Aho-Corasick String Matching on Shared and Distributed-Memory Parallel Architectures

Multiple matching algorithms are used to locate the occurrences of patterns from a finite pattern set in a large input string. Aho-Corasick and Wu-Manber, two of the most well known algorithms for multiple matching require an increased computing power, particularly in cases where large-size datasets must be processed, as is common in computational biology applications. Over the past years, Graphics Processing Units (GPUs) have evolved to powerful parallel processors outperforming Central Processing Units (CPUs) in scientific calculations. Moreover, multiple GPUs can be used in parallel, forming hybrid computer cluster configurations to achieve an even higher processing throughput. This paper evaluates the speedup of the parallel implementation of the Aho-Corasick and Wu-Manber algorithms on a hybrid GPU cluster, when used to process a snapshot of the Expressed Sequence Tags of the human genome and for different problem parameters.

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“…21 for a heterogeneous computer cluster with 5 NVIDIA Tesla S1070 boxes, each box being the equivalent of 4 C1060 GPUs, for a total of 20 GPUs.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Parallel Implementation of the Aho–Corasick and Wu–Manber Algorithms Using NVIDIA CUDA and MPI Evaluated on a Biological Sequence Database

Kouzinopoulos

Assael

Pyrgiotis

et al. 2015

Int. J. Artif. Intell. Tools

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“…Antonino Tumeo et al, [61] focus on the matching of unknown inputs streamed from a single source, typical of security applications and difficult to manage since the input cannot be preprocessed to obtain locality. They consider shared-memory architectures (Niagara 2, x86 multiprocessors and Cray XMT) and distributed memory architectures with homogeneous (InfiniBand cluster of x86 multicores) or heterogeneous processing elements (InfiniBand cluster of x86 multicores with NVIDIA Tesla C1060 GPUs).…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

“…Antonino Tumeo et.al [62] presented several software implementations of the Aho-Corasick pattern matching algorithm for high performance systems, and carefully analyzed their performance. It considered the various tradeoffs in terms of peak performance, performance variability, and data set size.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

Parallel String Matching Problems with Computing Models An Analysis of the Most Recent Studies

Raju¹,

Rao²,

Raju³

2013

IJCA

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We survey the current techniques to handle with the problem of parallel string matching with computing models. This is becoming a more and more relevant issue for many fast growing areas such as information retrieval and computational biology. We focus on current developments of parallel string matching, computing models, and the central ideas of the algorithms and their complexities. We present the performance of the different algorithms and their effectiveness. Finally this analysis helps the researchers to develop the better technique.

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“…In [12], a comparison on several software-based implementations of the Aho-Corasick algorithm for high performance systems has been presented. A detailed comparison has been presented on how each solution achieves the objectives of supporting large dictionaries, sustaining high performance, and enabling customization and flexibility using various data sets and considering shared-memory and distributed memory architectures.…”

Section: Related Workmentioning

confidence: 99%

Accelerating string matching for bio-computing applications on multi-core CPUs

Herath

Lakmali

Ragel

2012

2012 IEEE 7th International Conference on Industrial and Information Systems (ICIIS)

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Huge amount of data in the form of strings are being handled in bio-computing applications and searching algorithms are quite frequently used in them. Many methods utilizing on both software and hardware are being proposed to accelerate processing of such data. The typical hardware-based acceleration techniques either require special hardware such as generalpurpose graphics processing units (GPGPUs) or need building a new hardware such as an FPGA based design. On the other hard, software-based acceleration techniques are easier since they only require some changes in the software code or the software architecture. Typical software-based techniques make use of computers connected over a network, also known as a network grid to accelerate the processing. In this paper, we test the hypothesis that multi-core architectures should provide better performance in this kind of computation, but still it would depend on the algorithm selected as well as the programming model being utilized. We present the acceleration of a stringsearching algorithm on a multi-core CPU via a POSIX thread based implementation. Our implementation on an 8-core processor (that supports 16-threads) resulted in 9x throughput improvement compared to a single thread implementation.

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Aho-Corasick String Matching on Shared and Distributed-Memory Parallel Architectures

Cited by 34 publications

References 9 publications

A Hybrid Parallel Implementation of the Aho–Corasick and Wu–Manber Algorithms Using NVIDIA CUDA and MPI Evaluated on a Biological Sequence Database

A Hybrid Parallel Implementation of the Aho–Corasick and Wu–Manber Algorithms Using NVIDIA CUDA and MPI Evaluated on a Biological Sequence Database

Parallel String Matching Problems with Computing Models An Analysis of the Most Recent Studies

Accelerating string matching for bio-computing applications on multi-core CPUs

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