Sparse solvers provide essential functionality for a wide variety of scientific applications.
Highly parallel sparse solvers are essential for continuing advances in high-fidelity, multi …

Sparse direct solvers play a vital role in large-scale high performance computing in science
and engineering. Existing distributed sparse direct methods employ multifrontal/supernodal …

This paper presents a unified communication optimization framework for sparse triangular
solve (SpTRSV) algorithms on CPU and GPU clusters. The framework builds upon a 3D …

This paper presents GPTuneCrowd, a crowd-based autotuning framework for tuning high-
performance computing applications. GPTuneCrowd collects performance data from various …

We show how to exploit graph sparsity in the Floyd-Warshall algorithm for the all-pairs
shortest path (Apsp) problem. Floyd-Warshall is an attractive choice for Apsp on high …

Existing streaming graph processing systems typically adopt two phases of refinement and
recomputation to ensure the correctness of the incremental computation. However, severe …

Sparse LU factorization is the indispensable building block of the circuit simulation, and
dominates the simulation time, especially when dealing with large-scale circuits. Radio …

We design and implement an efficient parallel algorithm for finding a perfect matching in a
weighted bipartite graph such that weights on the edges of the matching are large. This …

Sparse LU factorization is essential for scientific and engineering simulations. In this work,
we present swSuperLU, a highly scalable sparse direct solver on Sunway manycore …

We propose a new algorithm to improve the strong scalability of right-looking sparse LU
factorization on distributed memory systems. Our 3D algorithm for sparse LU uses a three …