The solution of large sparse linear systems arises in many applications, such as
computational fluid dynamics and oil reservoir simulation. In realistic cases the matrices are …

Unlike traditional dark silicon works that attack the computing logic, this article puts a focus
on the memory part, which dissipates most of the energy for memory-bound CPU …

The solution of large sparse systems of linear constraints is at the base of most interactive
solvers for physically-based animation of soft body dynamics. We focus on applications with …

We develop the first parallel graph coloring heuristics with strong theoretical guarantees on
work and depth and coloring quality. The key idea is to design a relaxation of the vertex …

Graph algorithms are challenging to parallelize on manycore architectures due to complex
data dependencies and irregular memory access. We consider the well studied problem of …

When dealing with large bipartite graphs, butterfly counting is a crucial and time-consuming
operation. Graphics processing units (GPUs) are widely used parallel heterogeneous …

In this paper, we study the effect of enhancing GPU-accelerated Krylov solvers with
preconditioners. We consider the BiCGSTAB, CGS, QMR, and IDR (s) Krylov solvers. For a …

The sparse triangular matrix solve (SpTrSV) is an important computation kernel that is
demanded by a variety of numerical methods such as the Gauss-Seidel iterations. However …

Graph coloring is an assignment of colors to the vertices of a graph such that no two
adjacent vertices get the same color. It is a key building block in many applications. Finding …

We discuss new developments of a hybrid parallel iterative sparse linear solver framework
focused on petroleum reservoir flow and geomechanical simulation. It runs efficiently on …