Parallel data compression

Data compression schemes remove data redundancy in communicated and stored data and increase the effective capacities of communication and storage devices. Parallel algorithms and implementations for textual data compression are surveyed. Related concepts from parallel computation and information theory are briefly discussed. Static and dynamic methods for codeword construction and transmission on various models of parallel computation are described. Included are parallel methods which boost system speed by coding data concurrently, and approaches which employ multiple compression techniques to improve compression ratios. Theoretical and empirical comparisons are reported and areas for future research are suggested

Design and Evaluation of a BLAST Ungapped Extension Accelerator, Master\u27s Thesis

The amount of biosequence data being produced each year is growing exponentially. Extracting useful information from this massive amount of data is becoming an increasingly difficult task. This thesis focuses on accelerating the most widely-used software tool for analyzing genomic data, BLAST. This thesis presents Mercury BLAST, a novel method for accelerating searches through massive DNA databases. Mercury BLAST takes a streaming approach to the BLAST computation by offloading the performance-critical sections onto reconfigurable hardware. This hardware is then used in combination with the processor of the host system to deliver BLAST results in a fraction of the time of the general-purpose processor alone. Mercury BLAST makes use of new algorithms combined with reconfigurable hardware to accelerate BLAST-like similarity search. An evaluation of this method for use in real BLAST-like searches is presented along with a characterization of the quality of results associated with using these new algorithms in specialized hardware. The primary focus of this thesis is the design of the ungapped extension stage of Mercury BLAST. The architecture of the ungapped extension stage is described along with the context of this stage within the Mercury BLAST system. The design is compact and performs over 20× faster than that of the standard software ungapped extension, yielding close to 50× speedup over the complete software BLAST application. The quality of Mercury BLAST results is essentially equivalent to the standard BLAST results

FPGA acceleration of DNA sequence alignment: design analysis and optimization

Existing FPGA accelerators for short read mapping often fail to utilize the complete biological information in sequencing data for simple hardware design, leading to missed or incorrect alignment. In this work, we propose a runtime reconfigurable alignment pipeline that considers all information in sequencing data for the biologically accurate acceleration of short read mapping. We focus our efforts on accelerating two string matching techniques: FM-index and the Smith-Waterman algorithm with the affine-gap model which are commonly used in short read mapping. We further optimize the FPGA hardware using a design analyzer and merger to improve alignment performance. The contributions of this work are as follows. 1. We accelerate the exact-match and mismatch alignment by leveraging the FM-index technique. We optimize memory access by compressing the data structure and interleaving the access with multiple short reads. The FM-index hardware also considers complete information in the read data to maximize accuracy. 2. We propose a seed-and-extend model to accelerate alignment with indels. The FM-index hardware is extended to support the seeding stage while a Smith-Waterman implementation with the affine-gap model is developed on FPGA for the extension stage. This model can improve the efficiency of indel alignment with comparable accuracy versus state-of-the-art software. 3. We present an approach for merging multiple FPGA designs into a single hardware design, so that multiple place-and-route tasks can be replaced by a single task to speed up functional evaluation of designs. We first experiment with this approach to demonstrate its feasibility for different designs. Then we apply this approach to optimize one of the proposed FPGA aligners for better alignment performance.Open Acces

ROACH accelerated BLAST

Includes abstract.Includes bibliographical references (p. 115-118).Reconfigurable computing, in recent years, has been taking great strides in becoming part of mainstream computing largely due to the rapid growth in the size of FPGAs and their ability to adapt to certain complex applications efficiently. This dissertation investigates the reuse of application specific hardware developed for radio astronomy in accelerating a popular bioinformatics algorithm

Investigation into the wafer-scale integration of fine-grain parallel processing computer systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis investigates the potential of wafer-scale integration (WSI) for the implementation of low-cost fine-grain parallel processing computer systems. As WSI is a relatively new subject, there was little work on which to base investigations. Indeed, most WSI architectures existed only as untried and sometimes vague proposals. Accordingly, the research strategy approached this problem by identifying a representative WSI structure and architecture on which to base investigations. An analysis of architectural proposals identified associative memory to be general purpose parallel processing component used in a wide range of WSI architectures. Furthermore, this analysis provided a set of WSI-level design requirements to evaluate the sustainability of different architectures as research vehicles. The WSI-ASP (WASP) device, which has a large associative memory as its main component is shown to meet these requirements and hence was chosen as the research vehicle. Consequently, this thesis addresses WSI potential through an in-depth investigation into the feasibility of implementing a large associative memory for the WASP device that meets the demanding technological constraints of WSI. Overall, the thesis concludes that WSI offers significant potential for the implementation of low-cost fine-grain parallel processing computer systems. However, due to the dual constraints of thermal management and the area required for the power distribution network, power density is a major design constraint in WSI. Indeed, it is shown that WSI power densities need to be an order of magnitude lower than VLSI power densities. The thesis demonstrates that for associative memories at least, VLSI designs are unsuited to implementation in WSI. Rather, it is shown that WSI circuits must be closely matched to the operational environment to assure suitable power densities. These circuits are significantly larger than their VLSI equivalents. Nonetheless, the thesis demonstrates that by concentrating on the most power intensive circuits, it is possible to achieve acceptable power densities with only a modest increase in area overheads.SER

A Parallel Computational Approach for String Matching- A Novel Structure with Omega Model

In r e cent day2019;s parallel string matching problem catch the attention of so many researchers because of the importance in different applications like IRS, Genome sequence, data cleaning etc.,. While it is very easily stated and many of the simple algorithms perform very well in practice, numerous works have been published on the subject and research is still very active. In this paper we propose a omega parallel computing model for parallel string matching. The algorithm is designed to work on omega model pa rallel architecture where text is divided for parallel processing and special searching at division point is required for consistent and complete searching. This algorithm reduces the number of comparisons and parallelization improves the time efficiency. Experimental results show that, on a multi - processor system, the omega model implementation of the proposed parallel string matching algorithm can reduce string matching time

AI for Healthcare: Diagnosis, Clinical-Trial Matching, and Patient Recruitment

Medical diagnosis is the most critical component in the treatment of a patient. But diagnosis often is a complicated process since a myriad of diseases share the same symptoms. If a patient is diagnosed with a disease in its end-stage, potential new treatments (clinical trials) are sometimes the last option available. However, matching a patient to the correct clinical-trial requires advanced medical knowledge on behalf of the patient. In this study, we try to address the following problems and close the technical gaps, (i) Diagnosis: Advances in neural network approaches and the availability of massive labeled datasets have sparked renewed interests in automated diagnosis. We explore novel techniques to identify pathology in chest radiographs by using a labeled radiograph dataset, which is also substantially large for the domain of medical diagnosis. (ii) Clinical-Trial Matching: Given the difficulty of perusing the jargon in standard clinical trial texts, we try to complement the process by using machine learning and information retrieval methods to fetch similar health records showing the entities responsible for the match. We implement an efficient visual tool (TextMed) to aid our algorithm and make it easier for users to utilize the power of machine learning. Our tool helps in searching through a database of criteria and records and fetches the information about the query