CASM: a VLSI chip for approximate string matching

Sastry, R.; Ranganathan, N.; Remedios, K.

doi:10.1109/34.400575

Cited by 28 publications

(11 citation statements)

References 13 publications

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“…This architecture has also been used for approximate string matching [19,22,37] by means of a parallelizing a dynamic programming algorithm but it exhibits very limited flexibility due to the encoding scheme used. A similar approach is pursued in [56,57], using again a dynamic programming approach, but an improved encoding scheme and limited communication and control overheads. However, the bi-directional data flow imposes low processor utilization (approximately 50%) and increased computation time (approximately 2n steps instead of n steps, which is the case in the design proposed herein).…”

Section: Implementation and Performance Considerationsmentioning

confidence: 96%

“…These algorithms require each cell to perform simple bit-level arithmetic and logical operations as opposed to the comparison operations used in other architectures [37,57,56,42,26,15,27,19,7,63,14,53,52]. The advantage of the bit-level arithmetic and logical operations is that they are executed fast enough compared to the comparison operations.…”

Section: Implementation and Performance Considerationsmentioning

confidence: 97%

“…Some surveys on arrays processors and architectures for approximate string matching and related problems can be found in [6,20,21,54]. A number of arrays processors have been proposed by several researchers for approximate string matching and LCS [11,6,36,37,38,22,57,56,42,26,15,27,18,62,40] and [55,68,30,28,29,31,32,39,33,35,34], respectively. Recently, we proposed the array processors architectures which perform approximate string matching for flexible patterns that contain don't care, complement and classes symbols [46,43,45,47,48].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A programmable array processor architecture for flexible approximate string matching algorithms

Michailidis

Margaritis

2007

Journal of Parallel and Distributed Computing

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Section: Implementation and Performance Considerationsmentioning

confidence: 96%

Section: Implementation and Performance Considerationsmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A programmable array processor architecture for flexible approximate string matching algorithms

Michailidis

Margaritis

2007

Journal of Parallel and Distributed Computing

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“…There are two basic methods to improve the performance of approximate string matching on large text collections: one is based on the finegrain parallelization of the approximate string matching algorithm [2,12,13,6,4,5] and the other is based on the distribution of the computation of character comparisons on supercomputers or network of workstations. As far as the second method, is concerned distributed implementations of approximate string matching algorithm are not available in the literature.…”

Section: Introductionmentioning

confidence: 99%

A Performance Study of Load Balancing Strategies for Approximate String Matching on an MPI Heterogeneous System Environment

Michailidis

Margaritis

2002

Recent Advances in Parallel Virtual Machine and Message Passing Interface

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Abstract. In this paper, we present three parallel approximate string matching methods on a parallel architecture with heterogeneous workstations to gain supercomputer power at low cost. The first method is the static master-worker with uniform distribution strategy, the second one is the dynamic master-worker with allocation of subtexts and the third one is the dynamic master-worker with allocation of text pointers. Further, we propose a hybrid parallel method that combines the advantages of static and dynamic parallel methods in order to reduce the load imbalance and communication overhead. This hybrid method is based on the following optimal distribution strategy: the text collection is distributed proportional to workstation's speed. We evaluated the performance of four methods with clusters 1, 2, 4, 6 and 8 heterogeneous workstations. The experimental results demonstrate that the dynamic allocation of text pointers and hybrid methods achieve better performance than the two original ones.

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“…For the algorithms in Table II, the existing string-matching hardware are mainly based on dynamic programming, simple matching, and automaton algorithms, while backward filtering and bit-parallel algorithms are seldom implemented as the matching hardware. The work from Blüthgen et al [2000] and Sastry et al [1995] implemented the dynamic programming algorithm with the systolic array, which is only appropriate for short patterns and text, since the circuit size is proportional to the lengths of patterns and the text. Since the naïve algorithm (simple matching) is easily implemented in hardware design, many previous works applied the parallel circuits, content addressable memory (CAM), and hashing function techniques to accelerate the naive algorithm.…”

Section: String-matching Hardwarementioning

confidence: 99%

A fast scalable automaton-matching accelerator for embedded content processors

Tseng

Lai

Lin

et al. 2009

ACM Trans. Embed. Comput. Syst.

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Home and office network gateways often employ a cost-effective embedded network processor to handle their network services. Such network gateways have received strong demand for applications dealing with intrusion detection, keyword blocking, antivirus and antispam. Accordingly, we were motivated to propose an appropriate fast scalable automaton-matching (FSAM) hardware to accelerate the embedded network processors. Although automaton matching algorithms are robust with deterministic matching time, there is still plenty of room for improving their average-case performance. FSAM employs novel prehash and root-index techniques to accelerate the matching for the nonroot states and the root state, respectively, in automation based hardware. The prehash approach uses some hashing functions to pretest the input substring for the nonroot states while the root-index approach handles multiple bytes in one single matching for the root state. Also, FSAM is applied in a prevalent automaton algorithm, AhoCorasick (AC), which is often used in many content-filtering applications. When implemented in FPGA, FSAM can perform at the rate of 11.1Gbps with the pattern set of 32,634 bytes, demonstrating that our proposed approach can use a small logic circuit to achieve a competitive performance, although a larger memory is used. Furthermore, the amount of patterns in FSAM is not limited by the amount of internal circuits and memories. If the high-speed external memories are employed, FSAM can support up to 21,302 patterns while maintaining similar high performance.

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CASM: a VLSI chip for approximate string matching

Cited by 28 publications

References 13 publications

A programmable array processor architecture for flexible approximate string matching algorithms

A programmable array processor architecture for flexible approximate string matching algorithms

A Performance Study of Load Balancing Strategies for Approximate String Matching on an MPI Heterogeneous System Environment

A fast scalable automaton-matching accelerator for embedded content processors

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