As both CPUs and GPUs become employed in a wide range of applications, it has been
acknowledged that both of these Processing Units (PUs) have their unique features and …

Kernel methods provide an elegant and principled approach to nonparametric learning, but
so far could hardly be used in large scale problems, since naïve implementations scale …

Solving dense linear systems of equations is a fundamental problem in scientific computing.
Numerical simulations involving complex systems represented in terms of unknown …

A class of preconditioners based on balancing domain decomposition by constraints
methods is introduced in the Portable, Extensible Toolkit for Scientific Computation (PETSc) …

The Keeneland project—named for a historic thoroughbred horse racing track in Lexington,
Kentucky—is a five-year Track 2D grant awarded by the US National Science Foundation …

To fully tap into the potential of heterogeneous machines composed of multicore processors
and multiple accelerators, simple offloading approaches in which the main trunk of the …

We present the performance analysis of a port of the LU benchmark from the NAS Parallel
Benchmark (NPB) suite to NVIDIA's Compute Unified Device Architecture (CUDA), and …

We present a Hessenberg reduction (HR) algorithm for hybrid systems of homogeneous
multicore with GPU accelerators that can exceed 25× the performance of the corresponding …

Solving large sparse linear systems is often the most computationally intensive component
of many scientific computing applications. In the past, sparse multifrontal direct factorization …

We present a high-performance GPU kernel with a substantial speedup over vendor
libraries for very small matrix computations. In addition, we discuss most of the challenges …