@conference {19285,
	title = {Scalable representation of dataflow graph structures using topological patterns},
	booktitle = {2010 IEEE Workshop on Signal Processing Systems (SIPS)},
	year = {2010},
	month = {2010},
	pages = {13 - 18},
	abstract = {Tools for designing signal processing systems with their semantic foundation in dataflow modeling often use high-level graphical user interface (GUI) or text based languages that allow specifying applications as directed graphs. Such graphical representations serve as an initial reference point for further analysis and optimizations that lead to platform-specific implementations. For large-scale applications, the underlying graphs often consist of smaller substructures that repeat multiple times. To enable more concise representation and direct analysis of such substructures in the context of high level DSP specification languages and design tools, we develop the modeling concept of topological patterns, and propose ways for supporting this concept in a high-level language. We augment the DIF language - a language for specifying DSP-oriented dataflow graphs - with constructs for supporting topological patterns, and we show how topological patterns can be effective in various aspects of embedded signal processing design flows using specific application examples.},
	keywords = {arrays, data flow graphs, Dataflow graphs, DIF language, Digital signal processing, directed graphs, DSP specification languages, embedded signal processing design flows, embedded systems, Field programmable gate arrays, graphical user interface, Graphical user interfaces, High-level languages, modelbased design, optimisation, optimizations, scalable dataflow graph structures representation, semantics, Signal processing, Signal processing systems, Specification languages, text based languages, Topological patterns, Topology, Transform coding},
	author = {Sane, N. and Kee, Hojin and Seetharaman, G. and Bhattacharyya, Shuvra S.}
}
@conference {11902,
	title = {Approximate kernel matrix computation on GPUs forlarge scale learning applications},
	booktitle = {Proceedings of the 23rd international conference on Supercomputing},
	series = {ICS {\textquoteright}09},
	year = {2009},
	month = {2009///},
	pages = {511 - 512},
	publisher = {ACM},
	organization = {ACM},
	address = {Yorktown Heights, NY, USA},
	abstract = {Kernel-based learning methods require quadratic space and time complexities to compute the kernel matrix. These complexities limit the applicability of kernel methods to large scale problems with millions of data points. In this paper, we introduce a novel representation of kernel matrices on Graphics Processing Units (GPU). The novel representation exploits the sparseness of the kernel matrix to address the space complexity problem. It also respects the guidelines for memory access on GPUs, which are critical for good performance, to address the time complexity problem. Our representation utilizes the locality preserving properties of space filling curves to obtain a band approximation of the kernel matrix. To prove the validity of the representation, we use Affinity Propagation, an unsupervised clustering algorithm, as an example of kernel methods. Experimental results show a 40x speedup of AP using our representation without degradation in clustering performance.},
	keywords = {affinity propagation, algorithms, arrays, gpu, kernel methods, parallel programming, performance, space filling curves, sparse matrices},
	isbn = {978-1-60558-498-0},
	doi = {10.1145/1542275.1542355},
	author = {Hussein,Mohamed E and Abd-Almageed, Wael}
}
@conference {16838,
	title = {A Fast Similarity Join Algorithm Using Graphics Processing Units},
	booktitle = {Data Engineering, 2008. ICDE 2008. IEEE 24th International Conference on},
	year = {2008},
	month = {2008/04//},
	pages = {1111 - 1120},
	abstract = {A similarity join operation A BOWTIE<sub>epsiv</sub> B takes two sets of points A, B and a value epsiv isin Ropf, and outputs pairs of points p isin A,q isin B, such that the distance D(p, q) les epsiv. Similarity joins find use in a variety of fields, such as clustering, text mining, and multimedia databases. A novel similarity join algorithm called LSS is presented that executes on a graphics processing unit (GPU), exploiting its parallelism and high data throughput. As GPUs only allow simple data operations such as the sorting and searching of arrays, LSS uses these two operations to cast a similarity join operation as a GPU sort-and-search problem. It first creates, on the fly, a set of space-filling curves on one of its input datasets, using a parallel GPU sort routine. Next, LSS processes each point p of the other dataset in parallel. For each p, it searches an interval of one of the space-filling curves guaranteed to contain all the pairs in which p participates. Using extensive theoretical and experimental analysis, LSS is shown to offer a good balance between time and work efficiency. Experimental results demonstrate that LSS is suitable for similarity joins in large high-dimensional datasets, and that it performs well when compared against two existing prominent similarity join methods.},
	keywords = {algorithm;graphics, algorithms;quadtrees;set, arrays, curves;parallel, join, problem;space-filling, PROCESSING, searching;fast, similarity, theory;sorting;, units;sort-and-search},
	doi = {10.1109/ICDE.2008.4497520},
	author = {Lieberman,M.D. and Sankaranarayanan,J. and Samet, Hanan}
}
@article {17259,
	title = {Interactively exploring hierarchical clustering results [gene identification]},
	journal = {Computer},
	volume = {35},
	year = {2002},
	month = {2002/07//},
	pages = {80 - 86},
	abstract = {To date, work in microarrays, sequenced genomes and bioinformatics has focused largely on algorithmic methods for processing and manipulating vast biological data sets. Future improvements will likely provide users with guidance in selecting the most appropriate algorithms and metrics for identifying meaningful clusters-interesting patterns in large data sets, such as groups of genes with similar profiles. Hierarchical clustering has been shown to be effective in microarray data analysis for identifying genes with similar profiles and thus possibly with similar functions. Users also need an efficient visualization tool, however, to facilitate pattern extraction from microarray data sets. The Hierarchical Clustering Explorer integrates four interactive features to provide information visualization techniques that allow users to control the processes and interact with the results. Thus, hybrid approaches that combine powerful algorithms with interactive visualization tools will join the strengths of fast processors with the detailed understanding of domain experts},
	keywords = {algorithmic methods, arrays, Bioinformatics, biological data sets, biology computing, Data analysis, data mining, data visualisation, Data visualization, DNA, Fluorescence, gene functions, gene identification, gene profiles, Genetics, Genomics, Hierarchical Clustering Explorer, hierarchical systems, interactive exploration, interactive information visualization tool, interactive systems, Large screen displays, meaningful cluster identification, metrics, microarray data analysis, pattern clustering, pattern extraction, Process control, Sensor arrays, sequenced genomes, Tiles},
	isbn = {0018-9162},
	doi = {10.1109/MC.2002.1016905},
	author = {Seo,Jinwook and Shneiderman, Ben}
}
