Browsing by Keywords "Graph partitioning"
Now showing items 1-15 of 15
-
Adaptive decomposition and remapping algorithms for object-space-parallel direct volume rendering of unstructured grids
(Academic Press, 2007-01)Object space (OS) parallelization of an efficient direct volume rendering algorithm for unstructured grids on distributed-memory architectures is investigated. The adaptive OS decomposition problem is modeled as a graph ... -
Cascade-aware partitioning of large graph databases
(Springer, 2019)Graph partitioning is an essential task for scalable data management and analysis. The current partitioning methods utilize the structure of the graph, and the query log if available. Some queries performed on the database ... -
An effective model to decompose linear programs for parallel solution
(Springer, 1996-08)Although 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 ... -
Exploiting locality in sparse matrix-matrix multiplication on many-core rchitectures
(IEEE Computer Society, 2017)Exploiting 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 ... -
Improving performance of sparse matrix dense matrix multiplication on large-scale parallel systems
(Elsevier BV, 2016)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 ... -
Latency-centric models and methods for scaling sparse operations
(Bilkent University, 2016-08)Parallelization of sparse kernels and operations on large-scale distributed memory systems remains as a major challenge due to ever-increasing scale of modern high performance computing systems and multiple con icting ... -
A novel partitioning method for accelerating the block cimmino algorithm
(Society for Industrial and Applied Mathematics Publications, 2018)We propose a novel block-row partitioning method in order to improve the convergence rate of the block Cimmino algorithm for solving general sparse linear systems of equations. The convergence rate of the block Cimmino ... -
Parallel direct and hybrid methods based on row block partitioning for solving sparse linear systems
(Bilkent University, 2017-08)Solving system of linear equations is a kernel operation in many scienti c and industrial applications. These applications usually give rise to linear systems in which the coe cient matrix is very large and sparse. The ... -
Partitioning models for scaling distributed graph computations
(Bilkent University, 2019-09)The focus of this thesis is intelligent partitioning models and methods for scaling the performance of parallel graph computations on distributed-memory systems. Distributed databases utilize graph partitioning to provide ... -
Partitioning models for scaling parallel sparse matrix-matrix multiplication
(Association for Computing Machinery, 2018)We investigate outer-product--parallel, inner-product--parallel, and row-by-row-product--parallel formulations of sparse matrix-matrix multiplication (SpGEMM) on distributed memory architectures. For each of these three ... -
Recursive bipartitioning models for performance improvement in sparse matrix computations
(Bilkent University, 2017-09)Sparse matrix computations are among the most important building blocks of linear algebra and arise in many scienti c and engineering problems. Depending on the problem type, these computations may be in the form of ... -
Reducing communication overhead in sparse matrix and tensor computations
(Bilkent University, 2020-08)Encapsulating multiple communication cost metrics, i.e., bandwidth and latency, is proven to be important in reducing communication overhead in the parallelization of sparse and irregular applications. Communication ... -
Scaling sparse matrix-matrix multiplication in the accumulo database
(Springer, 2020)We propose and implement a sparse matrix-matrix multiplication (SpGEMM) algorithm running on top of Accumulo’s iterator framework which enables high performance distributed parallelism. The proposed algorithm provides ... -
Site-based partitioning and repartitioning techniques for parallel pagerank computation
(Institute of Electrical and Electronics Engineers, 2011-05)The PageRank algorithm is an important component in effective web search. At the core of this algorithm are repeated sparse matrix-vector multiplications where the involved web matrices grow in parallel with the growth of ... -
Spatiotemporal graph and hypergraph partitioning models for sparse matrix-vector multiplication on many-core architectures
(IEEE Computer Society, 2019)There 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 ...
