With recent advances in data collection technologies such as remote sensing and global positioning systems, the amount of spatial data being produced has been increasing at a staggering rate. Simultaneously, a shift is being experienced in computing from single-core to multi-core processors. To effectively utilize the computational power afforded by these new generation of processors for serving data-intensive geospatial applications, parallel computing techniques need to be employed. Parallel computing, however, raises new challenges associated with handling the input and output of spatial data in parallel. This paper describes a Parallel Input/Output System (PIOS) to address challenges associated with handling large amounts of diverse spatial data. The PIOS is based on a hierarchical structure that uses a scalable file partitioning strategy and combines data and metadata to enable efficient handling of terabyte-scale data sets in parallel. A spatially-explicit agent-based model is developed as a case study. Computational experiments were conducted on a supercomputer supported by the National Science Foundation. PIOS achieved ten times speedup in parallel input/output time, and was demonstrated to efficiently scale to over one thousand processing cores and handle multiple terabytes of data.

Information services are a critical piece of Grid infrastructure, they collect, aggregate, and organize sources of Grid resource information, and provide the information to applications to schedule computational tasks, and enable Virtual Organizations (VO) to share distributed computing resources. To be successful an information provider needs to be able to aggregate diverse information from numerous sources, publish information into different Grid monitoring systems, function under different Grid deployments, and be adaptable to different information schemas. Addressing these challenges would provide a platform for an interoperable information service. In this chapter, we present the Modular Information Provider (MIP) that address the aforementioned challenges and eliminates the shortcomings of the existing Grid information providers. MIP adopts a XML-based data model to enable robust schema validation as well as to support the latest generation of service-oriented Grid monitoring software. MIP provides a critical capability for production Grid environments focusing on achieving Grid interoperability and manageability. To validate this we conduct experiments on the Open Science Grid and Grid Australia. These experiments demonstrate MIP’s Grid-independent approach is successful and can easily adapt to different software stacks and the heterogeneous configuration of several sites on a Grid. We use GISolve, a TeraGrid science gateway as an example to demonstrate how MIP is useful from the perspective of Grid application. GISolve provides a geospatial problem solving platform that hides the complexity of Grid information services based on MIP, which enables GISolve applications to access multiple Grids seamlessly. We illustrate how MIP is integrated within GISolve so that GISolve applications can benefit from the interoperable information service MIP provides.

The Modular Information Provider (MIP) has been developed to systematically aggregate multiple sources of information for grid information services (GIS). MIP undertakes the challenge of mapping information from a large number of sources to information services with minimal human intervention. MIP addresses such mappings using a modular approach to achieve interoperability among grid environments. MIP can be customized in a straightforward way to a specific Grid environment or information schema. Current MIP implementation is based on Globus MDS4 and the XML version of GLUE Schema 1.2. The design of MIP aims to address the shortcomings that exist in current grid information providers (e. g., the generic information provider) as well as to support Web service-based GIS. Our modular approach has been developed to ease maintenance and management of grid information systems by automatically aggregating information sources and providing appropriate pieces of information to GIS based on predefined information schemas. Upon installation, MIP is configured to set up only necessary modules, which minimizes memory usage

This paper provides an overview of the Hour of Cyberinfrastructure (Hour of CI), a project creating a suite of self-paced, hour-long lessons aimed at helping learners in the areas of spatial, social, and environmental sciences take their first steps in the path toward cyberinfrastructure. Using collaboratively developed lessons written in Jupyter Notebooks, the Hour of CI aims to lower barriers to cyberinfrastructure for next-generation scientists and scholars from broad and diverse backgrounds. Early findings based on a pilot of four lessons suggest our approach has created engaging and appropriately challenging lessons for diverse learners. The project will continue developing lessons to help learners build cyber literacy for GIScience and prepare them to tackle global problems.