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Showing 1 to 12 of 12 for “"sparse matrix-vector multiplication"”.
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Sparse matrix-vector multiplication by specialization
… in the very common numerical procedure of sparse matrix-vector multiplication, in the case where a single matrix is to be multiplied by many vectors, is explored. The main objective is the evaluation of the speed-ups that can be obtained with program specialization without considering the …
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Optimization by runtime specialization for sparse matrix-vector multiplication
… the potential for obtaining speed-ups for sparse matrix-dense vector multipli- cation using runtime specialization, in the case where a single matrix is to be multiplied by many vectors. We experiment with five methods involving run-time specialization with parallelization, comparing them …
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On implementing sparse matrix-vector multiplication on intel platform
Sparse matrix-vector multiplication, SpMV, can be a performance bottle-neck in iterative solvers and algebraic eigenvalue problems. In this thesis, we present our sparse matrix compressed chunk storage format (CCF) and SpMV CCF kernel that realizes high performance on Intel Xeon multicore and Phi …
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High-Performancs Sparse Matrix-Vector Multiplication on GPUS for Structured Grid Computations
In this thesis, we address efficient sparse matrix-vector multiplication for matrices arising from structured grid problems with high degrees of freedom at each grid node. Sparse matrix-vector multiplication is a critical step in the iterative solution of sparse linear systems of equations arising …
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Hypergraph-Based Combinatorial Optimization of Matrix -Vector Multiplication
The second problem we address is parallel matrix-vector multiplication for large sparse matrices. Parallel sparse matrix-vector multiplication is a particularly important numerical kernel in computational science. We have focused on optimizing the parallel performance of this operation by reducing …
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Reducing communication in sparse solvers
Sparse matrix operations dominate the cost of many scientific applications. In parallel, the performance and scalability of these operations is limited by irregular point-to-point communication. Multiple methods are investigated throughout this dissertation for reducing the cost associated with …
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Hypergraph-Based Combinatorial Optimization of Matrix-Vector Multiplication
… thesis, we will describe our work on optimizing matrix-vector multiplication using combinatorial techniques. Our research has focused on two different problems in combinatorial scientific computing, both involving matrix-vector multiplication, and both are solved using hypergraph models. For both …
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Fill estimation for blocked sparse matrices and tensors
Many sparse matrices and tensors from a variety of applications, such as finite element methods and computational chemistry, have a natural aligned rectangular nonzero block structure. Researchers have designed high-performance blocked sparse operations which can take advantage of this sparse …
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Towards a deeper understanding of hybrid programming
… implementation options for a structured grid sparse matrix-vector multiplication in depth. These choices differ in how hybrid parallelism is exploited at the application level (coarse-grained vs. fine-grained problem decomposition) and with respect to the hybrid programming systems (pure MPI …
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A parallel fill estimation algorithm for sparse matrices and tensors in blocked formats
Many sparse matrices and tensors from a variety of applications, such as finite element methods and computational chemistry, have a natural aligned rectangular nonzero block structure. Researchers have designed high-performance blocked sparse operations which can take advantage of this sparsity …
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A language and a system for program optimization
… on heuristics. Locus was able to generate a matrix-matrix multiplication code that outperformed the IBM XLC internal hand-optimized version by 2× on the Power 9 processors. On Intel E5, Locus generates code with performance comparable to Intel MKL’s. We also improve performance relative to …
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Accelerating induction machine finite-element simulation with parallel processing
… are preconditioner formation, computation of the sparse iterative solution, and matrix-vector multiplication for magnetic flux density calculation. Due to the sparsity of the finite element problem, GPU-implementation of the sparse iterative solution did not result in faster computation times. The …