Subhashini Sivagnanam

Apache Airavata Sharing Service: A Tool for Enabling User Collaboration in Science Gateways

Science Gateways provide user environments and a set of supporting services that help researchers make effective and enhanced use of a diverse set of computing, storage, and related resources. Gateways provide the services and tools users require to enable their scientific exploration, which includes tasks such as running computer simulations or performing data analysis. Historically gateways have been constructed to support the workflow of individual users, but collaboration between users has become an increasingly important part of the discovery process. This trend has created a driving need for gateways to support data sharing between users. For example, a chemistry research group may want to run simulations collaboratively, analyze experimental data or tune parameter studies based on simulation output generated by peers, whether as a default capability, or through explicit creation of sharing privileges. As another example, students in a classroom setting may be required to share their simulation output or data analysis results with the instructor. However most existing gateways (including the popularly used XSEDE gateways SEAGrid, Ultrascan, CIPRES, and NSG), do not support direct data sharing, so users have to handle these collaborations outside the gateway environment. Given the importance of collaboration in current scientific practice, user collaboration should be a prime consideration in building science gateways. In this work, we present design considerations and implementation of a generic model that can be used to describe and handle a diverse set of user collaboration use cases that arise in gateways, based on general requirements gathered from the SEAGrid, CIPRES, and NSG gateways. We then describe the integration of this sharing service into these gateways. Though the model and the system were tested and used in the context of Science Gateways, the concepts are universally applicable to any domain, and the service can support data sharing in a wide variety of use cases.

Early experiences in developing and managing the neuroscience gateway

The last few decades have seen the emergence of computational neuroscience as a mature field where researchers are interested in modeling complex and large neuronal systems and require access to high performance computing machines and associated cyber infrastructure to manage computational workflow and data. The neuronal simulation tools, used in this research field, are also implemented for parallel computers and suitable for high performance computing machines. But using these tools on complex high performance computing machines remains a challenge because of issues with acquiring computer time on these machines located at national supercomputer centers, dealing with complex user interface of these machines, dealing with data management and retrieval. The Neuroscience Gateway is being developed to alleviate and/or hide these barriers to entry for computational neuroscientists. It hides or eliminates, from the point of view of the users, all the administrative and technical barriers and makes parallel neuronal simulation tools easily available and accessible on complex high performance computing machines. It handles the running of jobs and data management and retrieval. This paper shares the early experiences in bringing up this gateway and describes the software architecture it is based on, how it is implemented, and how users can use this for computational neuroscience research using high performance computing at the back end. We also look at parallel scaling of some publicly available neuronal models and analyze the recent usage data of the neuroscience gateway. Copyright

Open Source Brain: a collaborative resource for visualizing, analyzing, simulating and developing standardized models of neurons and circuits

Computational models are powerful tools for investigating brain function in health and disease. However, biologically detailed neuronal and circuit models are complex and implemented in a range of specialized languages, making them inaccessible and opaque to many neuroscientists. This has limited critical evaluation of models by the scientific community and impeded their refinement and widespread adoption. To address this, we have combined advances in standardizing models, open source software development and web technologies to develop Open Source Brain, a platform for visualizing, simulating, disseminating and collaboratively developing standardized models of neurons and circuits from a range of brain regions. Model structure and parameters can be visualized and their dynamical properties explored through browser-controlled simulations, without writing code. Open Source Brain makes neural models transparent and accessible and facilitates testing, critical evaluation and refinement, thereby helping to improve the accuracy and reproducibility of models, and their dissemination to the wider community.

Evolutionary algorithm optimization of biological learning parameters in a biomimetic neuroprosthesis

Biomimetic simulation permits neuroscientists to better understand the complex neuronal dynamics of the brain. Embedding a biomimetic simulation in a closed-loop neuroprosthesis, which can read and write signals from the brain, will permit applications for amelioration of motor, psychiatric, and memory-related brain disorders. Biomimetic neuroprostheses require real-time adaptation to changes in the external environment, thus constituting an example of a dynamic data-driven application system. As model fidelity increases, so does the number of parameters and the complexity of finding appropriate parameter configurations. Instead of adapting synaptic weights via machine learning, we employed major biological learning methods: spike-timing dependent plasticity and reinforcement learning. We optimized the learning metaparameters using evolutionary algorithms, which were implemented in parallel and which used an island model approach to obtain sufficient speed. We employed these methods to train a cortical spiking model to utilize macaque brain activity, indicating a selected target, to drive a virtual musculoskeletal arm with realistic anatomical and biomechanical properties to reach to that target. The optimized system was able to reproduce macaque data from a comparable experimental motor task. These techniques can be used to efficiently tune the parameters of multiscale systems, linking realistic neuronal dynamics to behavior, and thus providing a useful tool for neuroscience and neuroprosthetics.

A neuroscience gateway: software and implementation

In this paper, we describe the neuroscience gateway (NSG), which facilitates access to high performance computing resources for computational neuroscientists. Through a simple web-based portal, the NSG provides a streamlined environment for uploading models, specifying HPC job parameters, querying running job status, receiving job completion notices, and storing and retrieving output data. The NSG architecture transparently distributes user jobs to appropriate HPC resources available through the XSEDE organization.

The CIPRES workbench: a flexible framework for creating science gateways

Here we describe the CIPRES Workbench (CW), an open source software framework for creating new science gateways with minimal overhead. The CW is a web application that can be deployed on a modest server, and can be configured to submit command line instructions to any resource where the application has submission privileges. It is designed to be highly configurable/customizable, and supports GUI-based access to HPC resources through a web browser interface as well as programmatic access via a ReSTful API. Using browser access, the CW architecture creates an environment with secure user accounts where user input data, job results, and job provenance are stored. The ReSTful API allows users with a registered a client application to deliver command lines to analytical codes and retrieve results from remote compute resources. A development effort is underway to allow the CW to submit jobs via the Science Gateways as a Platform (SciGaP) services hosted at Indiana University.

TeraGrid resource selection tools: a road test

On a grid of computers, users often must decide between individual machines for job submission. Usually, the goal is to minimize time-to-completion. Several tools are available on TeraGrid to help users make this decision. In this paper, we use these tools to perform actual job submissions on TeraGrid machines. We evaluate the time-to-job-start effectiveness of these tools.

Understanding the Evolving Cyberinfrastructure Needs of the Neuroscience Community

In this paper, we first present a brief summary of the Neuroscience Gateway (NSG) which has been in operation since 2013. NSG is providing computational neuroscientists access to Extreme Science and Engineering Discovery Environment (XSEDE) high performance computing (HPC) resources. As a part of running the NSG we have interacted closely with the neuroscience community. This has given us the opportunity to receive input and feedback from the neuroscience researchers regarding their cyberinfrastructure needs. This is now more important given the context of the BRAIN (Brain Research through Advancing Innovative Neurotechnologies) Initiative which is a national initiative announced in 2013. Based on this interaction with the neuroscience community and the input we have received for the last three years, we analyze the comprehensive cyberinfrastructure needs of the neuroscience community in the second part of the paper.

The Neuroscience Gateway: Enabling Large Scale Modeling and Data Processing in Neuroscience

The NSF funded Neuroscience Gateway (NSG) has been in operation since the early 2013. We originally designed NSG to reduce technical and administrative barriers that exist to using high performance computing resources for computational neuroscientists. In the last two years, in addition to computational neuroscientists, cognitive and experimental neuroscientists are also using NSG. Currently NSG has over 600 registered users and it is steadily growing. Users can access NSG via a web portal and via RESTful programmatic access. A particular usage mode of programmatic access to NSG enables users of community neuroscience projects such as the Open Source Brain, research projects within the European Human Brain Project and others to access HPC resources via NSG without having to obtain their own accounts on NSG. Based on demand and usage, over the last five years we have successfully acquired increasingly larger allocations (millions to ~ten million core hours) on resources of the Extreme Science and Engineering Discovery Environment (XSEDE) program via the competitive peer review process. We will discuss the overall NSG architecture. We implemented NSG from the generic CIPRES science gateway software to create the NSG specifically for the neuroscience community. We will describe the front end user interface, based on web portal and RESTful programmatic access, and the backend architecture. We will discuss how NSG is evolving over time in response to the interests and needs of the neuroscience community, adapting itself to become a dissemination platform for new tools and pipelines, and becoming an environment for modelers and experimentalists to jointly develop models.