Ümit V. Çatalyürek

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ÐIn this work, we show that the standard graph-partitioning-based decomposition of sparse matrices does not reflect the actual communication volume requirement for parallel matrix-vector multiplication. We propose two computational hypergraph models which avoid this crucial deficiency of the graph model. The proposed models reduce the decomposition problem(More)
Many emerging large-scale data science applications require searching large graphs distributed across multiple memories and processors. 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. Scalability was tested on IBM BlueGene/L with 32,768 nodes at the(More)
A k-core of a graph is a maximal connected subgraph in which every vertex is connected to at least k vertices in the subgraph. k-core decomposition is often used in large-scale network analysis, such as community detection, protein function prediction, visualization, and solving NP-Hard problems on real networks efficiently, like maximal clique finding. In(More)
We investigate the problem of permuting a sparse rectangular matrix into block-diagonal form. Block-diagonal form of a matrix grants an inherent parallelism for solving the deriving problem, as recently investigated in the context of mathematical programming, LU factorization, and QR factorization. To represent the nonzero structure of a matrix, we propose(More)
1. Introduction. Partitioning and load-balancing are important issues in parallel scientific computing. The goal is to distribute data (and work) evenly among processors such as to reduce communication cost and achieve maximal performance. Graph partitioning has long served as a useful model for load balancing in parallel computing. Data are represented as(More)
We are developing a computer-aided prognosis system for neuroblastoma (NB), a cancer of the nervous system and one of the most malignant tumors affecting children. Histopathological examination is an important stage for further treatment planning in routine clinical diagnosis of NB. According to the International Neuroblastoma Pathology Classification (the(More)
Analysis of data is an important step in understanding and solving a scientiic problem. Analysis involves extracting the data of interest from all the available raw data in a dataset and processing it into a data product. However, in many areas of science and engineering, a scientist's ability to analyze information is increasingly becoming hindered by(More)
In parallel adaptive applications, the computational structure of the applications changes over time, leading to load imbalances even though the initial load distributions were balanced. To restore balance and to keep communication volume low in further iterations of the applications, dynamic load balancing (repartitioning) of the changed computational(More)
We propose a new duplication-based DAG scheduling algorithm for heterogeneous computing environments. Contrary to the traditional approaches, proposed algorithm traverses the DAG in a bottom-up fashion while taking advantage of task duplication and task insertion. Experimental results on random DAGs and three different application DAGs show that the(More)