A Survey of Processing Systems for Phylogenetics and Population Genetics

The COVID-19 pandemic brought Bioinformatics into the spotlight, revealing that several existing methods, algorithms, and tools were not well prepared to handle large amounts of genomic data efficiently. This led to prohibitively long execution times and the need to reduce the extent of analyses to obtain results in a reasonable amount of time. In this survey, we review available high-performance computing and hardware-accelerated systems based on FPGA and GPU technology. Optimized and hardware-accelerated systems can conduct more thorough analyses considerably faster than pure software implementations, allowing to reach important conclusions in a timely manner to drive scientific discoveries. We discuss the reasons that are currently hindering high-performance solutions from being widely deployed in real-world biological analyses and describe a research direction that can pave the way to enable this

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

Characterizing and Accelerating Bioinformatics Workloads on Modern Microarchitectures

Bioinformatics, the use of computer techniques to analyze biological data, has been a particularly active research field in the last two decades. Advances in this field have contributed to the collection of enormous amounts of data, and the sheer amount of available data has started to overtake the processing capability possible with current computer systems. Clearly, computer architects need to have a better understanding of how bioinformatics applications work and what kind of architectural techniques could be used to accelerate these important scientific workloads on future processors. In this dissertation, we develop a bioinformatic benchmark suite and provide a detailed characterization of these applications in common use today from a computer architect's point of view. We analyze a wide range of detailed execution characteristics including instruction mix, IPC measurements, L1 and L2 cache misses on a real architecture; and proceed to analyze the workloads' memory access characteristics. We then concentrate on accelerating a particularly computationally intensive bioinformatics workload on the novel Cell Broadband Engine multiprocessor architecture. The HMMER workload is used for protein profile searching using hidden Markov models, and most of its execution time is spent running the Viterbi algorithm. We parallelize and partition the HMMER application to implement it on the Cell Broadband Engine. In order to run the Viterbi algorithm on the 256KB local stores of the Cell BE synergistic processing units (SPEs), we present a method to develop a fast SIMD implementation of the Viterbi algorithm that reduces the storage requirements significantly. Our HMMER implementation for the Cell BE architecture, Cell-HMMER, exploits the multiple levels of parallelism inherent in this application, and can run protein profile searches up to 27.98 times faster than a modern dual-core x86 microprocessor

Energy Efficiency of Sequence Alignment Tools - Software and Hardware Perspectives

Kierzynka M, Kosmann L, vor dem Berge M, et al. Energy Efficiency of Sequence Alignment Tools - Software and Hardware Perspectives. Future Generation Computer Systems. 2016;67:455-465

Large-scale Data Analysis and Deep Learning Using Distributed Cyberinfrastructures and High Performance Computing

Data in many research fields continues to grow in both size and complexity. For instance, recent technological advances have caused an increased throughput in data in various biological-related endeavors, such as DNA sequencing, molecular simulations, and medical imaging. In addition, the variance in the types of data (textual, signal, image, etc.) adds an additional complexity in analyzing the data. As such, there is a need for uniquely developed applications that cater towards the type of data. Several considerations must be made when attempting to create a tool for a particular dataset. First, we must consider the type of algorithm required for analyzing the data. Next, since the size and complexity of the data imposes high computation and memory requirements, it is important to select a proper hardware environment on which to build the application. By carefully both developing the algorithm and selecting the hardware, we can provide an effective environment in which to analyze huge amounts of highly complex data in a large-scale manner. In this dissertation, I go into detail regarding my applications using big data and deep learning techniques to analyze complex and large data. I investigate how big data frameworks, such as Hadoop, can be applied to problems such as large-scale molecular dynamics simulations. Following this, many popular deep learning frameworks are evaluated and compared to find those that suit certain hardware setups and deep learning models. Then, we explore an application of deep learning to a biomedical problem, namely ADHD diagnosis from fMRI data. Lastly, I demonstrate a framework for real-time and fine-grained vehicle detection and classification. With each of these works in this dissertation, a unique large-scale analysis algorithm or deep learning model is implemented that caters towards the problem and leverages specialized computing resources

Space Efficient Sequence Alignment for SRAM-Based Computing: X-Drop on the Graphcore IPU

Dedicated accelerator hardware has become essential for processing AI-based workloads, leading to the rise of novel accelerator architectures. Furthermore, fundamental differences in memory architecture and parallelism have made these accelerators targets for scientific computing. The sequence alignment problem is fundamental in bioinformatics; we have implemented the $X$-Drop algorithm, a heuristic method for pairwise alignment that reduces search space, on the Graphcore Intelligence Processor Unit (IPU) accelerator. The $X$-Drop algorithm has an irregular computational pattern, which makes it difficult to accelerate due to load balancing. Here, we introduce a graph-based partitioning and queue-based batch system to improve load balancing. Our implementation achieves $10\times$ speedup over a state-of-the-art GPU implementation and up to $4.65\times$ compared to CPU. In addition, we introduce a memory-restricted $X$-Drop algorithm that reduces memory footprint by $55\times$ and efficiently uses the IPU's limited low-latency SRAM. This optimization further improves the strong scaling performance by $3.6\times$.Comment: 12 pages, 7 figures, 2 table

High-Performance Meta-Genomic Gene Identification

Computational Genomics, or Computational Genetics, refers to the use of computational and statistical analysis for understanding the structure and the function of genetic material in organisms. The primary focus of research in computational genomics in the past three decades has been the understanding of genomes and their functional elements by analyzing biological sequence data. The high demand for low-cost sequencing has driven the development of highthroughput sequencing technologies, next-generation sequencing (NGS), that parallelize the sequencing process, producing thousands or millions of sequences concurrently. Moore’s Law is the observation that the number of transistors on integrated circuits doubles approximately every two years; correspondingly, the cost per transistor halves. The cost of DNA sequencing declines much faster, which implies more new DNA data will be obtained. This large-scale sequence data, produced with high throughput sequencing technologies, needs to be processed in a time-effective and cost-effective manner. In this dissertation, we present a high-performance meta-genome gene identification framework. This framework includes four modules: filter, alignment, error correction, and gene identification. The following chapters describe the proposed design and evaluation of this pipeline. The most computationally expensive kernel in the framework is the alignment procedure. Thus, the filter module is developed to determine unnecessary alignment operations. Without the filter module, the alignment module requires 1.9 hours to complete all-to-all alignment on a test file of size 512,000 sequences with each sequence average length 750 base pairs by using ten Kepler K20 NVIDIA GPU. On the other hand, when combined with the filter kernel, the total time is 11.3 minutes. Note that the ideal speedup is nearly 91.4 times faster when new alignment kernel is run on ten GPUs ( 10*9.14). We conclude that accuracy can be achieved at the expense of more resources while operating frequency can still be maintained

Research And Application Of Parallel Computing Algorithms For Statistical Phylogenetic Inference

Estimating the evolutionary history of organisms, phylogenetic inference, is a critical step in many analyses involving biological sequence data such as DNA. The likelihood calculations at the heart of the most effective methods for statistical phylogenetic analyses are extremely computationally intensive, and hence these analyses become a bottleneck in many studies. Recent progress in computer hardware, specifically the increase in pervasiveness of highly parallel, many-core processors has created opportunities for new approaches to computationally intensive methods, such as those in phylogenetic inference. We have developed an open source library, BEAGLE, which uses parallel computing methods to greatly accelerate statistical phylogenetic inference, for both maximum likelihood and Bayesian approaches. BEAGLE defines a uniform application programming interface and includes a collection of efficient implementations that use NVIDIA CUDA, OpenCL, and C++ threading frameworks for evaluating likelihoods under a wide variety of evolutionary models, on GPUs as well as on multi-core CPUs. BEAGLE employs a number of different parallelization techniques for phylogenetic inference, at different granularity levels and for distinct processor architectures. On CUDA and OpenCL devices, the library enables concurrent computation of site likelihoods, data subsets, and independent subtrees. The general design features of the library also provide a model for software development using parallel computing frameworks that is applicable to other domains. BEAGLE has been integrated with some of the leading programs in the field, such as MrBayes and BEAST, and is used in a diverse range of evolutionary studies, including those of disease causing viruses. The library can provide significant performance gains, with the exact increase in performance depending on the specific properties of the data set, evolutionary model, and hardware. In general, nucleotide analyses are accelerated on the order of 10-fold and codon analyses on the order of 100-fold