Parallelization of dynamic programming recurrences in computational biology

The rapid growth of biosequence databases over the last decade has led to a performance bottleneck in the applications analyzing them. In particular, over the last five years DNA sequencing capacity of next-generation sequencers has been doubling every six months as costs have plummeted. The data produced by these sequencers is overwhelming traditional compute systems. We believe that in the future compute performance, not sequencing, will become the bottleneck in advancing genome science. In this work, we investigate novel computing platforms to accelerate dynamic programming algorithms, which are popular in bioinformatics workloads. We study algorithm-specific hardware architectures that exploit fine-grained parallelism in dynamic programming kernels using field-programmable gate arrays: FPGAs). We advocate a high-level synthesis approach, using the recurrence equation abstraction to represent dynamic programming and polyhedral analysis to exploit parallelism. We suggest a novel technique within the polyhedral model to optimize for throughput by pipelining independent computations on an array. This design technique improves on the state of the art, which builds latency-optimal arrays. We also suggest a method to dynamically switch between a family of designs using FPGA reconfiguration to achieve a significant performance boost. We have used polyhedral methods to parallelize the Nussinov RNA folding algorithm to build a family of accelerators that can trade resources for parallelism and are between 15-130x faster than a modern dual core CPU implementation. A Zuker RNA folding accelerator we built on a single workstation with four Xilinx Virtex 4 FPGAs outperforms 198 3 GHz Intel Core 2 Duo processors. Furthermore, our design running on a single FPGA is an order of magnitude faster than competing implementations on similar-generation FPGAs and graphics processors. Our work is a step toward the goal of automated synthesis of hardware accelerators for dynamic programming algorithms

Wavefront Longest Common Subsequence Algorithm On Multicore And Gpgpu Platform.

String comparison is a central operation in numerous applications. It has a critical task in many operations such as data mining, spelling error correction and molecular biology (Tan et al, 2007; Michailidis and Margaritis, 2000)

Automated Genome-Wide Protein Domain Exploration

Exploiting the exponentially growing genomics and proteomics data requires high quality, automated analysis. Protein domain modeling is a key area of molecular biology as it unravels the mysteries of evolution, protein structures, and protein functions. A plethora of sequences exist in protein databases with incomplete domain knowledge. Hence this research explores automated bioinformatics tools for faster protein domain analysis. Automated tool chains described in this dissertation generate new protein domain models thus enabling more effective genome-wide protein domain analysis. To validate the new tool chains, the Shewanella oneidensis and Escherichia coli genomes were processed, resulting in a new peptide domain database, detection of poor domain models, and identification of likely new domains. The automated tool chains will require months or years to model a small genome when executing on a single workstation. Therefore the dissertation investigates approaches with grid computing and parallel processing to significantly accelerate these bioinformatics tool chains

Reconfigurable acceleration of genetic sequence alignment: A survey of two decades of efforts

Genetic sequence alignment has always been a computational challenge in bioinformatics. Depending on the problem size, software-based aligners can take multiple CPU-days to process the sequence data, creating a bottleneck point in bioinformatic analysis flow. Reconfigurable accelerator can achieve high performance for such computation by providing massive parallelism, but at the expense of programming flexibility and thus has not been commensurately used by practitioners. Therefore, this paper aims to provide a thorough survey of the proposed accelerators by giving a qualitative categorization based on their algorithms and speedup. A comprehensive comparison between work is also presented so as to guide selection for biologist, and to provide insight on future research direction for FPGA scientists

A framework for automatically generating optimized digital designs from C-language loops

Reconfigurable computing has the potential for providing significant performance increases to a number of computing applications. However, realizing these benefits requires digital design experience and knowledge of hardware description languages (HDLs). While a number of tools have focused on translation of high-level languages (HLLs) to HDLs, the tools do not always create optimized digital designs that are competitive with hand-coded solutions. This work describes an automatic optimization in the C-to-HDL transformation that reorganizes operations between pipeline stages in order to reduce critical path lengths. The effects of this optimization are examined on the MD5, SHA-1, and Smith-Waterman algorithms. Results show that the optimization results in performance gains of 13%-37% and that the automatically-generated implementations perform comparably to hand-coded implementations

A framework for automatically generating optimized digital designs from C-language loops

Reconfigurable computing has the potential for providing significant performance increases to a number of computing applications. However, realizing these benefits requires digital design experience and knowledge of hardware description languages (HDLs). While a number of tools have focused on translation of high-level languages (HLLs) to HDLs, the tools do not always create optimized digital designs that are competitive with hand-coded solutions. This work describes an automatic optimization in the C-to-HDL transformation that reorganizes operations between pipeline stages in order to reduce critical path lengths. The effects of this optimization are examined on the MD5, SHA-1, and Smith-Waterman algorithms. Results show that the optimization results in performance gains of 13%-37% and that the automatically-generated implementations perform comparably to hand-coded implementations

Tuning the Computational Effort: An Adaptive Accuracy-aware Approach Across System Layers

This thesis introduces a novel methodology to realize accuracy-aware systems, which will help designers integrate accuracy awareness into their systems. It proposes an adaptive accuracy-aware approach across system layers that addresses current challenges in that domain, combining and tuning accuracy-aware methods on different system layers. To widen the scope of accuracy-aware computing including approximate computing for other domains, this thesis presents innovative accuracy-aware methods and techniques for different system layers. The required tuning of the accuracy-aware methods is integrated into a configuration layer that tunes the available knobs of the accuracy-aware methods integrated into a system

Homology sequence analysis using GPU acceleration

A number of problems in bioinformatics, systems biology and computational biology field require abstracting physical entities to mathematical or computational models. In such studies, the computational paradigms often involve algorithms that can be solved by the Central Processing Unit (CPU). Historically, those algorithms benefit from the advancements of computing power in the serial processing capabilities of individual CPU cores. However, the growth has slowed down over recent years, as scaling out CPU has been shown to be both cost-prohibitive and insecure. To overcome this problem, parallel computing approaches that employ the Graphics Processing Unit (GPU) have gained attention as complementing or replacing traditional CPU approaches. The premise of this research is to investigate the applicability of various parallel computing platforms to several problems in the detection and analysis of homology in biological sequence. I hypothesize that by exploiting the sheer amount of computation power and sequencing data, it is possible to deduce information from raw sequences without supplying the underlying prior knowledge to come up with an answer. I have developed such tools to perform analysis at scales that are traditionally unattainable with general-purpose CPU platforms. I have developed a method to accelerate sequence alignment on the GPU, and I used the method to investigate whether the Operational Taxonomic Unit (OTU) classification problem can be improved with such sheer amount of computational power. I have developed a method to accelerate pairwise k-mer comparison on the GPU, and I used the method to further develop PolyHomology, a framework to scaffold shared sequence motifs across large numbers of genomes to illuminate the structure of the regulatory network in yeasts. The results suggest that such approach to heterogeneous computing could help to answer questions in biology and is a viable path to new discoveries in the present and the future.Includes bibliographical reference