Deborah K. Gracio

The Changing Paradigm of Data-Intensive Computing

Through the development of new classes of software, algorithms, and hardware, data-intensive applications provide timely and meaningful analytical results in response to exponentially growing data complexity and associated analysis requirements.

New paradigms in problem solving environments for scientific computing

Computer and computational scientists at Pacific Northwest National Laboratory (PNNL) are studying and designing collaborative problem solving environments (CPSEs) for scientific computing in various domains. Where most scientific computing efforts focus at the level of the scientific codes, file systems, data archives, and networked computers, our analysis and design efforts are aimed at developing enabling technologies that are directly meaningful and relevant to domain scientist at the level of the practice and the science. We seek to characterize the nature of scientific problem solving and look for innovative ways to improve it. Moreover, we aim to glimpse beyond current systems and technical limitations to derive a design that expresses the scientists own perspective on research activities, processes, and resources. The product of our analysis and design work is a conceptual scientific CPSE prototype that specifies a complete simulation and modeling user environment and a suite of high-level problem solving tools.

The extensible computational chemistry environment: a problem solving environment for high performance theoretical chemistry

The Extensible Computational Chemistry Environment (Ecce) is a suite of distributed applications that are integrated as a comprehensive problem solving environment for computational chemistry. Ecce provides scientists with an easily used graphical user interface to the tasks of setting up complex molecular modeling calculations, distributed use of high performance computers, and scientific visualization and analysis. Ecces flexible, standards-based architecture is an extensible framework that represents a significant milestone in production systems, both in the field of computational chemistry and problem solving environment research. Its base problem solving architecture components and concepts are applicable to problem solving environments beyond the computational chemistry domain.

In the trenches with ObjectStore

This article discusses our experience with the ObjectStore object-oriented database management system from Object Design, Inc. We have been using ObjectStore since 1992 as the database management system at the core of two significant projects. This is a “war story” of sorts, and while we have been winning the war and succeeding with ObjectStore, the battles have not been easy.

Data-Intensive Computing: Architectures, Algorithms, and Applications

The world is awash with digital data from social networks, blogs, business, science, and engineering. Data-intensive computing facilitates understanding of complex problems that must process massive amounts of data. Through the development of new classes of software, algorithms, and hardware, data-intensive applications can provide timely and meaningful analytical results in response to exponentially growing data complexity and associated analysis requirements. This emerging area brings many challenges that are different from traditional high-performance computing. This reference for computing professionals and researchers describes the dimensions of the field, the key challenges, the state of the art, and the characteristics of likely approaches that future data-intensive problems will require. Chapters cover general principles and methods for designing such systems and for managing and analyzing the big data sets of today that live in the cloud, and describe example applications in bioinformatics and cybersecurity that illustrate these principles in practice.

Computational Chemistry in the Environmental Molecular Sciences Laboratory

There are numerous serious environmental issues facing the world. Many of these have anthropogenic sources and are due to the production of materials for the consumer or for national defense and to energy production and consumption, e.g. global warming. For example, four decades of nuclear weapons production at Department of Energy facilities across the United States has resulted in the interim storage of millions of gallons of highly radioactive mixed wastes in hundreds of underground tanks, extensive contamination of the soil and groundwater at thousands of sites and hundreds of buildings that must be decontaminated and decommissioned.1 The single most challenging environmental issue confronting the DOE and perhaps the United States, is the safe and cost-effective management of these wastes. Questions that must be addressed include “What is the physical and chemical form of the wastes in the tanks and in the ground?” ; “How can the radioactive wastes be safely processed?”; and “How can the processed waste be safely stored?”2–4 The answers to these questions are, in general, unknown and the scientific basis required to meet these complex technological issues is not available today.5 In order to address these issues, major new scientific advances are required. An important technique that can be used to solve such complex problems is computational science which often enables the replacement or curtailment of expensive experiments, especially experiments involving radioactive species. Computational science can be used to provide fundamental answers to the questions enumerated above, to provide the conceptual and numerical bridge for the extrapolation of experimental data available for the lanthanides to the actinides and to allow us to reliably extend the experimental data into other regions of parameter space.

The Evolving Leadership Path of Visual Analytics

Visual analytics has become internationally recognized as a growing research area, producing increasingly sophisticated analytic technologies. The pioneers of this multidisciplinary field blazed a leadership path, which continues to evolve as the field develops. Key leadership strategies were 1) recognizing the need for a different approach, 2) establishing the vision and concept, 3) enlisting mission-driven champions and resource providers, 4) establishing enabling structures and collaborations, and 5) developing and deploying visual analytics tools. Strategies for future growth include increasing domains and applications, improving integration within research communities, and broadening bases of support.

Applications in Data-Intensive Computing

Abstract The total quantity of digital information in the world is growing at an alarming rate. Scientists and engineers are contributing heavily to this data “tsunami” by gathering data using computing and instrumentation at incredible rates. As data volumes and complexity grow, it is increasingly arduous to extract valuable information from the data and derive knowledge from that data. Addressing these demands of ever-growing data volumes and complexity requires game-changing advances in software, hardware, and algorithms. Solution technologies also must scale to handle the increased data collection and processing rates and simultaneously accelerate timely and effective analysis results. This need for ever faster data processing and manipulation as well as algorithms that scale to high-volume data sets have given birth to a new paradigm or discipline known as “data-intensive computing.” In this chapter, we define data-intensive computing, identify the challenges of massive data, outline solutions for hardware, software, and analytics, and discuss a number of applications in the areas of biology, cyber security, and atmospheric research.

Enabling Systems Biology: A Scientific Problem-Solving Environment

Biologists today are striving to solve multidisciplinary, complex systems biology questions. To successfully address these questions, software tools must be created to allow scientists to capture data and information, to share this information, and to analyze the data as elements of a complete system. At Pacific Northwest National Laboratory, we are creating the Computational Cell Environment, a biology-centered collaborative problem-solving environment with the goal of providing data retrieval, management, and analysis through all aspects of biological study. A horizontal prototype called SysBioPSE, demonstrates this vision. Our initial work is centered on developing the Distributed Data Management and Analysis subsystem, which is a specific tool for retrieving data from multiple heterogeneous data stores, providing storage facilities that support pedigree tracking and data and information analysis under a common user interface. With time, many such individual subsystems will be developed and integrated to fulfill the Computational Cell Environment vision.

Conceptualizing a Collaborative Problem Solving Environment for Regional Climate Modeling and Assessment of Climate Impacts

Computational scientists at Pacific Northwest National Laboratory (PNNL) are designing a collaborative problem solving environment (CPSE) to support regional climate modeling and assessment of climate impacts. Where most climate computational science research and development projects focus at the level of the scientific codes, file systems, data archives, and networked computers, our analysis and design efforts are aimed at designing enabling technologies that are directly meaningful and relevant to climate researchers at the level of the practice and the science. We seek to characterize the nature of scientific problem solving and look for innovative ways to improve it. Moreover, we aim to glimpse beyond current systems and technical limitations to derive a design that expresses the regional climate or impact assessment modelers own perspective on research activities, processes, and resources. The product of our analysis and design work is a conceptual regional climate and impact assessment CPSE prototype that specifies a complete simulation and modeling user environment and a suite of high-level problem solving tools.