Mukunoki, D., Takahashi, D.: Optimization of sparse matrix-vector multiplication for CRS format on NVIDIA Kepler architecture GPUs. In: Murgante, B., Misra, S., Carlini, M., Torre, C.M., Nguyen, H.Q., Taniar, D., Apduhan, B.O., Gervasi, O. (eds.) Computational Science and...
of sparse matrix-vector multiplication, we are not concerned with modifying matrices, we will only consider static sparse matrix formats, as opposed to those suitable for rapid insertion and deletion of elements. The primary distinction among sparse matrix representations is the sparsity pattern, or ...
Parallel Sparse Matrix Vector Multiplication on Intel MIC: Performance AnalysisNumerous important scientific and engineering applications rely on and are hindered by, the intensive computational and storage requirements of sparse matrix-vector multiplication (SpMV) operation. SpMV also forms an important part...
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Double precision floating point Sparse Matrix-Vector Multiplication (SMVM) is a critical computational kernel used in iterative solvers for systems of sparse linear equations. The poor data locality exhibited by sparse matrices along with the high memory bandwidth requirements of SMVM result in poor ...
Sparse matrix-vector multiplication on GPGPU clusters: A new storage format and a scalable implementation Highly efficient finite element analysis of bone structure.Element-by-element sparse matrix multiplication.Solution of problems in linear elasticity with u... A Basermann,AR Bishop,G Hager,... ...
In this study, we discuss the implementation and performance of sparse matrix-vector multiplication (SpMV), which is the kernel of sparse iterative solvers, with reduced-precision floating-point formats on general-purpose processors. The reduced-precision scheme we adopt truncates the mantissa bits ...
classSolution {public: vector<vector<int>> multiply(vector<vector<int>>& A, vector<vector<int>>&B) { vector<vector<int>>ret;intha =A.size();if(!ha)returnret;intwa = A[0].size();if(!wa)returnret;inthb =wa;intwb = B[0].size();if(!wb)returnret;//Preprocessingunordered_map...
Autotuning Sparse Matrix-Vector Multiplication for MulticoreSparse matrix-vector multiplication (SpMV) is an important kernel in scientific and engineering computing. Straightforward parallel implementations of SpMV often perform poorly, and with the increasing variety of architectural features in multicore ...
The floating- point Sparse Matrix Vector (SpMatVec) multiplication, a key computation kernel in many scientific applications does not run at peak performance on general purpose microprocessors. The high I/O bandwidth and avoidance of cache-hierarchy architecture in this reconfigurable computer allow us...