Earticle

최근 유전체 분석 기법과 시퀀싱 기기의 발전에 따라 개인별 맞춤 의학의 실현에 관한 기대가 높아지고 있다. 개인 맞춤 의학은 개인의 유전 정보를 분석하고 이 들의 특이성을 파악하여, 개인의 질병 예방 및 치료에 적용하고자 하는 전략이다. 그러나 한 개인의 시퀀스 분석을 위한 적정 커버리지(약 30 커버리지로 예측됨)의 시퀀싱 데이터는 100 GB를 넘는 방대한 크기를 가지므로 다수 개인의 유전 정보의 분석/비교를 위하여 대규모 데이터 처리/분석 환경 및 고성능 컴퓨팅 방식의 도입이 필수적이다. 본 연구에서는 클라우드 컴퓨팅 기술을 기반으로 하는 새로운 병렬 유전체 단위반복변위 (Copy Number Variation, CNV) 영역 추출 알고리즘을 제안한다. 제안된 방식에서는 다수의 컴퓨팅 노드를 활용하는 병렬 처리를 위하여 Hadoop의 MapReduce 오픈 소스를 사용하며, 대규모 유전자 시퀀스를 병렬로 처리하여 (개인_ID, 염색체_ID, 컨티그_ID) 단위로 CNV 영역을 추출, 보고한다.

Recent advances in genome analysis method and sequencing technology may allow for a greater degree of personalized medicine than is currently available. Personalized medicine is a future medical model emphasizing the systematic use of genetic information about an individual patient to apply and optimize patient's preventative and therapeutic care. However, as an enormous amount of data (around 100 GB or up to 30x read coverage data for an individual) is needed to analyze and compare genetic information among many individuals, it is essential to employ the environment of high-performance computing system. In this study, we propose a novel algorithm to detect CNV (Copy Number Variation) regions by carrying out parallel and transparency processing based on cloud computing in a part of new frontier technologies. This method is able to perform simultaneous tasking with large numbers of computing nodes using open source of 'MapReduce' in 'Hadoop' project. The CNV regions detected by the proposed method are reported as a unit of map consists of <individual_id>, <chromosome_id>, and <contig_id> by carrying out parallel processing for tremendous size of short read data from next generation sequencing instrument.

최근 클라우드 컴퓨팅이 발전함에 따라 데이터베이스 아웃소싱에 대한 관심이 증가하고 있다. 그러나 데이터베이스 를 아웃소싱하는 경우 데이터 소유자의 민감한 데이터가 노출될 수 있다는 문제점이 존재한다. 따라서 본 논문에서 는 프라이버시 보호를 지원하는 병렬 kNN 분류 알고리즘을 제안한다. 제안하는 알고리즘은 기존 기법과 동일한 수 준의 정보 보호 수준을 제공하면서 다중 CPU 코어를 활용한 병렬 처리를 지원한다. 이를 위해 제안하는 알고리즘 은 단일 CPU 코어 기반 데이터 처리 프로토콜을 다중 CPU 코어 기반 데이터 처리 프로토콜로 변환함으로써 효율 적인 질의처리를 수행한다. 또한 노이즈(Noise) 데이터를 전처리함으로써 정보 보호를 지원하는 동시에 효율적인 질의 처리가 가능하다. 마지막으로 성능 평가를 통해 제안하는 알고리즘이 기존 기법보다 질의처리 시간 측면에서 8~30배 성능이 우수함을 보인다.

With the recent development of cloud computing, interest in database outsourcing is increasing. However, when outsourcing a database, there is a problem that sensitive data of the data owner may be exposed. Therefore, in this paper, we propose a parallel kNN classification algorithm that supports information protection. The proposed algorithm supports parallel processing using multiple CPU cores while providing the same level of information protection as the existing techniques. For this, the proposed algorithm performs efficient query processing by converting a data processing protocol based on a single CPU core into a data processing protocol based on multiple CPU cores. By preprocessing noise data, it also supports information protection and enables efficient query processing. Finally, it is shown from performance evaluation that the proposed algorithm is 8 to 30 times better than the existing techniques, in terms of query processing time.

In this paper, all phase biorthogonal transform (APBT) based on parallel algorithm is proposed. It can solve two problems. First, block-based DCT transform coding has serious blocking artifacts when the image is highly compressed at low bit rate. Second, APBT can solve the problem about blocking artifacts, but it does not have a fast algorithm, it has a low efficiency when APBT applies to image processing. So APBT based on parallel algorithm can solve the above problems, and it provides more space for improving the processing speed of APBT. We use the CUDA toolkit based on GPU which is released by NVIDIA to design the parallel algorithm of APBT. Experimental results show that the maximum speedup ratio of parallel algorithm of APBT can reach more than 40 times with a very low version GPU, compared with conventional serial APBT. And the reconstructed image using the proposed algorithm has the same performance with the serial one in terms of objective quality and subjective effect. The proposed parallel algorithm based on GPU of APBT also can be used in image compression, video compression, the edge detection, and some other fields of image processing.

Cloud computing integrates vast computing and/or storage resources together, which provides services on demand via network. The cloud computing data center is usually composed of thousand of commercial computers, and these computers are connected by network. A cloud computing programming model is urgent to design to help user to use cloud resources without concerning the details of implementation. Nowadays, some cloud computing programming models have been proposed. This paper first analyzes the problems which cloud computing programming model need to solve, and then analyzes the characteristics of the cloud computing programming model. The advantages and disadvantages of each programming model are proposed. Finally, this is paper concludes the current issues and future trends.

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.

In the big data era, with the parallel evolution of computer architecture, computing changes and modifications of industrial application mode resource expansion capability, we need to explore a new parallel computing model, to reflect the properties and large data applications form the current parallel machines, and a variety of mainstream big data processing system for unified theoretical analysis to guide large data applications tuning. Currently, despite the large data programming model study made many achievements, and is widely used in the TB level or even PB-class data processing and analysis, but the corresponding computational model study has just begun. From traditional parallel computing model, research big data programming model and large data computation model, summed up the three basic problems of large data model, in theory, need to be addressed: the three elements of the problem model, scalability and fault tolerance issues and performance optimization. Around these three questions, on the one hand and performance optimization model to calculate the theoretical study of data from a large, on the other hand these performance optimization methods in case of an actual big data.

The demands of data analysis and processing make the parallel computing platforms are continuously developed. But the existing parallel computing models which are the core of platforms present the characteristics of diversification, high pertinence and short cycle. So a synthetic model of supporting flexible platforms urgently needs to be researched. This work researches a synthetic model to shield the heterogeneity of parallel computing models under the theories of λ-calculus, functional language and graph reduction. Based on the MapReduce and BSP models, first of all, the performing principles of models are analyzed. And then the unified modalities of models are found. Finally, the synthetic model having high performance is developed with graph reduction rules, and the experiment results are also shown.

This research contributes to the on-going research effort by exploring alternate methods for soft computing optimization the highly nonlinear and uncertain systems. This research addresses two basic issues related to the control of an uncertain system; (1) design of a robust feedback controller, and (2) the design of a parallel artificial intelligence based optimization to increase the result qualification. The robust backstepping controller proposed in this research is used to further demonstrate the appealing features exhibited by the continuum robot. Robust feedback controller is used to position control of continuum robot in presence of uncertainties. Using Lyapunov type stability arguments, a robust backstepping controller is designed to achieve this objective. The controller developed in this research is designed in two steps. Firstly, a robust stabilizing torque is designed for the nominal continuum robot dynamics derived using the constrained Lagrangian formulation. Next, the fuzzy logic methodology applied to it to solution uncertainty problem by parallel optimization. The fuzzy model free optimization is formulated to minimize the problem of nonlinear formulation of uncertain systems.

With the rapid development of the 3G network, traditional calculation methods are unable to adapt to the data scene that telecom users' Network access behavior's data scale increase rapidly dozens of TB. The cloud techniques such as Hadoop platform are introduced to solve the data storage problem. The appropriate data mining algorithms are designed from the perspective of practical application. This paper improves the traditional decision tree SPRINT algorithms, proposes a parallel computing program and successfully applies to the Hadoop platform.

In social media services and social network services, it is necessary to collect, analyze and process their big data with low maintenance cost. Therefore, distributed-parallel data processing on cloud platform is getting spotlight as useful solution for them. In this paper, we present a new architecture of DIVE-C: DIstributed-parallel Virtual Environment on Cloud computing platform for distributed parallel data processing applications which offers a transparent virtual computing environment in order to provide a way easy to launch user’s distributed parallel applications. It hides the complexity of the cloud, and helps users to focus on their new applications and core services. DIVE-C uses agent-based resource management scheme to configure VM resources and application deployment for offering various distributed-parallel application models. VM resources are automatically provided by unified cloud management layer. Furthermore, an easy-to-use web interface of DIVE-C offers convenience to users. We implemented a prototype of DIVE-C, and its experiment results show the competitive performance of DIVE-C for dynamic resource and virtual computing environment provisioning for various data processing models.

E-commerce is a cyber, electronic and informational business activities. Electronic commerce has been developing rapidly in recent years in China. Cloud computing provides a new idea for the development of electronic commerce. In practice, cloud computing also has a very good combination of e-commerce. To predict the profit of electronic commerce can find the existing problems, grasp the development trend, and make better management of electronic commerce. In this paper, we study the profit prediction of electronic commerce. Then, we propose an improved parallel PSO-LSSVM algorithm, and use this algorithm to predict the benefits of electronic commerce. Experimental results show that the proposed algorithm is effective and reliable.

The paper Proposed Item parallel collaborative filtering recommendation algorithm (IP-CF). Through designing efficient parallel algorithm, compute-extensive procedures are distributed to different processing nodes in Hadoop platform. Taking advantage of parallel computing, we accelerate the response of recommendation. The experimental results show that our proposed algorithm IP-CF is more efficient and scalable than current parallel algorithms.

다양한 병렬 컴퓨팅 시스템을 지원하기 위해서는 LLVM IR을 벡터/행렬을 보다 효과적으로 지원할 수 있도록 확장하는 것과 LLVM IR을 machine code로 바꾸어 주는 부분을 새로운 알고리즘으로 설계하여 구현하면 된다. IR 예제에서 보았듯이 기본적으로 RISC 명령어로 구성되어 있기 때문에 RISC 명령어 생성은 자연스럽게 생성되며, 벡 터 또한 현재 지원가능한데 행렬 명령어는 지원되지 못하고 있다. 벡터/행렬을 보다 강력하게 지원하기 위한 새로운 IR 구조, 명령어 생성 알고리즘 및 관련 부분의 확장이 필요하다. 이를 위해 LLVM IR의 각 명령어를 (벡터/행렬을 위한) target architecture의 적당한 명령어로 mapping을 해주는 부분 (instruction selection 알고리즘)이 중요하다. LLVM IR 명령어의 의미를 파악하고, target architecture의 각 명령어 의미와 syntax를 비교하여, 패턴이 일치하는 명령어를 선택하여 mapping을 효율적으로 해줘야 한다.

In order to support various parallel computing systems, it is necessary to extend LLVM IR to more efficiently support vector / matrix and to design LLVM IR to machine code as a new algorithm. As shown in the IR example, RISC instruction generation is naturally generated because the RISC instruction is basically composed of the RISC instruction, and the vector instruction is also not supported. There is a need for new IR structures, command generation algorithms and related extensions to support vector / matrix more robustly. To do this, it is important to map each instruction in the LLVM IR to the appropriate instruction in the target architecture (vector / matrix) (instruction selection algorithm). It is necessary to understand the meaning of LLVM IR command, to compare the meaning of each instruction of the target architecture with syntax, and to select the instruction that matches the pattern to make mapping efficient.

본 논문에서는 Edge computing 환경에서 다수의 노드들로 구성된 네트워크의 디바이스를 효율적으로 관리하기 위한 방법을 제안한다. 기존의 클라이언트-서버 모델은 모든 데이터와 그에 대한 요청을 중심 서버에서 처리하기 때문에, 다수의 노드로부터 생성된 많은 양의 데이터를 처리하는 데 빠른 응답속도를 보장하지 못한다. Edge computing은 분담을 통해 네트워크의 부담을 줄일 수 있는 IoT 네트워크에 적합한 방법으로, 데이터를 전송하고 받는 과정에서 네트워크의 대역폭을 사용하는 대신 서로 연결된 노드들이 협력해서 데이터를 처리하고, 또한 네트워크 말단에서의 데이터 처리가 허용되어 데이터 센터의 부담을 줄일 수 있다. 여러병렬 기계학습 모델 중 본 연구에서는 Stale Synchronous Parallel(SSP) 모델을 이용하여 Edge 노드에서 분산기계 학습에 적용하였다.

Synthetic Aperture Radar (SAR) observations are powerful tools to monitor surface's displacement very accurately, induced by earthquake, volcano, ground subsidence, glacier movement, etc. Especially, radar interferometry (InSAR) which utilizes phase information related to distance from sensor to target, can generate displacement map in line-of-sight direction with accuracy of a few cm or mm. Due to decorrelation effect, however, degradation of coherence in the InSAR application often prohibit from construction of differential interferogram. Offset tracking method is an alternative approach to make a two-dimensional displacement map using intensity information instead of the phase. However, there is limitation in that the offset tracking requires very intensive computation power and time. In this paper, efficiency of parallel computing has been investigated using high performance computer for estimation of glacier velocity. Two TanDEM-X SAR observations which were acquired on September 15, 2013 and September 26, 2013 over the Narsap Sermia in Southwestern Greenland were collected. Atotal of 56 of 2.4 GHz Intel Xeon processors(28 physical processors with hyperthreading) by operating with linux environment were utilized. The Gamma software was used for application of offset tracking by adjustment of the number of processors for the OpenMP parallel computing. The processing times of the offset tracking at the 256 by 256 pixels of window patch size at single and 56 cores are; 26,344 sec and 2,055 sec, respectively. It is impressive that the processing time could be reduced significantly about thirteen times (12.81) at the 56 cores usage. However, the parallel computing using all the processors prevent other background operations or functions. Except the offset tracking processing, optimum number of processors need to be evaluated for computing efficiency.

Computational Geometry is concerned with the design and analysis of computational algorithms which solve geometry problems. Geometry problems have a large number of applications areas such as pattern recognition, image processing, computer graphics, VLSI design and statistics since they involve inherently geometric problems for which efficient algorithms have to be developed. Several parallel algorithms, based on various parallel computation models, have been proposed for solving geometric problems. We review the current status of the parallel algorithms in computational geometry.