SWARM: Scheduling Large-Scale Jobs over the Loosely-Coupled HPC Clusters

Abstract — Compute-intensive scientific applications are heavily reliant on the available quantity of computing resources. The Grid paradigm provides a large scale computing environment for scientific users. However, conventional Grid job submission tools do not provide a high-level job scheduling environment for these users across multiple institutions. For extremely large number of jobs, a more scalable job scheduling framework that can leverage highly distributed clusters and supercomputers is required. In this paper, we propose a high-level job scheduling Web service framework, Swarm. Swarm is developed for scientific applications that must submit massive number of high-throughput jobs or workflows to highly distributed computing clusters. The Swarm service itself is designed to b

Distributed Many-to-Many Protein Sequence Alignment using Sparse Matrices

Identifying similar protein sequences is a core step in many computational biology pipelines such as detection of homologous protein sequences, generation of similarity protein graphs for downstream analysis, functional annotation and gene location. Performance and scalability of protein similarity searches have proven to be a bottleneck in many bioinformatics pipelines due to increases in cheap and abundant sequencing data. This work presents a new distributed-memory software, PASTIS. PASTIS relies on sparse matrix computations for efficient identification of possibly similar proteins. We use distributed sparse matrices for scalability and show that the sparse matrix infrastructure is a great fit for protein similarity searches when coupled with a fully-distributed dictionary of sequences that allows remote sequence requests to be fulfilled. Our algorithm incorporates the unique bias in amino acid sequence substitution in searches without altering the basic sparse matrix model, and in turn, achieves ideal scaling up to millions of protein sequences.Comment: To appear in International Conference for High Performance Computing, Networking, Storage, and Analysis (SC'20

Large-scale methods in computational genomics

The explosive growth in biological sequence data coupled with the design and deployment of increasingly high throughput sequencing technologies has created a need for methods capable of processing large-scale sequence data in a time and cost effective manner. In this dissertation, we address this need through the development of faster algorithms, space-efficient methods, and high-performance parallel computing techniques for some key problems in computational genomics;The first problem addressed is the clustering of DNA sequences based on a measure of sequence similarity. Our clustering method: (i) guarantees linear space complexity, in contrast to the quadratic memory requirements of previously developed methods; (ii) identifies sequence pairs containing long maximal matches in the decreasing order of their maximal match lengths in run-time proportional to the sum of input and output sizes; (iii) provides heuristics to significantly reduce the number of pairs evaluated for checking sequence similarity without affecting quality; and (iv) has parallel strategies that provide linear speedup and a proportionate reduction in space per processor. Our approach has significantly enhanced the problem size reach while also drastically reducing the time to solution;The next problem we address is the de novo detection of genomic repeats called Long Terminal Repeat (LTR) retrotransposons. Our algorithm guarantees linear space complexity and produces high quality candidates for prediction in run-time proportional to the sum of input and output sizes. Validation of our approach on the yeast genome demonstrates both superior quality and performance results when compared to previously developed software;In a genome assembly project, fragments sequenced from a target genome are computationally assembled into numerous supersequences called contigs , which are then ordered and oriented into scaffolds . In this dissertation, we introduce a new problem called retroscaffolding for scaffolding contigs based on the knowledge of their LTR retrotransposon content. Through identification of sequencing gaps that span LTR retrotransposons, retroscaffolding provides a mechanism for prioritizing sequencing gaps for finishing purposes;While most of the problems addressed here have been studied previously, the main contribution in this dissertation is the development of methods that can scale to the largest available sequence collections

Assembly and analysis of complex plant genomes

Concurrent advances in high-throughput sequencing and assembly have led to the completion of many complex genomes. Even so, these assemblies require substantial computational resources. In this dissertation, we present a massively parallel approach that scales to thousands of processors without duplicating the biological expertise present in conventional assembly software.;Additional bioinformatics techniques were required to accurately assemble the maize genome including novel repeat detection, and the resulting framework has been strongly supported by maize experimental data. More recently, this framework has been generalized for fruit fly, sorghum, soybean and environmental sequence assemblies.;Questions in plant genome analysis were also addressed. For example, we have discovered an estimated 350 orphan maize genes and have shown that approximately 1% of all maize genes were recently duplicated, many of which into at least two functional copies. LCM-454 sequencing is introduced and analyses that indicate this approach can discover rare, potentially tissue-specific transcripts and thousands of SNPs will be presented.;This dissertation combines high performance computing, computational biology and high-throughput sequencing for our ongoing work on the maize genome project. We conclude by describing how these contributions can be useful for any species, including non-model organisms that are unlikely to be fully sequenced