The general matrix-matrix multiplication (GEMM) is the most important numerical kernel in
dense linear algebra, and is the key component for obtaining high performance in most …

Scientific applications require solvers that work on many small size problems that are
independent from each other. At the same time, the high-end hardware evolves rapidly and …

We present a review of the current best practices in parallel programming models for dense
linear algebra (DLA) on heterogeneous architectures. We consider multicore CPUs, stand …

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 …

This paper proposes an API for Batched Basic Linear Algebra Subprograms (Batched
BLAS). We focus on many independent BLAS operations on small matrices that are grouped …

Current research on cloud service certification is working on techniques to continuously, ie
automatically and repeatedly, assess whether cloud services satisfy certification criteria …

As modern hardware keeps evolving, an increasingly effective approach to develop energy
efficient and high-performance solvers is to design them to work on many small size …

Many scientific applications, ranging from national security to medical advances, require
solving a number of relatively small-size independent problems. As the size of each …

A challenging class of problems arising in many GPU applications, called batched problems,
involves linear algebra operations on many small-sized matrices. We designed batched …

The manycore paradigm shift, and the resulting change in modern computer architectures,
has made the development of optimal numerical routines extremely challenging. In this …