A cross-platform Sparse Matrix Vector Multiplication (SpMV) framework for many-core architectures (GPUs and Xeon Phi).

Sparsification technology is crucial for deploying convolutional neural networks in resource-constrained environments. However, the efficiency of sparse models is hampered by irregular memory access ...

Operations related to Sparse matrix multiplication are frequently used in scientific computing area, and these operations usually become a performance bottleneck because of their high operational ...

Therefore, sparse matrix multiplication is the most time-consuming step in the density matrix purification algorithm for linear-scaling DFT calculations. We propose to use the MPI_Allgather function ...

SpMV: Sparse Matrix–Vector Multiplication, a core operation in many numerical algorithms where a sparse matrix is multiplied by a vector.

In particular, we extend the DBCSR sparse matrix library, which is the basic building block for linear scaling electronic structure theory and low scaling correlated methods in CP2K. The library is ...

Real PIM systems can provide high levels of parallelism, large aggregate memory bandwidth and low memory access latency, thereby being a good fit to accelerate the widely-used, memory-bound Sparse ...