Youngho Kim

A space-efficient alphabet-independent Four-Russians' lookup table and a multithreaded Four-Russians' edit distance algorithm

Given two strings X ( | X | = m ) and Y ( | Y | = n ) over an alphabet Σ, the edit distance between X and Y can be computed in O ( m n / t ) time with the help of the Four-Russians lookup table whose block size is t. The Four-Russians lookup table can be constructed in O ( ( 3 | Σ | ) 2 t t 2 ) time using O ( ( 3 | Σ | ) 2 t t ) space. However, the construction time and space requirement of the lookup table grow very fast as the alphabet size increases and thus it has been used only when | Σ | is very small. For example, when a string is a protein sequence, | Σ | = 20 and thus it is almost impossible to use the Four-Russians lookup table on typical workstations. In this paper, we present an efficient alphabet-independent Four-Russians lookup table. It requires O ( 3 2 t ( 2 t ) ! t ) space and can be constructed in O ( 3 2 t ( 2 t ) ! t 2 ) time. Thus, the Four-Russians lookup table can be constructed and used irrespective of the alphabet size. The time and space complexity were achieved by compacting the lookup table using a clever encoding of the preprocessed strings. Experimental results show that the space requirement of the lookup table is reduced to about 1/5,172,030 of its original size when | Σ | = 26 and t = 4 . Furthermore, we present efficient multithreaded parallel algorithms for edit distance computation using the Four-Russians lookup table. The parallel algorithm for lookup table construction runs in O ( t ) time and the parallel algorithm for edit distance computation between X and Y runs in O ( m + n ) time. Experiments performed on CUDA-supported GPU show that our algorithm runs about 942 times faster than the sequential version of the original Four-Russians algorithm for 100 pairs of random strings of length approximately 1,000 when | Σ | = 4 and t = 4 .

Approximate Periods of Strings based on Distance Sum for DNA Sequence Analysis

Repetitive strings such as periods have been studied vigorously in so diverse fields as data compression, computer-assisted music analysis, bioinformatics, and etc. In bioinformatics, periods are highly related to repetitive patterns in DNA sequences so called tandem repeats. In some cases, quite similar but not the same patterns are repeated and thus we need approximate string matching algorithms to study tandem repeats in DNA sequences. In this paper, we propose a new definition of approximate periods of strings based on distance sum. Given two strings

Parallel Computation For The Edit Distance Based On The Four-Russians` Algorithm

p({mid}p{mid}

A power- and storage-efficient HLS media server for multi-bitrate VOD services

Approximate string matching problems have been studied in diverse fields. Recently, fast approximate string matching algorithms are being used to reduce the time and costs for the next generation sequencing. To measure the amounts of errors between two strings, we use a distance function such as the edit distance. Given two strings X(|X| = m) and Y(|Y| = n) over an alphabet Σ , the edit distance between X and Y is the minimum number of edit operations to convert X into Y. The edit distance between X and Y can be computed using the well-known dynamic programming technique in O(mn) time and space. The edit distance also can be computed using the Four-Russians algorithm whose preprocessing step runs in O((3?Σ?) 2t t 2 ) time and O((3?Σ?) 2t t) space and the computation step runs in O(mn/t) time and O(mn) space where t represents the size of the block. In this paper, we present a parallelized version of the computation step of the Four-Russians algorithm. Our algorithm computes the edit distance between X and Y in O(m+n) time using m/t threads. Then we implemented both the sequential version and our parallelized version of the Four-Russians algorithm using CUDA to compare the execution times. When t = 1 and t = 2, our algorithm runs about 10 times and 3 times faster than the sequential algorithm, respectively.

Parallel Computation for Extended Edit Distances Using the Shared Memory on GPU

The growth of social media and mobile service, and the diversity of client devices have greatly increased media servers storage costs and network traffic. This paper proposes an HTTP live streaming (HLS) media server for multi-bitrate video-on-demand (VOD) services that is efficient in terms of power consumption and storage space. The proposed server provides efficient storage space management by taking into account the users video streaming patterns and by exploiting real-time transcoding. The experiment results show that the proposed server yields an enhanced storage efficiency compared to the previous HLS media server. The proposed HLS media server can decrease storage space by 25% when the distribution of video qualities requested by users is in the form of a normal distribution, and by 30% when it is a Pareto distribution. Improved storage efficiency also leads to improved power efficiency. The proposed HLS media server reduces the required power consumption by 9.9%.

Two-dimensional SnS2 materials as high-performance NO2 sensors with fast response and high sensitivity

Given two strings ue017 and ue018 ( ue04due017ue04due047ue0f1 , ue04due018ue04due047 ue0f2) over an alphabet ue096, the extended edit distance between ue017 and ue018 can be computed using dynamic programming in ue00eue044ue0f1ue0f2ue045 time and space. Recently, a parallel algorithm that takes ue00eue044ue0f1ue048ue0f2ue045 time and ue00eue044ue0f1ue0f2ue045 space using ue0f1 threads to compute the extended edit distance between ue017 and ue018 was presented. In this paper, we present an improved parallel algorithm using the shared memory on GPU. The experimental results show that our parallel algorithm runs about 19~25 times faster than the previous parallel algorithm.