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 …

State-of-the-art engineering and science codes have grown in complexity dramatically over
the last two decades. Application teams have adopted more sophisticated development …

In this paper, we develop software for decomposing sparse tensors that is portable to and
performant on a variety of multicore, manycore, and GPU computing architectures. The result …

Matrix multiplication (GEMM) is the most important operation in dense linear algebra.
Because it is a computebound operation that is rich in data reuse, many applications from …

There is a growing interest in custom spatial accelerators for machine learning applications.
These accelerators employ a spatial array of processing elements (PEs) interacting via …

Training Convolutional Neural Network (CNN) is a computationally intensive task, requiring
efficient parallelization to shorten the execution time. Considering the ever-increasing size of …

Many scientific applications rely on sparse direct solvers for their numerical robustness.
However, performance optimization for these solvers remains a challenging task, especially …

Factorization and multiplication of dense matrices and tensors are critical, yet extremely
expensive pieces of the scientific toolbox. Careful use of low rank approximation can …

One of the most computationally challenging problems expected for the High-Luminosity
Large Hadron Collider (HL-LHC) is determining the trajectory of charged particles during …