COMPARATIVE ANALYSIS OF BIT-PARALLEL STRING PATTERN MATCHING ALGORITHMS FOR BIOLOGICAL SEQUENCES

The inherent parallelism in a bit operation like AND/OR inside a computer word is known as bit parallelism. It plays a greater role in string pattern matching and has good application in the analysis of biological data. The use of recently developed bit parallel string matching algorithms approaches helps in improving the efficiency of the other string pattern matching algorithms. This paper discusses the working of some of these bit parallel string matching algorithms and their application on biological sequences. It also shows how bit-parallelism can be efficiently used to address various matching problems in Bioinformatics to analyze biological sequences such as Deoxyribonucleic acid (DNA), Ribonucleic acid (RNA), and Protein with examples. It can also serve as a greater tool for researchers when looking for the appropriate method to use on Biological sequences

Improved algorithms for string searching problems

We present improved practically efficient algorithms for several string searching problems, where we search for a short string called the pattern in a longer string called the text. We are mainly interested in the online problem, where the text is not preprocessed, but we also present a light indexing approach to speed up exact searching of a single pattern. The new algorithms can be applied e.g. to many problems in bioinformatics and other content scanning and filtering problems. In addition to exact string matching, we develop algorithms for several other variations of the string matching problem. We study algorithms for approximate string matching, where a limited number of errors is allowed in the occurrences of the pattern, and parameterized string matching, where a substring of the text matches the pattern if the characters of the substring can be renamed in such a way that the renamed substring matches the pattern exactly. We also consider searching multiple patterns simultaneously and searching weighted patterns, where the weight of a character at a given position reflects the probability of that character occurring at that position. Many of the new algorithms use the backward matching principle, where the characters of the text that are aligned with the pattern are read backward, i.e. from right to left. Another common characteristic of the new algorithms is the use of q-grams, i.e. q consecutive characters are handled as a single character. Many of the new algorithms are bit parallel, i.e. they pack several variables to a single computer word and update all these variables with a single instruction. We show that the q-gram backward string matching algorithms that solve the exact, approximate, or multiple string matching problems are optimal on average. We also show that the q-gram backward string matching algorithm for the parameterized string matching problem is sublinear on average for a class of moderately repetitive patterns. All the presented algorithms are also shown to be fast in practice when compared to earlier algorithms. We also propose an alphabet sampling technique to speed up exact string matching. We choose a subset of the alphabet and select the corresponding subsequence of the text. String matching is then performed on this reduced subsequence and the found matches are verified in the original text. We show how to choose the sampled alphabet optimally and show that the technique speeds up string matching especially for moderate to long patterns

Streaming Tree Transducers

Theory of tree transducers provides a foundation for understanding expressiveness and complexity of analysis problems for specification languages for transforming hierarchically structured data such as XML documents. We introduce streaming tree transducers as an analyzable, executable, and expressive model for transforming unranked ordered trees in a single pass. Given a linear encoding of the input tree, the transducer makes a single left-to-right pass through the input, and computes the output in linear time using a finite-state control, a visibly pushdown stack, and a finite number of variables that store output chunks that can be combined using the operations of string-concatenation and tree-insertion. We prove that the expressiveness of the model coincides with transductions definable using monadic second-order logic (MSO). Existing models of tree transducers either cannot implement all MSO-definable transformations, or require regular look ahead that prohibits single-pass implementation. We show a variety of analysis problems such as type-checking and checking functional equivalence are solvable for our model.Comment: 40 page

FPGA-based Query Acceleration for Non-relational Databases

Database management systems are an integral part of today’s everyday life. Trends like smart applications, the internet of things, and business and social networks require applications to deal efficiently with data in various data models close to the underlying domain. Therefore, non-relational database systems provide a wide variety of database models, like graphs and documents. However, current non-relational database systems face performance challenges due to the end of Dennard scaling and therefore performance scaling of CPUs. In the meanwhile, FPGAs have gained traction as accelerators for data management. Our goal is to tackle the performance challenges of non-relational database systems with FPGA acceleration and, at the same time, address design challenges of FPGA acceleration itself. Therefore, we split this thesis up into two main lines of work: graph processing and flexible data processing. Because of the lacking benchmark practices for graph processing accelerators, we propose GraphSim. GraphSim is able to reproduce runtimes of these accelerators based on a memory access model of the approach. Through this simulation environment, we extract three performance-critical accelerator properties: asynchronous graph processing, compressed graph data structure, and multi-channel memory. Since these accelerator properties have not been combined in one system, we propose GraphScale. GraphScale is the first scalable, asynchronous graph processing accelerator working on a compressed graph and outperforms all state-of-the-art graph processing accelerators. Focusing on accelerator flexibility, we propose PipeJSON as the first FPGA-based JSON parser for arbitrary JSON documents. PipeJSON is able to achieve parsing at line-speed, outperforming the fastest, vectorized parsers for CPUs. Lastly, we propose the subgraph query processing accelerator GraphMatch which outperforms state-of-the-art CPU systems for subgraph query processing and is able to flexibly switch queries during runtime in a matter of clock cycles

LIPIcs, Volume 274, ESA 2023, Complete Volume

LIPIcs, Volume 274, ESA 2023, Complete Volum