As Alvin Toffler had eloquently put it "You've got to think about big things while you're doing small things, so that all the small things go in the right direction." [6] We have a long history of building many innovative solutions. With a quick search on the web, we can find various tools that offer similar capabilities such as search, visualization, subsetting, analysis, etc. The community is very good with building domain-specific solutions for specific applications. The lack of cohesiveness among these tools introduces technology gaps, which lead to even more stovepipe solutions. An Analytics Center Framework is an architectural concept to establish an extensible, reusable software framework for specific research communities. This paper discusses the application of an open source data analytics framework NASA is developing through its Advanced Information Systems Technology (AIST) program to improve estimating ocean circulation modeling product generation and analysis.

We describe the data processing and analysis algorithms used for high-precision retrievals of CO2weighted-column mixing ratio and 2-μm surface reflectance from the Carbon Dioxide Laser Absorption Spectrometer (CO2LAS). The CO2LAS at the Jet Propulsion Laboratory, Pasadena, CA, USA, is one of the instruments designed to demonstrate capabilities needed for the NASA Active Sensing over Nights, Days, and Seasons mission concept. The integrated path differential absorption technique is used in CO2retrieval. The along-track spatial resolution for the airborne measurements described here ranges from 10 m to 1 km. Our approach employs heterodyne detection of two laser signals reflected off earth's surface. Frequency domain processing enables return signal peak detection and high-fidelity power estimation. We compensate for range to ground variations using a digital elevation model and emphasize the importance of reflectance weighting in time averaging of the surface elevation. Quality control filters are applied, as well as a statistical methodology to filter laser speckle fluctuations. Reflectance is also retrieved on the scale of a few meters of ground track. Our data processing workflow and example retrievals are presented.

Abstract We describe how the Grid enables new research possibilities in astronomy through multi‐wavelength images. To see sky images in the same pixel space, they must be projected to that space, a computer‐intensive process. There is thus a virtual data space induced that is defined by an image and the applied projection. This virtual data can be created and replicated with Planners and Replica catalog technology developed under the GriPhyN project. We plan to deploy our system (MONTAGE) on the U.S. Teragrid. Grid computing is also needed for ingesting data—computing background correction on each image—which forms a separate virtual data space. Multi‐wavelength images can be used for pushing source detection and statistics by an order of magnitude from current techniques; for optimization of multi‐wavelength image registration for detection and characterization of extended sources; and for detection of new classes of essentially multi‐wavelength astronomical phenomena. The paper discusses both the Grid architecture and the scientific goals. Copyright © 2003 John Wiley & Sons, Ltd.

In this paper, we describe how our computational model can be used for the problems of processor allocation and task mapping. The intended applications for this model include the dynamic mapping problems of shrinking or spreading an existing mapping when the available pool of processors changes during execution of the problem. The concept of problem edge class and other features of our model are developed to realistically and efficiently support task partitioning and merging for static and dynamic mapping. The model dictates realistic changes in the computation and communication characteristics of a problem when the problem partitioning is modified dynamically. This model forms the basis of our algorithms for shrinking and spreading, and yields realistic results for a variety of problems mapped onto real systems. An emulation program running on a network of workstations under PVM is used to measure execution times for the mapping solutions found by the algorithms. The results indicate that the problem edge class is a crucial consideration for processor allocation and task mapping.

The early version of recursive refinement, an algorithm, for fast task mapping is reported in this paper. An intended application for this algorithm is dynamic load balancing on a parallel/distributed system, including a network of workstations. Since this requires a fast mapping, the algorithm restricts the range of tasks movement during optimization and maps groups of tasks to groups of PE's in each iteration. Tasks are allowed to be swapped only between the two partitions that comprise the parent partition from the previous iteration. More freedom of task movement is unnecessary because structural characteristics of the problem graph are used to guide the mapping. The groups are derived from a spanning tree that is constructed from the original problem graph and is used to identify the structural characteristics required for the algorithm. Experimental results show that the algorithm is able to achieve a mapping quality as good as that by another mapping scheme characteristic of a class of algorithms that require an order of magnitude more time.