Exploiting matrix symmetry to halve memory footprint offers an opportunity for accelerating memory-bound computations like Sparse Matrix-Vector Multiplication (SpMV). However, symmetric SpMV incurs ...

Sparse matrix vector multiplications (SpMVs) are typical sparse operations which have a high ratio of memory reference volume to computations. According to the roof-line model, the performance of such ...

In particular, we examine and optimize the general and symmetric matrix-vector multiplication routines (gemv/symv), which are some of the most heavily used linear algebra kernels in many important ...

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 ...

The result of elementwise multiplication is also known as the Schur or Hadamard product. Element multiplication (using the # operator) should not be confused with matrix multiplication (using the * ...