Browsing by Subject "Hypergraph model"
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Item Open Access An effective model to decompose linear programs for parallel solution(Springer, 1996-08) Pınar, Ali; Aykanat, CevdetAlthough inherent parallelism in the solution of block angulax Linear Programming (LP) problems has been exploited in many research works, the literature that addresses decomposing constraint matrices into block angular form for parallel solution is very rare and recent. We have previously proposed hypergraph models, which reduced the problem to the hypergraph partitioning problem. However, the quality of the results reported were limited due to the hypergraph partitioning tools we have used. Very recently, multilevel graph partitioning heuristics have been proposed leading to very successful graph partitioning tools; Chaco and Metis. In this paper, we propose an effective graph model to decompose matrices into block angular form, which reduces the problem to the well-known graph partitioning by vertex separator problem. We have experimented the validity of our proposed model with various LP problems selected from NETLIB and other sources. The results are very attractive both in terms of solution quality and running times. © Springer-Verlag Berlin Heidelberg 1996.Item Open Access Exploiting locality in sparse matrix-matrix multiplication on many-core rchitectures(IEEE Computer Society, 2017) Akbudak K.; Aykanat, CevdetExploiting spatial and temporal localities is investigated for efficient row-by-row parallelization of general sparse matrix-matrix multiplication (SpGEMM) operation of the form C=A,B on many-core architectures. Hypergraph and bipartite graph models are proposed for 1D rowwise partitioning of matrix A to evenly partition the work across threads with the objective of reducing the number of B-matrix words to be transferred from the memory and between different caches. A hypergraph model is proposed for B-matrix column reordering to exploit spatial locality in accessing entries of thread-private temporary arrays, which are used to accumulate results for C-matrix rows. A similarity graph model is proposed for B-matrix row reordering to increase temporal reuse of these accumulation array entries. The proposed models and methods are tested on a wide range of sparse matrices from real applications and the experiments were carried on a 60-core Intel Xeon Phi processor, as well as a two-socket Xeon processor. Results show the validity of the models and methods proposed for enhancing the locality in parallel SpGEMM operations. © 1990-2012 IEEE.Item Open Access Hypergraph-theoretic partitioning models for parallel web crawling(Springer, London, 2012) Türk, Ata; Cambazoğlu, B. Barla; Aykanat, CevdetParallel web crawling is an important technique employed by large-scale search engines for content acquisition. A commonly used inter-processor coordination scheme in parallel crawling systems is the link exchange scheme, where discovered links are communicated between processors. This scheme can attain the coverage and quality level of a serial crawler while avoiding redundant crawling of pages by different processors. The main problem in the exchange scheme is the high inter-processor communication overhead. In this work, we propose a hypergraph model that reduces the communication overhead associated with link exchange operations in parallel web crawling systems by intelligent assignment of sites to processors. Our hypergraph model can correctly capture and minimize the number of network messages exchanged between crawlers. We evaluate the performance of our models on four benchmark datasets. Compared to the traditional hash-based assignment approach, significant performance improvements are observed in reducing the inter-processor communication overhead. © 2012 Springer-Verlag London Limited.Item Open Access Reordering methods for exploiting spatial and temporal localities in parallel sparse matrix-vector multiplication(Bilkent University, 2016-08) AbuBaker, NabilSparse Matrix-Vector multiplication (SpMV) is a very important kernel operation for many scientific applications. For irregular sparse matrices, the SpMV operation suffers from poor cache performance due to the irregular accesses of the input vector entries. In this work, we propose row and column reordering methods based on Graph partitioning (GP) and Hypergraph partitioning (HP) in order to exploit spatial and temporal localities in accessing input vector entries by clustering rows/columns with a similar sparsity pattern close to each other. The proposed methods exploit spatial and temporal localities separately (using either rows or columns of the matrix in a GP or HP method), simultaneously (using both rows and column) and in a two-phased manner(using either rows or columns in each phase). We evaluate the validity of the proposed models on a 60- core Xeon Phi co-processor for a large set of sparse matrices arising from different applications. The performance results confirm the validity and the effectiveness of the proposed methods and models.Item Open Access Spatiotemporal graph and hypergraph partitioning models for sparse matrix-vector multiplication on many-core architectures(IEEE Computer Society, 2019) Abubaker, Nabil; Akbudak, K.; Aykanat, CevdetThere exist graph/hypergraph partitioning-based row/column reordering methods for encoding either spatial or temporal locality for sparse matrix-vector multiplication (SpMV) operations. Spatial and temporal hypergraph models in these methods are extended to encapsulate both spatial and temporal localities based on cut/uncut net categorization obtained from vertex partitioning. These extensions of spatial and temporal hypergraph models encode the spatial locality primarily and the temporal locality secondarily, and vice-versa, respectively. However, the literature lacks models that simultaneously encode both spatial and temporal localities utilizing only vertex partitioning for further improving the performance of SpMV on shared-memory architectures. In order to fill this gap, we propose a novel spatiotemporal hypergraph model that leads to a one-phase spatiotemporal reordering method which encodes both types of locality simultaneously. We also propose a framework for spatiotemporal methods which encodes both types of locality in two dependent phases and two separate phases. The validity of the proposed spatiotemporal models and methods are tested on a wide range of sparse matrices and the experiments are performed on both a 60-core Intel Xeon Phi processor and a Xeon processor. Results show the validity of the methods via almost doubling the Gflop/s performance through enhancing data locality in parallel SpMV operations.