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 highlight the trends leading to the increased appeal of using hybrid multicore+ GPU
systems for high performance computing. We present a set of techniques that can be used to …

Most recent HPC platforms have heterogeneous nodes composed of multi-core CPUs and
accelerators, like GPUs. Programming such nodes is typically based on a combination of …

In this paper, we propose an OpenCL framework that combines multiple GPUs and treats
them as a single compute device. Providing a single virtual compute device image to the …

While general-purpose homogeneous multi-core architectures are becoming ubiquitous,
there are clear indications that, for a number of important applications, a better …

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 …

We describe an implementation of the Communication-Avoiding QR (CAQR) factorization
that runs entirely on a single graphics processor (GPU). We show that the reduction in …

Communication overhead is one of the dominant factors affecting performance in high-end
computing systems. To reduce the negative impact of communication, programmers overlap …

Accelerator-enhanced computing platforms have drawn a lot of attention due to their
massive peak commutational capacity. Despite significant advances in the programming …

Researchers from the Formal Linear Algebra Method Environment (Flame) project have
developed new methodologies for analyzing, designing, and implementing linear algebra …