• Journal of KIISE:Computer Systems and Theory
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• v.30 no.10
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• pp.586-594
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• 2003

X-Match algorithm is a lossless compression algorithm suitable for hardware implementation owing to its simplicity. It can compress 32 bits per clock cycle and is suitable for real time compression. However, as the bus width increases 64-bit, the compression unit also need to increase. This paper proposes the cooperative parallel X-Match (X-MatchCP) algorithm, which improves the compression speed by performing the two X-Match algorithms in parallel. It searches the all dictionary for two words, combines the compression codes of two words generated by parallel X-Match compression and outputs the combined code while the previous parallel X-Match algorithm searches an individual dictionary. The compression ratio in X-MatchCP is almost the same as in X-Match. X-MatchCP algorithm is described and simulated by Verilog hardware description language.

• Journal of KIISE:Databases
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• v.32 no.5
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• pp.558-566
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• 2005

The XML is the new standard fir data representation and exchange on the Internet. In this paper, we describe a new approach for evaluating a path-oriented query against XML document. In our approach, we propose the Parallel Match Indexing Fabric to speed up evaluation of path-oriented query using path signature and design the parallel match algorithm to perform a match process between a path signature of input query and path signatures of elements stored in the database. To construct a structure of the parallel match indexing, we first make the binary tie for all path signatures on an XML document and then which trie is transformed to the Parallel Match Indexing Fabric. Also we use the Parallel Match Indexing Fabric and a parallel match algorithm for executing a search operation of a path-oriented query. In our proposed approach, Time complexity of the algorithm is proportional to the logarithm of the number of path signatures in the XML document.

• 한국전산유체공학회:학술대회논문집
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• 2006.05a
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• pp.103-106
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• 2006

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.119-124
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• 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 Institute of Electronics Engineers of Korea SP
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• v.44 no.4 s.316
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• pp.101-108
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• 2007

Parallel processing algorithms, coupled with advanced networking and distributed computing architectures, improve the overall computational performance, dependability, and versatility of a digital signal processing system In this paper, novel parallel algorithms are introduced and investigated for advanced sonar algorithm, conventional matched-field processing (CMFP). Based on a specific domain, each parallel algorithm decomposes the sequential workload in order to obtain scalable parallel speedup. Depending on the processing requirement of the algorithm, the computational performance of the parallel algorithm reveals different characteristics. The high-complexity algorithm, CMFP shows scalable parallel performance on the array of DSP processors. The impact on parallel performance due to workload balancing, communication scheme, algorithm complexity, processor speed, network performance, and testbed configuration is explored.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.1
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• pp.27-38
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• 2017

Packet classification is one of the essential functionalities of Internet routers in providing quality of service. Since the arrival rate of input packets can be tens-of-millions per second, wire-speed packet classification has become one of the most challenging tasks. While traditional packet classification only reports a single matching result, new network applications require multiple matching results. Ternary content-addressable memory (TCAM) has been adopted to solve the multi-match classification problem due to its ability to perform fast parallel matching. However, TCAM has a fundamental issue: high power dissipation. Since TCAM is designed for a single match, the applicability of TCAM to multi-match classification is limited. In this paper, we propose a cost- and energy-efficient multi-match classification architecture that combines TCAM with a tuple space search algorithm. The proposed solution uses two small TCAM modules and requires a single-cycle TCAM lookup, two SRAM accesses, and several Bloom filter query cycles for multi-match classifications.

• ETRI Journal
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• v.43 no.3
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• pp.483-510
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• 2021

In computational biology, desired patterns are searched in large text databases, and an exact match is preferable. Classical benchmark algorithms obtain competent solutions for pattern matching in O (N) time, whereas quantum algorithm design is based on Grover's method, which completes the search in $O(\sqrt{N})$ time. This paper briefly explains existing quantum algorithms and defines their processing limitations. Our initial work overcomes existing algorithmic constraints by proposing the quantum-based combined exact (QBCE) algorithm for the pattern-matching problem to process exact patterns. Next, quantum random access memory (QRAM) processing is discussed, and based on it, we propose the QRAM processing-based exact (QPBE) pattern-matching algorithm. We show that to find all t occurrences of a pattern, the best case time complexities of the QBCE and QPBE algorithms are $O(\sqrt{t})$ and $O(\sqrt{N})$, and the exceptional worst case is bounded by O (t) and O (N). Thus, the proposed quantum algorithms achieve computational speedup. Our work is proved mathematically and validated with simulation, and complexity analysis demonstrates that our quantum algorithms are better than existing pattern-matching methods.

• Journal of IKEEE
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• v.1 no.1 s.1
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• pp.31-46
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• 1997

In this paper, a parallel Optimal Best-First search Branch-and-Bound(B&B) algorithm(pobs) is designed and evaluated for MIN-based multiprocessor systems. The proposed algorithm decomposes a problem into G subproblems, where each subproblem is processed on a group of P processors. Each processor group uses tile sub-Global Best-First search technique to find a local solution. The local solutions are broadcasted through the network to compute the global solution. This broadcast provides not only the comparison of G local solutions but also the load balancing among the processor groups. A performance analysis is then conducted to estimate the speed-up of the proposed parallel B&B algorithm. The analytical model is developed based on the probabilistic properties of the B&B algorithm. It considers both the computation time and communication overheads to evaluate the realistic performance of the algorithm under the parallel processing environment. In order to validate the proposed evaluation model, the simulation of the parallel B&B algorithm on a MIN-based system is carried out at the same time. The results from both analysis and simulation match closely. It is also shown that the proposed Optimal Best-First search B&B algorithm performs better than other reported schemes with its various advantageous features such as: less subproblem evaluations, prefer load balancing, and limited scope of remote communication.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.3
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• pp.46-58
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• 1998

Multi-disk data allocation problem examined in this paper is to find a method to distribute a Binary Cartesian Product File on multiple disks to maximize parallel disk I/O accesses for partial match retrieval. This problem is known to be NP-hard, and heuristkc approaches have been applied to obtain sub-optimal solutions. Recently, efficient methods have been proposed with a restriction that the number of disks in which files are stored should be power of 2. In this paper, we propose a new disk Allocation method based on Genetic Algorithm(GA) to remove the restriction on the number of disks to be applied. Using the schema theory, we prove that our method can find a near-optimal solutionwith high probability. We compare the quality of solution derived by our method with General Disk Modulo, Binary Disk Modulo, and Error Correcting Code methods through the simulation. The simulation results show that proposed GA is superior to GDM method in all cases and provides comparable performance to the BDM method which has a restriction on the number of disks.

• Journal of Korea Multimedia Society
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• v.5 no.3
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• pp.272-280
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• 2002

In this paper, we present a method to extract a moving object and to measure the distance to it by using the stereo vision system. The moving factor is to be extracted through a match of a pixel unit for the moving object where the adaptive threshold is effectively dealt with to remove changes in the brightness of the image. The distance to moving object is measured by using a stereo vision system which employs a parallel camera. The experimental results show that the proposed algorithm could be effectively applied to distance measurement to moving object because it has an average error of one percent.