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Ümit V. Çatalyürek
Researcher at Georgia Institute of Technology
Publications - 311
Citations - 9420
Ümit V. Çatalyürek is an academic researcher from Georgia Institute of Technology. The author has contributed to research in topics: Scheduling (computing) & Graph (abstract data type). The author has an hindex of 48, co-authored 302 publications receiving 8624 citations. Previous affiliations of Ümit V. Çatalyürek include Bilkent University & University of Maryland, College Park.
Papers
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Journal ArticleDOI
Hypergraph-partitioning-based decomposition for parallel sparse-matrix vector multiplication
TL;DR: It is shown that the standard graph-partitioning-based decomposition of sparse matrices does not reflect the actual communication volume requirement for parallel matrix-vector multiplication, and two computational hypergraph models are proposed which avoid this crucial deficiency of the graph model.
Proceedings ArticleDOI
Parallel hypergraph partitioning for scientific computing
TL;DR: This work presents a parallel software package for hypergraph (and sparse matrix) partitioning developed at Sandia National Labs, and presents empirical results that show the parallel implementation achieves good speedup on several large problems.
Proceedings ArticleDOI
A Scalable Distributed Parallel Breadth-First Search Algorithm on BlueGene/L
Andy Yoo,Edmond Chow,Keith Henderson,William Clarence McLendon,Bruce Hendrickson,Ümit V. Çatalyürek +5 more
TL;DR: This paper presents a distributed breadth- first search (BFS) scheme that scales for random graphs with up to three billion vertices and 30 billion edges, and develops efficient collective communication functions for the 3D torus architecture of BlueGene/L that take advantage of the structure in the problem.
Journal ArticleDOI
A comparative analysis of biclustering algorithms for gene expression data
TL;DR: The analyses show that the biclustering method and its parameters should be selected based on the desired model, whether that model allows overlapping biclusters, and its robustness to noise, and these algorithms are observed to be more successful at capturing biologically relevant clusters.
Journal ArticleDOI
Benchmarking short sequence mapping tools
TL;DR: A benchmarking suite to extensively analyze sequencing tools with respect to various aspects and provide an objective comparison is provided that reveals and evaluates the different factors affecting the mapping process.