SpMVSparse Matrix-Vector Multiplication
SpMVSatellite Panicum Mosaic Virus (biology)
SpMVSynaptosomal Plasma Membrane Vesicle
SpMVSijil Pelajaran Malaysia Vokasional
SpMVSri Padmavati Mahila Visvavidyalayam (Indian school)
SpMVSyncytiotrophoblast Microvillous Plasma Membrane Vesicles (immunology)
SpMVSistem Placil Malih Vrednosti (Slovenian: Retail Payment System)
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In each iteration m, updates to the next solution [x.sub.m+1] and residual [r.sub.m+1] consist of one or more sparse matrix-vector multiplications (SpMVs) and vector-vector operations in each iteration.
(01) for i = 0 to n - 1 (02) {the value set of the ith parameter ps[i] = SPMV (type[i]); (03) for (each p in ps[i]) (04) {if (the type of p is numeric type && p does not meet valCS[i]) (05) ps = ps[i] - {p}; (06) else if (the type of p is other type && p meets valCS[i]) (07) ps = ps[i] - {p}; (08) } (09) } (10) if(n == 1) return ps[0]; (11) else if (n == 2) (12) using pair-wise combinational testing method to generate test cases ts; (13) else if (n >= 3) (14) using 3-tuple combinational testing method to generate test cases ts; (15) for (each t in ts) (16) {if (t meets relCS) (17) ts = ts - {t}; (18) } (19) return ts; PROCEDURE 3: SPMV().
Condition and parameter mutation algorithms (PCMA and SPMV) are presented to generate mutated precondition and parameter value based on security testing framework.
We propose an improved PCSR called IPCSR to calculate SpMV on the GPU.
Sparse matrix-vector multiplication (SpMV) is so important that there has existed a great deal of work on accelerating it.
Liu and Schmidt present LightSpMV, a novel fast CUDA-compatible SpMV algorithm using the standard CSR format, which achieves high parallelism by benefiting from the fine-grained dynamic distribution of matrix rows over warps/vectors [16].
PCSR is CSR-based SpMV implementation on the GPU and involves two kernels.
Like other GPU-accelerated SpMV algorithms, the performance of IPCSR is also sensitive to the GPU architecture.
(i) A novel SpMV implementation on a GPU, which keeps CSR intact, is proposed.
(ii) Our proposed SpMV algorithm on a GPU is extended to multiple GPUs.
Following this introduction, the matrix storage, CUDA architecture, and SpMV are described in Section 2.
Assume that A is an nxn sparse matrix and x isa vector of size n, and a sequential version of CSR-based SpMV is described in Algorithm 1.