The symmetric sparse matrix-vector multiplication (SymmSpMV) is an important building
block for many numerical linear algebra kernel operations or graph traversal applications …

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

Nonlinear reformulations of the spectral clustering method have gained a lot of recent
attention due to their increased numerical benefits and their solid mathematical background …

Sparse matrix-vector products (SpMVs) are a bottleneck in many scientific codes. Due to the
heavy strain on the main memory interface from loading the sparse matrix and the possibly …

Estimating the graphical structures of high dimensional data and identifying the presence of
clusters in them are ubiquitous tasks in every scientific domain that deals with interacting or …

The explicit evaluation of selected entries of the inverse of a given sparse matrix is an
important process in various application fields and is gaining visibility in recent years. While …