In many large scale scientific or engineering computations, ranging from computing the
frequency response of a circuit to the earthquake response of a buildingto the energy levels …

We are witnessing a dramatic change in computer architecture due to the multicore
paradigm shift, as every electronic device from cell phones to supercomputers confronts …

This paper introduces a storage format for sparse matrices, called compressed sparse
blocks (CSB), which allows both Ax and A, x to be computed efficiently in parallel, where A is …

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

Sparse matrix–vector multiplication is an important computational kernel that performs
poorly on most modern processors due to a low compute-to-memory ratio and irregular …

Efficient Computation of PageRank Taher H. Haveliwala (taherh@ db. stanford. edu)
Abstract: This paper discusses efficient techniques for computing PageRank, a ranking …

Several manufacturers have already started to commercialize near-bank Processing-In-
Memory (PIM) architectures, after decades of research efforts. Near-bank PIM architectures …

Sparse matrix-vector multiplication (SpMxV) is one of the most important computational
kernels in scientific computing. It often suffers from poor cache utilization and extra load …

This dissertation presents an automated system to generate highly efficient, platform-
adapted implementations of sparse matrix kernels. We show that conventional …

Important workloads, such as machine learning and graph analytics applications, heavily
involve sparse linear algebra operations. These operations use sparse matrix compression …