We present performance results for dense linear algebra using recent NVIDIA GPUs. Our
matrix-matrix multiply routine (GEMM) runs up to 60% faster than the vendor's …

We present performance results for dense linear algebra using the 8-series NVIDIA GPUs.
Our matrix-matrix multiply routine (GEMM) runs 60% faster than the vendor implementation …

Automatic data distribution is a key feature to obtain efficient implementations from abstract
and portable parallel codes. We present a highly efficient and extensible runtime library that …

Graphics processor units (GPU) that are originally designed for graphics rendering have
emerged as massively-parallel “co-processors” to the central processing unit (CPU). Small …

In many applications in flows through porous media, one needs to determine the properties
of subsurface to detect, monitor, or predict the actions of natural or induced forces. Here, we …

Abstract Today's Graphics Processor Units (GPU) are powerful computation platforms used
not only for graphic rendering but also for multi-purpose computation. Now reaching a …

This paper studies the impact of using automatic data-layout techniques on the process of
coding the well-known multigrid MG NAS parallel benchmark. We describe the sequential …

This work considers the acceleration of matrix-based computational electromagnetic (CEM)
techniques using graphics processing units (GPUs). These massively parallel processors …

The OpenVX standard has appeared as an answer from the computer vision community to
the challenge of accelerating vision applications on embedded heterogeneous platforms. It …

The accurate simulation of turbulence and the implementation of corresponding turbulence
models are both critical to the understanding of the complex physics behind turbulent flows …