Browsing by Keywords "Sparse matrices"
Now showing items 1-20 of 25
-
Approximate MLFMA as an efficient preconditioner
(IEEE, 2007)In this work, we propose a preconditioner that approximates the dense system operator. For this purpose, we develop an approximate multilevel fast multipole algorithm (AMLFMA), which performs a much faster matrix-vector ... -
Cache locality exploiting methods and models for sparse matrix-vector multiplication
(Bilkent University, 2009)The sparse matrix-vector multiplication (SpMxV) is an important kernel operation widely used in linear solvers. The same sparse matrix is multiplied by a dense vector repeatedly in these solvers to solve a system of ... -
An effective preconditioner based on schur complement reduction for integral-equation formulations of dielectric problems
(IEEE, 2009)The author consider effective preconditioning of recently proposed two integral-equation formulations for dielectrics; the combined tangential formulation (CTF) and the electric and magnetic current combined-field integral ... -
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 ... -
Hypergraph models for parallel sparse matrix-matrix multiplication
(Bilkent University, 2015-09)Multiplication of two sparse matrices (i.e., sparse matrix-matrix multiplication, which is abbreviated as SpGEMM) is a widely used kernel in many applications such as molecular dynamics simulations, graph operations, and ... -
A hypergraph partitioning model for profile minimization
(Society for Industrial and Applied Mathematics Publications, 2019)In this paper, the aim is to symmetrically permute the rows and columns of a given sparse symmetric matrix so that the profile of the permuted matrix is minimized. We formulate this permutation problem by first defining ... -
Hypergraph-partitioning-based decomposition for parallel sparse-matrix vector multiplication
(IEEE, 1999)In 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 ... -
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, 2016-07)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 ... -
Locality-aware parallel sparse matrix-vector and matrix-transpose-vector multiplication on many-core processors
(Institute of Electrical and Electronics Engineers, 2016)Sparse matrix-vector and matrix-transpose-vector multiplication (SpMMTV) repeatedly performed as z ← ATx and y ← A z (or y ← A w) for the same sparse matrix A is a kernel operation widely used in various iterative solvers. ... -
Minimizing communication latencies in conjugate gradient type parallel iterative solvers
(Bilkent University, 2001-07) -
ON two-dimensional sparse matrix partitioning: models, methods, and a recipe
(Society for Industrial and Applied Mathematics, 2010)We consider two-dimensional partitioning of general sparse matrices for parallel sparse matrix-vector multiply operation. We present three hypergraph-partitioning-based methods, each having unique advantages. The first one ... -
Parallel sparse matrix-vector multiplies and iterative solvers
(Bilkent University, 2005)Sparse matrix-vector multiply (SpMxV) operations are in the kernel of many scientific computing applications. Therefore, efficient parallelization of SpMxV operations is of prime importance to scientific computing ... -
Parallelization of Sparse Matrix Kernels for big data applications
(Springer, 2016)Analysis of big data on large-scale distributed systems often necessitates efficient parallel graph algorithms that are used to explore the relationships between individual components. Graph algorithms use the basic adjacency ... -
Partitioning hypergraphs in scientific computing applications through vertex separators on graphs
(Society for Industrial and Applied Mathematics, 2012)The modeling flexibility provided by hypergraphs has drawn a lot of interest from the combinatorial scientific community, leading to novel models and algorithms, their applications, and development of associated tools. ... -
Partitioning models for scaling distributed graph computations
(Bilkent University, 2019-08)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 ... -
Preconditioning large integral-equation problems involving complex targets
(IEEE, 2008-07)When the target problem is small in terms of the wavelength, simple preconditioners, such as BDP, may sufficiently accelerate the convergence. On the other hand, for large-scale problems, the matrix equations become much ... -
Recursive bipartitioning models for performance improvement in sparse matrix computations
(Bilkent University, 2017-08)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 volume overhead in large-scale parallel SpGEMM
(Bilkent University, 2016-12)Sparse matrix-matrix multiplication of the form of C = A x B, C = A x A and C = A x AT is a key operation in various domains and is characterized with high complexity and runtime overhead. There exist models for parallelizing ... -
Reducing latency cost in 2D sparse matrix partitioning models
(Elsevier BV, 2016)Sparse matrix partitioning is a common technique used for improving performance of parallel linear iterative solvers. Compared to solvers used for symmetric linear systems, solvers for nonsymmetric systems offer more ...
