FutureGrid--developing a Grid testbed for computer science research and domain science applications, (Sr. Rensselaer Supercomputers Battle Lyme Disease, Rensselaer Polytechnic Institute Review Vol. Intelligent Optimization of Parallel and Distributed Applicationsa€?National Science Foundation (NSF). As part of this work, I extended and improved metadata services, in particular the Metadata Catalog Service (MCS). This project focuses in part on bringing workflow technologies to earthquake science applications.
Towards Cognitive Grids : Knowledge-Rich Grid Services for Autonomous Workflow Refinement and Robust Execution. This research combines Artificial Intelligence and Distributed Computing techniques to create knowledge-rich workflow services that can support the execution of large-scale scientific workflows.
As part of this work, I worked with the Montage scientists to fully parallelize the application using the workflow paradigm and today Pegasus is used to generate science-grade mosaics of the sky.
In GriPhyN I lead the effort to develop the methodology and tools to support the a€?virtual dataa€? paradigm, where the scientist would ask for a particular data product and obtain the desired results.
Our team is composed of IT research groups and members of four NSF-funded frontier physics experiments.
As part of iVDGL Pegasus was then transitioned into other NSF-funded projects and is regularly released and supported as part of iVDGLa€™s Virtual Data Toolkit (VDT). This project continues to advance efforts to enhance and accelerate the process of very large-field 3D laser-scanning light microscopy to increase the throughput of generating multi-resolution a€?visiblea€? cells for computational neuroscience. The Globus project is developing fundamental technologies needed to build computational grids. This project will extend and adapt POEMS technology from model-based analysis of adaptive parallel and distributed systems to model-based control of adaptive parallel and distributed systems. The goal of this research is to identify major performance bottlenecks in supercomputer system software. Parsec is a C-based simulation language, developed by the Parallel Computing Laboratory at UCLA, for sequential and parallel execution of discrete-event simulation models.


The main foundation will be provided by expressive formal representations of the application workflow and of the execution environment. Driving the project are unprecedented requirements for geographically dispersed extraction of complex scientific information from very large collections of measured data. Our integrated research effort provides the coordination and tight feedback from prototypes and tests that will enable both communities to meet their goals.
Communities of thousands of scientists, distributed globally and served by networks of varying bandwidths, need to extract small signals from enormous backgrounds via computationally demanding analyses of datasets that will grow from the 100 Terabyte to the 100 Petabyte scale over the next decade.
For this workflow process, key data, computation, time, and labor intensive tasks are being identified and solutions for increasing end-to-end performance, accelerating computation, enhancing visualization, and interfacing federated databases are being explored. Grids are persistent environments that enable software applications to integrate instruments, displays, computational and information resources that are managed by diverse organizations in widespread locations. This will be achieved by a coordinated measurement, modeling, simulation, architectural evaluation, and experimental alteration effort, taking a global view of the overall systems software environment. These representations will support resource selection that will enhance application performance, resource reservation based on anticipated workflow needs, workflow repair capabilities in case of failures or in case of new resources coming on line.
To meet these requirements, which arise initially from the four physics experiments involved in this project but will also be fundamental to science and commerce in the 21st century, GriPhyN will deploy computational environments called Petascale Virtual Data Grids (PVDGs) that meet the data-intensive computational needs of a diverse community of thousands of scientists spread across the globe.
The four physics experiments are about to enter a new era of exploration of the fundamental forces of nature and the structure of the universe. The computing and storage resources required will be distributed, for both technical and strategic reasons, across national centers, regional centers, university computing centers, and individual desktops.
This feasibility demonstration will bring to the surface further research problems and technology requirements for model-based control.
The scale of this task, far outpaces our current ability to manage and process data in a distributed environment. It will explore the conditions under which basing control on more comprehensive system models will have benefit.
The GriPhyN collaboration proposes to carry out the necessary computer science and validate the concepts through a series of staged deployments, ultimately resulting in a set of production Data Grids. Model-based control can address issues of variability of resource availability as well as issues of variability in resource requirements engendered by adaptive algorithms.


The structure of a workflow specifies what analysis routines need to be executed, the data flow amongst them, and relevant execution details.
Variability of resource availability as well as variability of resource demand will be studied in this project. The application components will be viewed as dynamically adaptive algorithms for which there exist a set of variants and parameters that can be chosen to develop an optimized implementation. Workflows provide a systematic way to capture scientific methodology and provide provenance information for their results. A variant describes a distinct implementation of a code segment, perhaps even a different algorithm. Robust and flexible workflow creation, mapping, and execution are largely open research problems.
By encoding an application in this way, we can capture a large set of possible application mappings with a very compact representation. The aim of this workshop was to bring together IT researchers and practitioners working on a variety of aspects of workflow management as well as domain scientists that use workflows for day-to-day data analysis and simulation. Because the space of mappings is prohibitively large, the system captures and utilizes domain knowledge from the domain scientists and designers of the compiler, run-time and performance models to prune most of the possible implementation.
The National Science Foundation expects a final report with recommendations to the community regarding the challenges of scientific workflows and their role in cyber infrastructure planning for 21st century science and engineering research and education. Knowledge representation and machine learning techniques utilize this domain knowledge and past experience to navigate the search space efficiently. Incorporating cognitive search techniques and taking advantage of parallel resources, these alternative implementations are searched automatically by tools to find a high-quality implementation.



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