@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 {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 {15016,
	title = {Practical parallel algorithms for dynamic data redistribution, median finding, and selection},
	booktitle = {Parallel Processing Symposium, 1996., Proceedings of IPPS {\textquoteright}96, The 10th International},
	year = {1996},
	month = {1996/04//},
	pages = {292 - 301},
	abstract = {A common statistical problem is that of finding the median element in a set of data. This paper presents a fast and portable parallel algorithm for finding the median given a set of elements distributed across a parallel machine. In fact, our algorithm solves the general selection problem that requires the determination of the element of rank i, for an arbitrarily given integer i. Practical algorithms needed by our selection algorithm for the dynamic redistribution of data are also discussed. Our general framework is a distributed memory programming model enhanced by a set of communication primitives. We use efficient techniques for distributing, coalescing, and load balancing data as well as efficient combinations of task and data parallelism. The algorithms have been coded in SPLIT-C and run on a variety of platforms, including the Thinking Machines CM-5, IBM SP-1 and SP-2, Gray Research T3D, Meiko Scientific CS-2, Intel Paragon, and workstation clusters. Our experimental results illustrate the scalability and efficiency of our algorithms across different platforms and improve upon all the related experimental results known to the authors},
	keywords = {algorithms;performance, algorithms;scalability;statistical, allocation;, balancing, clusters;distributed, CM-5;communication, CS-2;SPLIT-C;Thinking, data, data;median, evaluation;resource, finding;parallel, Gray, Machines, memory, model;dynamic, Paragon;Meiko, primitives;distributed, problem;workstation, Programming, redistribution;load, research, scientific, SP-1;Intel, systems;parallel, T3D;IBM},
	doi = {10.1109/IPPS.1996.508072},
	author = {Bader, D.A. and JaJa, Joseph F.}
}
