Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 7 of 7 for “"SpMV"”.
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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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SuperTaco : Taco Tensor Algebra kernels on distributed systems using Legion
… We perform a strong scaling analysis for the SpMV and TTM kernels under a row blocking distribution schedule, and find speedups of 9-10x when using 20 cores on a single node. For multi-node systems using 20 cores per node, SpMV achieves a 33.3x speedup at 160 cores and TTM achieves a 42.0x …
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WACO: Learning workload-aware co-optimization of the format and schedule of a sparse tensor program
… We evaluate WACO for four different algorithms (SpMV, SpMM, SDDMM, and MTTKRP) on a CPU using 726 different sparsity patterns. Our experimental results shows that WACO outperformed four state-of-the-art baselines, Intel MKL, Formatonly auto-tuner, TACO with a default schedule, and ASpT. Compared …
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A parallel fill estimation algorithm for sparse matrices and tensors in blocked formats
… blocked sparse matrix-vector multiplication (SpMV) when the block size was chosen using fill estimates in a model due to Vuduc et al. Much of the work presented in this thesis appears in ["A Fill Estimation Algorithm for Sparse Matrices and Tensors in Blocked Formats," in 2018 IEEE …
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Structure-based Optimizations for Sparse Matrix-Vector Multiply
This dissertation introduces two novel techniques, OSF and PBR, to improve the performance of Sparse Matrix-vector Multiply (SMVM) kernels, which dominate the runtime of iterative solvers for systems of linear equations. SMVM computations that use sparse formats typically achieve only a small …
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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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Accelerating induction machine finite-element simulation with parallel processing
Finite element analysis used for detailed electromagnetic analysis and design of electric machines is computationally intensive. A means of accelerating two-dimensional transient finite element analysis, required for induction machine modeling, is explored using graphical processing units (GPUs) …