Sparse matrix-vector multiplication (SpMV) is an important kernel in many scientific
applications and is known to be memory bandwidth limited. On modern processors with …

A large class of applications need to execute the same workflow on different datasets of
identical size. Efficient execution of such applications necessitates intelligent distribution of …

We present an hp-adaptive discretization for a sharp interface model with a level-set ghost-
fluid method to simulate compressible multiphase flows. The scheme applies an efficient p …

We consider two-dimensional partitioning of general sparse matrices for parallel sparse
matrix-vector multiply operation. We present three hypergraph-partitioning-based methods …

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 …

Many applications in high performance computing are designed based on underlying
performance and execution models. While these models could successfully be employed in …

Focusing on algorithms for distributed-memory parallel architectures, Parallel Algorithms
presents a rigorous yet accessible treatment of theoretical models of parallel computation …

Using high-level synthesis techniques, this paper proposes an adaptable high-performance
streaming dataflow engine for sparse matrix dense vector multiplication (SpMV) suitable for …

Combinatorial techniques have become essential tools across the landscape of
computational science, and some of the combinatorial ideas undergirding these tools are …

It is shown that if the Boolean hierarchy (BH) collapses, then there exists a sparse set S such
that co-NP⊆NP^S, and therefore the polynomial time hierarchy (PH) collapses to …