The attention mechanism is becoming increasingly popular in Natural Language Processing
(NLP) applications, showing superior performance than convolutional and recurrent …

Sparse linear algebra includes the fundamental and important operations in various large-
scale scientific computing and real-world applications. There exists performance bottleneck …

Sparse matrix-vector multiplication (SpMV) is one of the important kernels of many iterative
algorithms for solving sparse linear systems. The limited storage and computational …

SpMV (Sparse matrix–vector multiplication) is widely used in many fields. Improving the
performance of SpMV has been the pursuit of many researchers. Parallel SpMV using multi …

Benefiting from the self-attention mechanism, Transformer models have attained impressive
contextual comprehension capabilities for lengthy texts. The requirements of high …

Sparse matrix-vector multiplication (SpMV) is a fundamental building block for various
numerical computing applications. However, most existing GPU-SpMV approaches may …

Sparse matrix-vector multiplication (SpMV) is a crucial computing kernel with widespread
applications in iterative algorithms. Over the past decades, research on SpMV optimization …

Iterative solutions of sparse linear systems and sparse eigenvalue problems have a
fundamental role in vital fields of scientific research and engineering. The crucial computing …

The irregular distribution of non-zero elements of large-scale sparse matrix leads to low data
access efficiency caused by the unique architecture of the Sunway many-core processor …

Deep variational residual auto-encoder (ResNet-VAE) has shown promising outcomes in
missing imputation of wastewater quality data. Nevertheless, with its large storage size and …