We outline the design and implementation of a system which implements the Simulation as a Service (SMaaS)Simulation as a Service (SaaS) model. SMaaS is based on the Software as a Service (SaaS)Simulation as a Service (SaaS) model, extending SaaS to include High-Performance Computing (HPC)-hosted applications. Simulations in an HPC context can be expensive, complex, and lengthy. The use of a cloud to provide and manage simulations as a service on an HPC cluster provides greater flexibility for users, in particular smaller businesses and educational institutions that might otherwise struggle to use simulation in their work. Adding a cloud to a standard HPC setup allows the HPC component to specialize in its strengths (e.g., performing calculations, storing Big Data), while the cloud can provide its own capabilities. We show how a cloud's ability to scale up/down and support heterogeneous environmentsHeterogeneous environments provides support for all phases of simulation workflow—education, prototyping, and production. This chapter covers several different systems built at the Ohio Supercomputer Center. All systems have been deployed to production and used by paying customers. In addition to strictly technical concerns issues related to payment, licensing, and other business topics are covered.

Large manufacturers increasingly leverage modelling and simulation to improve quality and reduce cost. Small manufacturers have not adopted these techniques due to sizable upfront costs for expertise, software and hardware. The software as a service (SaaS) model provides access to applications hosted in a cloud environment, allowing users to try services at low cost and scale as needed. We have extended SaaS to include high-performance computing-hosted applications, thus creating simulation as a service (SMaaS). Polymer portal is a first-generation SMaaS platform designed to integrate access to multiple modelling, simulation and training services. Polymer portal provides a number of features including an e-commerce front end, common AAA service, and support for both cloud-hosted virtual machine (VM) images and high-performance computing (HPC) jobs. It has been deployed for six months and has been used successfully for a number of training and simulation activities. This paper describes the requirements, challenges, design and implementation of the polymer portal.

In this paper, we describe the OnDemand web platform for providing OSC users integrated access to HPC systems, web applications and VNC services. We present the user experience and implementation of OnDemand and compare it with existing science gateway approaches.

Supercomputing centers, typically non-profit, government or university-based organizations with scarce resources, are increasingly being requested to provide customized web portals for user-centered access to their services in order to support a demanding customer base. These portals often have very similar architectures and meet similar requirements, with the variations primarily being in the specialized analysis applications, and in the input and output of these applications. Given these characteristics, Software Production Line Engineering (SPLE) approaches will be valuable in enabling development teams to cost-effectively meet demands. In this paper, we demonstrate a suite of web portals developed at The Ohio Supercomputer Center (OSC) by applying SPLE methodologies. We show how we applied feature modeling on these applications to identify commonalities in their application level features despite differences in their problem domains. We describe a common framework (we term it Per User DrupaL, or PUDL), which serves as the common foundation for these portals. We demonstrate the effectiveness of SPLE in terms of reduced development time and effort, and discuss the technical challenges faced in this process. Finally we propose, as an extension to our work, an automation framework for portal generation, which users could build their own customized portals.

We have reported previously on use of a web-based application, Supramap (http://supramap.org) for the study of biogeographic, genotypic, and phenotypic evolution. Using Supramap we have developed maps of the spread of drug-resistant influenza and host shifts in H1N1 and H5N1 influenza and coronaviruses such as SARS. Here we report on another zoonotic pathogen, H7 influenza, and provide an update on the implementation of Supramap as a web service. We find that the emergence of pathogenic strains of H7 is labile with many transitions from high to low pathogenicity, and from low to high pathogenicity. We use Supramap to put these events in a temporal and geospatial context. We identify several lineages of H7 influenza with biomarkers of high pathogenicity in regions that have not been reported in the scientific literature. The original implementation of Supramap was built with tightly coupled client and server software. Now we have decoupled the components to provide a modular web service for POY (http://poyws.org) that can be consumed by a data provider to create a novel application. To demonstrate the web service, we have produced an application, Geogenes (http://geogenes.org). Unlike in Supramap, in which the user is required to create and upload data files, in Geogenes the user works from a graphical interface to query an underlying dataset. Geogenes demonstrates how the web service can provide underlying processing for any sequence and metadata database. © The Willi Hennig Society 2012.

The challenges of data-intensive computing have been summarized (Gorton et al., 2008) as ‘‘managing and processing exponentially growing data volumes, often arriving in time-sensitive streams from arrays of sensors and instruments’’ and ‘‘significantly reducing data analysis cycles so that researchers can make timely decisions.’’ The management of such data requires integrated services for the (high-speed) transfer, storage, indexing, and retrieval of data. Enabling technologies for data management are under active development and investigation (including high-speed networks such as those studied by GENI (http://www.geni.net/), high-performance file systems and semantic ontologies for data access). In addition to existing cluster-based highperformance computing solutions, data-intensive cloud programming environments (e.g., MapReduce and Dryad) are emerging technologies that show promise. The Ohio Supercomputer Center (OSC) has supported data-intensive science projects in the physical sciences [e.g., ALICE—A Large Ion Collider Experiment (http://www .osc.edu/press/releases/2010/supercollider.shtml)] and the environmental sciences [e.g., ASR—Arctic System Reanalysis (http://www.osc.edu/press/releases/2007/bromwich.shtml)]. In biomedical sciences, OSC is actively supporting dataintensive biomedical research groups located at the Comprehensive Cancer Center (CCC) at The Ohio State University’s Medical Center as well as those at the Research Institute at Nationwide Children’s Hospital (RINCH). These organizations contain a number of core facilities, common laboratories providing analysis to a collection of research and clinical groups. Currently, OSC is engaged with the following core facilities at the CCC:

BACKGROUND:An important focus of genomic science is the discovery and characterization of all functional elements within genomes. In silico methods are used in genome studies to discover putative regulatory genomic elements (called words or motifs). Although a number of methods have been developed for motif discovery, most of them lack the scalability needed to analyze large genomic data sets.METHODS:This manuscript presents WordSeeker, an enumerative motif discovery toolkit that utilizes multi-core and distributed computational platforms to enable scalable analysis of genomic data. A controller task coordinates activities of worker nodes, each of which (1) enumerates a subset of the DNA word space and (2) scores words with a distributed Markov chain model.RESULTS:A comprehensive suite of performance tests was conducted to demonstrate the performance, speedup and efficiency of WordSeeker. The scalability of the toolkit enabled the analysis of the entire genome of Arabidopsis thaliana; the results of the analysis were integrated into The Arabidopsis Gene Regulatory Information Server (AGRIS). A public version of WordSeeker was deployed on the Glenn cluster at the Ohio Supercomputer Center.CONCLUSION:WordSeeker effectively utilizes concurrent computing platforms to enable the identification of putative functional elements in genomic data sets. This capability facilitates the analysis of the large quantity of sequenced genomic data.

Encyclopedias of regulatory genomic elements provide a foundation for research in areas such as disease diagnosis, disease treatment, and crop enhancement. The construction of complete encyclopedias of organism-specific genomic elements involved in gene regulation remains a significant challenge. To address this problem, the authors present novel bioinformatics strategies for exploring the word landscapes of putative regulatory regions of genomes. The methods are incorporated into the WordSeeker software tool, which is available at http://word-seeker.org. The effectiveness of these strategies is demonstrated through several case studies.