• International Journal of Contents
• /
• v.1 no.1
• /
• pp.50-58
• /
• 2005

Generally, multidimensional data such as image and spatial data require large amount of storage space. There is a limit to store and manage those large amounts of data in single workstation. If we manage the data on parallel computing environment which is being actively researched these days, we can get highly improved performance. In this paper, we propose a parallel multidimensional index structure that exploits the parallelism of the parallel computing environment. The proposed index structure is nP(processor)-nxmD(disk) architecture which is the hybrid type of nP-nD and 1P-nD. Its node structure in-creases fan-out and reduces the height of an index. Also, a range search algorithm that maximizes I/O parallelism is devised, and it is applied to k-nearest neighbor queries. Through various experiments, it is shown that the proposed method outperforms other parallel index structures.

• Journal of the Korean Society for Railway
• /
• v.8 no.1
• /
• pp.1-5
• /
• 2005

This paper has presented the parallel cyclic redundancy check (CRC) technique that performs CRC computation in parallel superior to the conventional CRC technique that processes data bits serially. Also, it has showed that the implemented parallel CRC circuit has been successfully applied to the inductively coupled passive RFTD system working at a frequency of 13.56㎒ in order to process the detection of logical faults more fast and the system has been verified experimentally. In comparison with previous works, the proposed RFID system using the parallel CRC technique has been shown to reduce the latency and increase the data processing rates about 15％ In the results. Therefore, it seems reasonable to conclude that the parallel CRC realization in the RFID system offers a means of maintaining the integrity of data in the high speed RFID system.

• ETRI Journal
• /
• v.44 no.2
• /
• pp.313-326
• /
• 2022

This study presents a noble scheme for distributed and parallel simulations with optimized agent placement for simulation instances. The traditional parallel simulation has some limitations in that it does not provide sufficient performance even though using multiple resources. The main reason for this discrepancy is that supporting parallelism inevitably requires additional costs in addition to the base simulation cost. We present a comprehensive study of parallel simulation architectures, execution flows, and characteristics. Then, we identify critical challenges for optimizing large simulations for parallel instances. Based on our cost-benefit analysis, we propose a novel approach to overcome the performance constraints of agent-based parallel simulations. We also propose a solution for eliminating the synchronizing cost among local instances. Our method ensures balanced performance through optimal deployment of agents to local instances and an adaptive agent placement scheme according to the simulation load. Additionally, our empirical evaluation reveals that the proposed model achieves better performance than conventional methods under several conditions.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2004.11a
• /
• pp.444-447
• /
• 2004

A heart diagnosis system adopts hundreds of Superconducting Quantum Interface Device(SQUID) sensors for precision MCG(Magnetocardiogram) or MEG(Magnetoencephalogram) signal acquisitions. This system requires correct and real-time data acquisition from the sensors in a required sampling interval, i.e., 1 mili-second. This paper presents our hardware design and test results, to acquire data from 256 channel analog signal with 1-ksample/sec speed, using 12-bit 8-channel ADC devices, SPI interfaces, parallel interfaces, and 8-bit microprocessors. We chose to implement parallel data transfer between microprocessors as an effective way of achieving such data collection. Our result concludes that the data collection can be done in 250 ${\mu}sec$ time-interval.

• Communications for Statistical Applications and Methods
• /
• v.15 no.4
• /
• pp.587-595
• /
• 2008

Parallel coordinate plot of Inselberg (1985) is useful for visualizing dozens of variables, but so far the plot's applicability is limited to the variables of numerical type. The aim of this study is to extend the parallel coordinate plot so that it can accommodate both numerical and categorical variables. We combine Hayashi's (1950, 1952) quantification method of categorical variables and Hurley's (2004) endlink algorithm of ordering variables for the parallel coordinate plot. In line with our former study (Kwak and Huh, 2008), we develop Andrews' type modification of conventional straight-lines parallel coordinate plot to visualize the mixed-type data.

• Proceedings of the KSR Conference
• /
• 2004.10a
• /
• pp.1235-1240
• /
• 2004

This paper has presented the parallel cyclic redundancy check (CRC) technique that performs CRC computation in parallel superior to the conventional CRC technique that processes data bits serially. Also, it has showed that the implemented parallel CRC circuit had been successfully applied to the inductively coupled passive RFID system working at a frequency of 13.56MHz in order to process the detection of logical faults more fast and the system had been verified experimentally. In comparison with previous works, the proposed RFID system using the parallel CRC technique has been shown to reduce the latency and increase the data processing rates in the results. Therefore, it seems reasonable to conclude that the parallel CRC realization in the RFID system offers a means of maintaining the integrity of data in the high speed RFID system.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.1 no.1
• /
• pp.119-124
• /
• 2001

In this pattern recognition on the large volumes of remote sensing satellite images, the inference time is much increased. In the case of the remote sensing data [5] having 4 wavebands, the 778 training patterns are learned. Each land cover pattern is classified by using 159, 900 patterns including the trained patterns. For the fuzzy classification, the 778 fuzzy rules are generated. Each fuzzy rule has 4 fuzzy variables in the condition part. Therefore, high performance parallel fuzzy inference system is needed. In this paper, we propose a novel parallel fuzzy inference system on T3E parallel computer. In this, fuzzy rules are distributed and executed simultaneously. The ONE_To_ALL algorithm is used to broadcast the fuzzy input to the all nodes. The results of the MIN/MAX operations are transferred to the output processor by the ALL_TO_ONE algorithm. By parallel processing of the fuzzy rules, the parallel fuzzy inference algorithm extracts match parallelism and achieves a good speed factor. This system can be used in a large expert system that ha many inference variables in the condition and the consequent part.

• Journal of the Korea Society for Simulation
• /
• v.11 no.2
• /
• pp.67-75
• /
• 2002

This paper proposes a parallel simulation algorithm for bounded Petri nets in a single processor, which exploits the SIMD(Single Instruction Multiple Data)-type parallelism. The proposed algorithm is based on a data packing scheme which packs multiple bytes data in a single register, thereby being manipulated simultaneously. The parallelism can reduce simulation time of bounded Petri nets in a single processor environment. The effectiveness of the algorithm is demonstrated by presenting speed-up of simulation time for two bounded Petri nets.

• Spatial Information Research
• /
• v.17 no.3
• /
• pp.371-379
• /
• 2009

Recently, GPU (Graphics Processing Unit) has been improved rapidly on the need of speed for gaming. As a result, GPU contains multiple ALU (Arithmetic Logic Unit) for parallel processing of a lot of graphics data, such as transform, ray tracing, etc. Therefore, this paper proposed a technique for parallel processing of spatial data using GPU. Spatial data consists of multiple coordinates, and each coordinate contains value of x and y axis. To display spatial data graphics operations have to be processed to large amount of coordinates. Because the graphics operation is identical and coordinates are multiple data, SIMD (Single Instruction Multiple Data) parallel processing of GPU can be used for processing of spatial data to improve performance. This paper implemented SIMD parallel processing of spatial data using two kinds of SDK (Software Development Kit). CUDA and ATI Stream are used for NVIDIA and ATI GPU respectively. Experiments that measure time of calculation for graphics operations are carried out to observe enhancement of performance. Experimental result is reported that proposed method can enhance performance up to 1,162% for graphics operations. The proposed method that uses parallel processing with GPU for spatial data can be generally used to enhance performance for applications which deal with large amount of spatial data.

• International journal of advanced smart convergence
• /
• v.8 no.4
• /
• pp.194-199
• /
• 2019

As the performance of the system increases, more parallelized data is being processed than single processing of data. Today's cpu structure has been developed to leverage multicore, and hence data processing methods are being developed to enable parallel processing. In recent years desktop cpu has increased multicore, data is growing exponentially, and there is also a growing need for data processing as artificial intelligence develops. This neural network of artificial intelligence consists of a matrix, making it advantageous for parallel processing. This paper aims to speed up the processing of the system by using raspberrypi to implement the cluster building and parallel processing system against the backdrop of the foregoing discussion. Raspberrypi is a credit card-sized single computer made by the raspberrypi Foundation in England, developed for education in schools and developing countries. It is cheap and easy to get the information you need because many people use it. Distributed processing systems should be supported by programs that connected multiple computers in parallel and operate on a built-in system. RaspberryPi is connected to switchhub, each connected raspberrypi communicates using the internal network, and internally implements parallel processing using the Message Passing Interface (MPI). Parallel processing programs can be programmed in python and can also use C or Fortran. The system was tested for parallel processing as a result of multiplying the two-dimensional arrangement of 10000 size by 0.1. Tests have shown a reduction in computational time and that parallelism can be reduced to the maximum number of cores in the system. The systems in this paper are manufactured on a Linux-based single computer and are thought to require testing on systems in different environments.