Browsing by Keywords "Parallel computing"
Now showing items 1-20 of 22
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Coloring for distributed-memory-parallel Gauss-Seidel algorithm
(Bilkent University, 2019-09)Gauss-Seidel is a well-known iterative method for solving linear system of equations. The computations performed on Gauss-Seidel sweeps are sequential in nature since each component of new iterations depends on previously ... -
Domain specific language for deployment of parallel applications on parallel computing platforms
(Association for Computing Machinery, 2014-08)To increase the computing performance the current trend is towards applying parallel computing in which parallel tasks are executed on multiple nodes. The deployment of tasks on the computing platform usually impacts the ... -
Efficient overlapped FFT algorithms for hypercube-connected multicomputers
(1994)In this work, we propose parallel FFT algorithms, for medium-to-coarse grain hypercube-connected multicomputers, which are more elegant and efficient than the existing ones. The proposed algorithms achieve perfect load-balance ... -
Encapsulating multiple communication-cost metrics in partitioning sparse rectangular matrices for parallel matrix-vector multiplies
(SIAM, 2004)This paper addresses the problem of one-dimensional partitioning of structurally unsymmetric square and rectangular sparse matrices for parallel matrix-vector and matrix-transpose-vector multiplies. The objective is to ... -
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 ... -
Large-scale solutions of electromagnetics problems using the multilevel fast multipole algorithm and physical optics
(Bilkent University, 2015-04)Integral equations provide full-wave (accurate) solutions of Helmholtz-type electromagnetics problems. The multilevel fast multipole algorithm (MLFMA) discretizes the equations and solves them numerically with O(NLogN) ... -
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 ... -
Matrix factorization with stochastic gradient descent for recommender systems
(Bilkent University, 2019-02)Matrix factorization is an efficient technique used for disclosing latent features of real-world data. It finds its application in areas such as text mining, image analysis, social network and more recently and popularly ... -
Model-driven transformations for mapping parallel algorithms on parallel computing platforms
(MDHPCL, 2013)One of the important problems in parallel computing is the mapping of the parallel algorithm to the parallel computing platform. Hereby, for each parallel node the corresponding code for the parallel nodes must be implemented. ... -
One-dimensional partitioning for heterogeneous systems: theory and practice
(Academic Press, 2008-11)We study the problem of one-dimensional partitioning of nonuniform workload arrays, with optimal load balancing for heterogeneous systems. We look at two cases: chain-on-chain partitioning, where the order of the processors ... -
A parallel boundary element formulation for tracking multiple particle trajectories in Stoke's flow for microfluidic applications
(Tech Science Press, 2015)A new formulation for tracking multiple particles in slow viscous flow for microfluidic applications is presented. The method employs the manipulation of the boundary element matrices so that finally a system of equations ... -
Parallel image restoration using surrogate constraint methods
(Academic Press, 2007)When formulated as a system of linear inequalities, the image restoration problem yields huge, unstructured, sparse matrices even for images of small size. To solve the image restoration problem, we use the surrogate ... -
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 ... -
Parpatoh : A 2D-parallel hypergraph partitioning tool
(Bilkent University, 2006)Hypergraph partitioning is a process that is being used to find solutions for optimization problems in various areas, including parallel volume rendering, parallel information retrieval and VLSI circuit design. While the ... -
Partitioning sparse matrices for parallel preconditioned iterative methods
(Society for Industrial and Applied Mathematics, 2007)This paper addresses the parallelization of the preconditioned iterative methods that use explicit preconditioners such as approximate inverses. Parallelizing a full step of these methods requires the coefficient and ... -
Progressive refinement radiosity on ring-connected multicomputers
(ACM, 1993-10)The progressive refinement method is investigated for parallelization on ring-connected multicomputers. A synchronous scheme, based on static task assignment, is proposed, in order to achieve better coherence during the ... -
A recursive graph bipartitioning algorithm by vertex separators with fixed vertices for permuting sparse matrices into block diagonal form with overlap
(Bilkent University, 2011)Solving sparse system of linear equations Ax=b using preconditioners can be effi- ciently parallelized using graph partitioning tools. In this thesis, we investigate the problem of permuting a sparse matrix into a block ... -
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 ... -
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 ... -
Revisiting hypergraph models for sparse matrix partitioning
(Society for Industrial and Applied Mathematics, 2007)We provide an exposition of hypergraph models for parallelizing sparse matrix-vector multiplies. Our aim is to emphasize the expressive power of hypergraph models. First, we set forth an elementary hypergraph model for the ...
