequential circuit of ISCAS89.

최근 UAV를 활용하는 정밀 추적이나 임무완수 등의 군사 목적의 연구가 활발하게 진행되고 있다. 특히 앞서가는 유도 UAV의 자세 정보를 추정하고 이 정보를 이용하여 임무 UAV가 스텔스로 따라가서 자신의 임무를 완수하는 기능이 필요한 경우에는 유도 UAV의 자세 정보 추정 속도를 실시간으로 처리 해야만 한다. 최근까지 영상처리와 칼만 필터를 사용해서 앞서가는 유도 UAV의 자세정보를 정밀하게 추정하는 연구가 수행되어 왔으나 처리과정의 순차처리로 인해 처리속도에 있어 문제점이 있어왔다. 따라서 본 연구에서는 영상 처리에 있어 처리영역을 전체영역이 아닌 물체를 포함하는 ROI 영역으로 한정하고 또한 연속적인 처리 과정을 OpenMP 기반의 멀티스레드로 분배하고 스레드동기를 맞추어서 병렬 형태로 처리함으로써 자세정보 추정 속도를 향상시킬 수 있는 방법을 제안한다. 구현 결과를 통해서 기본의 처리에 비해 45%이상 처리 속도를 향상시킴으로써 실시간처리가 가능하게 되어 임무 UAV의 추적 기능 향상을 통한 임무 완수 가능성을 증가시킬 수 있음을 확인하였다.

Recently, researches for military purposes such as precision tracking and mission completion using UAVs have been actively conducted. In particular, if the posture information of the leading UAV is estimated and the mission UAV uses this information to follow in stealth and complete its mission, the speed of the posture information estimation of the guide UAV must be processed in real time. Until recently, research has been conducted to accurately estimate the posture information of the leading UAV using image processing and Kalman filters, but there has been a problem in processing speed due to the sequential processing of the processing process. Therefore, in this study we propose a way to improve processing speed by applying methods that the image processing area is limited to the ROI area including the object, not the entire area, and the continuous processing is distributed to OpenMP-based multi-threads and processed in parallel with thread synchronization to estimate attitude information. Based on the experimental results, it was confirmed that real-time processing is possible by improving the processing speed by more than 45% compared to the basic processing, and thus the possibility of completing the mission can be increased by improving the tracking and estimating speed of the mission UAV.

Many studies have been done on the integral imaging pickup whose objective is to get efficiently elemental images from a lens array with respect to three-dimensional (3D) objects. In the integral imaging pickup process, it is necessary to render an elemental image from each elemental lens in a lens array for 3D objects, and then to combine them into one total image. The multiple viewpoint rendering (MVR) is one of various methods for integral imaging pickup. This method, however, has the computing and rendering time problem for obtaining element images from a lot of elemental lens. In order to solve the problems, in this paper, we propose a parallel MVR (PMVR) method to generate elemental images in a parallel through distribution of elemental lenses into multiple threads simultaneously. As a result, the computation time of integral imaging using PMVR is reduced significantly rather than a sequential approach and then we showed that the PMVR is very useful.

This paper describes a two-step Tikhonov regularization procedure for converting the steady shear data generated by parallel-disk viscometers, in the presence of wall slip, into a shear stress-shear rate function and a wall shear stress-slip velocity functions. If the material under test has a yield stress or a critical wall shear stress below which no slip is observed the method will also provide an estimate of these stresses. Amplification of measurement noise is kept under control by the introduction of two separate regularization parameters and Generalized Cross Validation is used to guide the selection of these parameters. The performance of this procedure is demonstrated by applying it to the parallel disk data of an oil-in-water emulsion, of a foam and of a mayonnaise.

This paper deals with a parallel processing uninterruptible power supply (UPS) for sudden voltage fluctuation in computer applications to integrate power quality improvement, load voltage stabilization and UPS. To reduce the complexity, cost and number of power conversions, which results in higher efficiency, only one voltage-controlled voltage source inverter (VCVSI) is used. The system provides sinusoidal voltage at the fundamental value of 220V/60Hz for the load during abnormal utility power conditions or grid failure. Also, the system can be operated to mitigate the harmonic current and voltage demand from nonlinear loads and provide voltage stabilization for loads when sudden voltage fluctuation occur, such as sag and swell. System operation simulation demonstrates that the system protects against outages caused by abnormal utility power conditions and sudden voltage fluctuations and changes.