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

Modeling multi-way data can be accomplished using tensors, which are data structures
indexed along three or more dimensions. Tensors are increasingly used to analyze …

We investigate hypergraph partitioning-based methods for efficient parallelization of
communicating tasks. A good partitioning method should divide the load among the …

The focus of this article is efficient parallelization of the canonical polyadic decomposition
algorithm utilizing the alternating least squares method for sparse tensors on distributed …

Building upon the wide-ranging success of the first edition, Parallel Scientific Computation
presents a single unified approach to using a range of parallel computers, from a small …

Tomography is a powerful technique for 3D imaging of the interior of an object. With the
growing sizes of typical tomographic data sets, the computational requirements for …

To minimize the communication in parallel sparse matrix-vector multiplication while
maintaining load balance, we need to partition the sparse matrix optimally into k disjoint …

The sparse matrix partitioning problem arises when minimizing communication in parallel
sparse matrix–vector multiplications. Since the problem is NP-hard, heuristics are usually …

Irregular applications comprise an increasingly important workload domain for many fields,
including bioinformatics, chemistry, physics, social sciences and machine learning …

We investigate sparse matrix bipartitioning–a problem where we minimize the
communication volume in parallel sparse matrix-vector multiplication. We prove, by …