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T. Harish

Bio: T. Harish is an academic researcher from VIT University. The author has contributed to research in topics: Pattern matching & String searching algorithm. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

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01 Jan 2019
TL;DR: From the result analysis it is found that the performance is maximum, when the pattern size matches the tile size and it is less than 64, due to the size of the warp considered.
Abstract: Parallelizing pattern matching in multidimensional images is very vital in many applications to improve the performance. With SIMT architectures, the performance can be greatly enhanced if the hardware threads are utilized to the maximum. In the case of pattern matching algorithms, the main bottleneck arises due to the reduction operation that needs to be performed on the multiple parallel search operations. This can be solved by using Shift-Or operations. The recent trend has shown the improvement in pattern matching using Shift-Or operations for bit pattern matching. This has to be extended for multiple dimensional images like hyper-cubes. In this paper, we have extended the Shift-Or pattern matching for multidimensional images. The algorithm is implemented for GPU architectures. The complexity of the proposed algorithm is \( m*\frac{log(n)}{kw} \) where m is the number of dimensions, n is the size of the array if the multidimensional matrix values are placed in a single dimensional array, k is the size of the pattern and w is the size of the tile. From the result analysis it is found that the performance is maximum, when the pattern size matches the tile size and it is less than 64. This restriction is due to the size of the warp considered.

1 citations


Cited by
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01 Jan 1999
TL;DR: In this article, the Shift-And algorithm was used to solve the problem of pattern matching in LZW compressed text, where a pattern length is at most 32 or the word length.
Abstract: This paper considers the Shift-And approach to the problem of pattern matching in LZW compressed text, and gives a new algorithm that solves it. The algorithm is indeed fast when a pattern length is at most 32, or the word length. After an O(m + |Σ|) time and O(|Σ|) space preprocessing of a pattern, it scans an LZW compressed text in O(n + r) time and reports all occurrences of the pattern, where n is the compressed text length, m is the pattern length, and r is the number of the pattern occurrences. Experimental results show that it runs approximately 1.5 times faster than a decompression followed by a simple search using the Shift-And algorithm. Moreover, the algorithm can be extended to the generalized pattern matching, to the pattern matching with k mismatches, and to the multiple pattern matching, like the Shift-And algorithm.

56 citations