In this paper, we propose several optimizations for the SFA construction algorithm, which greatly reduce the in-memory footprint and the processing steps required to construct an SFA. We introduce fingerprints as a space- and time-efficient way to represent SFA states. To compute fingerprints, we apply the Barrett reduction algorithm and accelerate it using recent additions to the x86 instruction set architecture. We exploit fingerprints to introduce hashing for further optimizations. Our parallel SFA construction algorithm is nonblocking and utilizes instruction-level, data-level, and task-level parallelism of coarse-, medium- and fine-grained granularity. We adapt static workload distributions and align the SFA data-structures with the constraints of multicore memory hierarchies, to increase the locality of memory accesses and facilitate HW prefetching. We conduct experiments on the PROSITE protein database for FAs of up to 702 FA states to evaluate performance and effectiveness of our proposed optimizations. Evaluations have been conducted on a 4 CPU (64 cores) AMD Opteron 6378 system and a 2 CPU (28 cores, 2 hyperthreads per core) Intel Xeon E5-2697 v3 system. The observed speedups over the sequential baseline algorithm are up to 118541x on the AMD system and 2113968x on the Intel system.
Locating a string in a larger text has applications with text editing, compiler front-ends and web browsers, internet search engines, computer security, and DNA sequence analysis. Early string searching algorithms such as Aho--Corasick @cite_10 , Boyer--Moore @cite_1 and Rabin--Karp @cite_13 efficiently match a finite set of input strings against an input text.
@cite_1 @cite_10 @cite_13
2107670443 1979787006 1972418517 2030393641
Multiple string matching is known as locating all the occurrences of a given number of patterns in an arbitrary string. It is used in bio-computing applications where the algorithms are commonly used for retrieval of information such as sequence analysis and gene protein identification. Extremely large amount of data in the form of strings has to be processed in such bio-computing applications. Therefore, improving the performance of multiple string matching algorithms is always desirable. Multicore architectures are capable of providing better performance by parallelizing the multiple string matching algorithms. The Aho-Corasick algorithm is the one that is commonly used in exact multiple string matching algorithms. The focus of this paper is the acceleration of Aho-Corasick algorithm through a multicore CPU based software implementation. Through our implementation and evaluation of results, we prove that our method performs better compared to the state of the art. Approximate string matching problem is a common and often repeated task in information retrieval and bioinformatics. This paper proposes a generic design of a programmable array processor architecture for a wide variety of approximate string matching algorithms to gain high performance at low cost. Further, we describe the architecture of the array and the architecture of the cell in detail in order to efficiently implement for both the preprocessing and searching phases of most string matching algorithms. Further, the architecture performs approximate string matching for complex patterns that contain don't care, complement and classes symbols. We also simulate and evaluate the proposed architecture on a field programmable gate array (FPGA) device using the JHDL tool for synthesis and the Xilinx Foundation tools for mapping, placement, and routing. Finally, our programmable implementation achieves about 8-340 times faster execution than a desktop computer with a Pentium 4 3.5GHz for all algorithms when the length of the pattern is 1024. We present randomized algorithms to solve the following string-matching problem and some of its generalizations: Given a string X of length n (the pattern) and a string Y (the text), find the first occurrence of X as a consecutive block within Y. The algorithms represent strings of length n by much shorter strings called fingerprints, and achieve their efficiency by manipulating fingerprints instead of longer strings. The algorithms require a constant number of storage locations, and essentially run in real time. They are conceptually simple and easy to implement. The method readily generalizes to higher-dimensional patternmatching problems. Background Given n strings s1, …, sn each of length l and a nonnegative integer d, the CLOSEST STRING problem asks to find a center string s such that none of the input strings has Hamming distance greater than d from s. Finding a common pattern in many – but not necessarily all – input strings is an important task that plays a role in many applications in bioinformatics.