Browsing by Subject "Graph Partitioning"

Now showing 1 - 3 of 3

Open Access
Active set partitioning scheme for extending the lifetime of large wireless sensor networks
(2010) Kalkan, Mustafa
Wireless Sensor Networks consist of spatially distributed and energy-constrained autonomous devices called sensors to cooperatively monitor physical or environmental conditions such as temperature, sound, vibration, pressure or pollutants at different locations. Because these sensor nodes have limited energy supply, energy efficiency is a critical design issue in wireless sensor networks. Having all the nodes simultaneously work in the active mode, results in an excessive energy consumption and packet collisions because of high node density in the network. In order to minimize energy consumption and extend network life-time, this thesis presents a centralized graph partitioning approach to organize the sensor nodes into a number of active sensor node sets such that each active set maintains the desired level of sensing coverage and forms a connected network to perform sensing and communication tasks successfully. We evaluate our proposed scheme via simulations under different network topologies and parameters in terms of network lifetime and run-time efficiency and observe approximately 50% improvement in the number of obtained active node sets when compared with different active node set selection mechanisms.
Open Access
Image-space decomposition algorithms for sort-first parallel volume rendering of unstructured grids
(Springer, 2000) Kutluca, H.; Kurç, T. M.; Aykanat, Cevdet
Twelve adaptive image-space decomposition algorithms are presented for sort-first parallel direct volume rendering (DVR) of unstructured grids on distributed-memory architectures. The algorithms are presented under a novel taxonomy based on the dimension of the screen decomposition, the dimension of the workload arrays used in the decomposition, and the scheme used for workload-array creation and querying the workload of a region. For the 2D decomposition schemes using 2D workload arrays, a novel scheme is proposed to query the exact number of screen-space bounding boxes of the primitives in a screen region in constant time. A probe-based chains-on-chains partitioning algorithm is exploited for load balancing in optimal 1D decomposition and iterative 2D rectilinear decomposition (RD). A new probe-based optimal 2D jagged decomposition (OJD) is proposed which is much faster than the dynamic-programming based OJD scheme proposed in the literature. The summed-area table is successfully exploited to query the workload of a rectangular region in constant time in both OJD and RD schemes for the subdivision of general 2D workload arrays. Two orthogonal recursive bisection (ORB) variants are adapted to relax the straight-line division restriction in conventional ORB through using the medians-of-medians approach on regular mesh and quadtree superimposed on the screen. Two approaches based on the Hilbert space-filling curve and graph-partitioning are also proposed. An efficient primitive classification scheme is proposed for redistribution in 1D, and 2D rectilinear and jagged decompositions. The performance comparison of the decomposition algorithms is modeled by establishing appropriate quality measures for load-balancing, amount of primitive replication and parallel execution time. The experimental results on a Parsytec CC system using a set of benchmark volumetric datasets verify the validity of the proposed performance models. The performance evaluation of the decomposition algorithms is also carried out through the sort-first parallelization of an efficient DVR algorithm.
Open Access
Improving performance of sparse matrix dense matrix multiplication on large-scale parallel systems
(Elsevier BV, 2016) Acer, S.; Selvitopi, O.; Aykanat, Cevdet
We propose a comprehensive and generic framework to minimize multiple and different volume-based communication cost metrics for sparse matrix dense matrix multiplication (SpMM). SpMM is an important kernel that finds application in computational linear algebra and big data analytics. On distributed memory systems, this kernel is usually characterized with its high communication volume requirements. Our approach targets irregularly sparse matrices and is based on both graph and hypergraph partitioning models that rely on the widely adopted recursive bipartitioning paradigm. The proposed models are lightweight, portable (can be realized using any graph and hypergraph partitioning tool) and can simultaneously optimize different cost metrics besides total volume, such as maximum send/receive volume, maximum sum of send and receive volumes, etc., in a single partitioning phase. They allow one to define and optimize as many custom volume-based metrics as desired through a flexible formulation. The experiments on a wide range of about thousand matrices show that the proposed models drastically reduce the maximum communication volume compared to the standard partitioning models that only address the minimization of total volume. The improvements obtained on volume-based partition quality metrics using our models are validated with parallel SpMM as well as parallel multi-source BFS experiments on two large-scale systems. For parallel SpMM, compared to the standard partitioning models, our graph and hypergraph partitioning models respectively achieve reductions of 14% and 22% in runtime, on average. Compared to the state-of-the-art partitioner UMPa, our graph model is overall 14.5 ï¿½ faster and achieves an average improvement of 19% in the partition quality on instances that are bounded by maximum volume. For parallel BFS, we show on graphs with more than a billion edges that the scalability can significantly be improved with our models compared to a recently proposed two-dimensional partitioning model.