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The string matching problem is considered one of the substantial problems in the fields of computer science like speech and pattern recognition, signal and image processing, and artificial intelligence (AI). The increase in the speedup of performance is considered an important factor in meeting the growth rate of databases, Subsequently, one of the determinations to address this issue is the parallelization for exact string matching algorithms. In this study, the E-Abdulrazzaq string matching algorithm is chosen to be executed with the multi-core environment utilizing the OpenMP paradigm which can be utilized to decrease the execution time and increase the speedup of the algorithm. The parallelization algorithm got positive results within the parallel execution time, and excellent speeding-up capabilities, in comparison to the successive result. The Protein database showed optimal results in parallel execution time, and when utilizing short and long pattern lengths. The DNA database showed optimal speedup execution when utilizing short and long pattern lengths, while no specific database obtained the worst results.

The string matching problem is considered one of the substantial problems in the fields of computer science like speech and pattern recognition, signal and image processing, and artificial intelligence (AI). The increase in the speedup of performance is considered an important factor in meeting the growth rate of databases, Subsequently, one of the determinations to address this issue is the parallelization for exact string matching algorithms. In this study, the E-Abdulrazzaq string matching algorithm is chosen to be executed with the multi-core environment utilizing the OpenMP paradigm which can be utilized to decrease the execution time and increase the speedup of the algorithm. The parallelization algorithm got positive results within the parallel execution time, and excellent speeding-up capabilities, in comparison to the successive result. The Protein database showed optimal results in parallel execution time, and when utilizing short and long pattern lengths. The DNA database showed optimal speedup execution when utilizing short and long pattern lengths, while no specific database obtained the worst results. [ABSTRACT FROM AUTHOR]

Parallel computing is increasingly important in computer architectures, parallel architecture has become ubiquitous in our everyday lives. Novel architectures and programming models pose new challenges to algorithm design and system software development. This paper presents a two-stage column block parallel LU factorization algorithm for multiple-processor architectures. Any given matrix is first partitioned into large blocks, and then, every large block is partitioned into a number of small blocks according to the number of processors. Finally, the small column blocks are allocated to processors in an orderly “serpentine arrangement.” Each iteration of the column block parallel LU factorization is separated into two stages of operation. In the first stage, the first-step factorization operation is processed in advance and nonblocking communication is used to reduce the processor idle and waiting time and improve parallelism. In the second stage, the large blocks are used to satisfy more powerful processors, such as GPUs, which require more data to exploit their computing capabilities. Experiments are conducted on a multicore system and multi-GPU system with different configurations to test the algorithm's performance. Compared with other related column block parallel LU factorizations, the two-stage algorithm exhibits better load balancing and parallel execution time performance.