@article {12158,
	title = {The ASC-Alliance Projects: A Case Study of Large-Scale Parallel Scientific Code Development},
	journal = {Computer},
	volume = {41},
	year = {2008},
	month = {2008/03//},
	pages = {50 - 58},
	abstract = {Computational scientists face many challenges when developing software that runs on large-scale parallel machines. However, software-engineering researchers haven{\textquoteright}t studied their software development processes in much detail. To better understand the nature of software development in this context, the authors examined five large-scale computational science software projects operated at the five ASC-Alliance centers.},
	keywords = {ASC-Alliance, code, development;large-scale, development;parallel, engineering;, machines;software, Parallel, projects;computational, scale, Science, scientific, scientists;large, software, systems;parallel},
	isbn = {0018-9162},
	doi = {10.1109/MC.2008.101},
	author = {Hochstein, L. and Basili, Victor R.}
}
@conference {12541,
	title = {A Scalable Projective Bundle Adjustment Algorithm using the L infinity Norm},
	booktitle = {Computer Vision, Graphics Image Processing, 2008. ICVGIP {\textquoteright}08. Sixth Indian Conference on},
	year = {2008},
	month = {2008/12//},
	pages = {79 - 86},
	abstract = {The traditional bundle adjustment algorithm for structure from motion problem has a computational complexity of O((m+n)<sup>3</sup>) per iteration and memory requirement of O(mn(m+n)), where m is the number of cameras and n is the number of structure points. The sparse version of bundle adjustment has a computational complexity of O(m<sup>3</sup>+mn) per iteration and memory requirement of O(mn). Here we propose an algorithm that has a computational complexity of O(mn(radicm+radicn)) per iteration and memory requirement of O(max(m,n)). The proposed algorithm is based on minimizing the L<sub>infin</sub> norm of reprojection error. It alternately estimates the camera and structure parameters, thus reducing the potentially large scale optimization problem to many small scale subproblems each of which is a quasi-convex optimization problem and hence can be solved globally. Experiments using synthetic and real data show that the proposed algorithm gives good performance in terms of minimizing the reprojection error and also has a good convergence rate.},
	keywords = {adjustment, algorithm;structure, bundle, complexity;convex, complexity;iteration, error;scalable, estimation;, estimation;cameras;computational, L<sub>infin</sub>, method;large, methods;parameter, norm;camera;computational, OPTIMIZATION, parameter, problem;memory, problem;projection, problem;reprojection, programming;image, projective, reconstruction;iterative, reconstruction;quasiconvex, requirement;motion, scale},
	doi = {10.1109/ICVGIP.2008.51},
	author = {Mitra, K. and Chellapa, Rama}
}
@conference {15029,
	title = {Strategies for exploring large scale data},
	booktitle = {Parallel Architectures, Algorithms and Networks, 2004. Proceedings. 7th International Symposium on},
	year = {2004},
	month = {2004/05//},
	pages = {2 - 2},
	abstract = {We consider the problem of querying large scale multidimensional time series data to discover events of interest, test and validate hypotheses, or to associate temporal patterns with specific events. This type of data currently dominates most other types of available data, and will very likely become even more prevalent in the future given the current trends in collecting time series of business, scientific, demographic, and simulation data. The ability to explore such collections interactively, even at a coarse level, will be critical in discovering the information and knowledge embedded in such collections. We develop indexing techniques and search algorithms to efficiently handle temporal range value querying of multidimensional time series data. Our indexing uses linear space data structures that enable the handling of queries in I/O time that is essentially the same as that of handling a single time slice, assuming the availability of a logarithmic number of processors as a function of the temporal window. A data structure with provably almost optimal asymptotic bounds is also presented for the case when the number of multidimensional objects is relatively small. These techniques improve significantly over standard techniques for either serial or parallel processing, and are evaluated by extensive experimental results that confirm their superior performance.},
	keywords = {algorithms;, association;, asymptotic, bounds;, business, data, data;, database, databases;, demographic, discovery;, Indexing, indexing;, information, knowledge, large, linear, mining;, multidimensional, objects;, optimal, Parallel, pattern, processing;, query, querying;, range, scale, scientific, search, serial, series, series;, simulation, space, structure;, structures;, techniques;, temporal, TIME, value, very, window;},
	doi = {10.1109/ISPAN.2004.1300447},
	author = {JaJa, Joseph F.}
}
@conference {14961,
	title = {Efficient techniques for range search queries on earth science data},
	booktitle = {Scientific and Statistical Database Management, 2002. Proceedings. 14th International Conference on},
	year = {2002},
	month = {2002///},
	pages = {142 - 151},
	abstract = {We consider the problem of organizing large scale earth science raster data to efficiently handle queries for identifying regions whose parameters fall within certain range values specified by the queries. This problem seems to be critical to enabling basic data mining tasks such as determining associations between physical phenomena and spatial factors, detecting changes and trends, and content based retrieval. We assume that the input is too large to fit in internal memory and hence focus on data structures and algorithms that minimize the I/O bounds. A new data structure, called a tree-of-regions (ToR), is introduced and involves a combination of an R-tree and efficient representation of regions. It is shown that such a data structure enables the handling of range queries in an optimal I/O time, under certain reasonable assumptions. We also show that updates to the ToR can be handled efficiently. Experimental results for a variety of multi-valued earth science data illustrate the fast execution times of a wide range of queries, as predicted by our theoretical analysis.},
	keywords = {based, computing;, content, data, data;, databases;, Earth, factors;, large, mining, mining;, natural, processing;, queries;, query, range, raster, retrieval;, scale, Science, sciences, search, spatial, structures;, tasks;, temporal, tree, tree-of-regions;, visual},
	doi = {10.1109/SSDM.2002.1029714},
	author = {Shi,Qingmin and JaJa, Joseph F.}
}
@conference {15018,
	title = {Prefix computations on symmetric multiprocessors},
	booktitle = {Parallel and Distributed Processing, 1999. 13th International and 10th Symposium on Parallel and Distributed Processing, 1999. 1999 IPPS/SPDP. Proceedings},
	year = {1999},
	month = {1999/04//},
	pages = {7 - 13},
	abstract = {We introduce a new optimal prefix computation algorithm on linked lists which builds upon the sparse ruling set approach of Reid-Miller and Blelloch. Besides being somewhat simpler and requiring nearly half the number of memory accesses, we can bound our complexity with high probability instead of merely on average. Moreover, whereas Reid-Miller and Blelloch (1996) targeted their algorithm for implementation on a vector multiprocessor architecture, we develop our algorithm for implementation on the symmetric multiprocessor architecture (SMP). These symmetric multiprocessors dominate the high-end server market and are currently the primary candidate for constructing large scale multiprocessor systems. Our prefix computation algorithm was implemented in C using POSIX threads and run on four symmetric multiprocessors-the IBM SP-2 (High Node), the HP-Convex Exemplar (S-Class), the DEC AlphaServer; and the Silicon Graphics Power Challenge. We ran our code using a variety of benchmarks which we identified to examine the dependence of our algorithm on memory access patterns. For some problems, our algorithm actually matched or exceeded the performance of the standard sequential solution using only a single thread. Moreover, in spite of the fact that the processors must compete for access to main memory, our algorithm still achieved scalable performance with up to 16 processors, which was the largest platform available to us},
	keywords = {access, algorithms;, AlphaServer;HP-Convex, approach;symmetric, architecture;symmetric, Challenge;high-end, computations;scalable, DEC, Exemplar;IBM, Graphics, market;large, multiprocessor, multiprocessors;Unix;distributed, patterns;prefix, performance;sparse, power, ruling, scale, server, set, SP-2;POSIX, systems;memory, threads;Silicon},
	doi = {10.1109/IPPS.1999.760427},
	author = {Helman,D.R. and JaJa, Joseph F.}
}
