Efficient exploitation of exascale architectures requires rethinking of the numerical
algorithms used in many large-scale applications. These architectures favor algorithms that …

Recurrent neural networks (RNNs) are an important class of deep learning (DL) models.
Existing DL frameworks have unsatisfying performance for online serving: many RNN …

This work introduces CLBlast, an open-source BLAS library providing optimized OpenCL
routines to accelerate dense linear algebra for a wide variety of devices. It is targeted at …

The computational efficiency of a state of the art ab initio quantum transport (QT) solver,
capable of revealing the coupled electrothermal properties of atomically-resolved nano …

In recent years, the heterogeneity of both commodity and supercomputers hardware has
increased sharply. Accelerators, such as GPUs or Intel Xeon Phi co-processors, are often …

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 …

We present a computational framework for high-performance tensor contractions on GPUs.
High-performance is difficult to obtain using existing libraries, especially for many …

With the end of Dennard scaling and Moore's law, it is becoming increasingly difficult to build
hardware for emerging applications that meet power and performance targets, while …

This article describes a standard API for a set of Batched Basic Linear Algebra Subprograms
(Batched BLAS or BBLAS). The focus is on many independent BLAS operations on small …

Dense linear algebra (DLA) has historically been in the vanguard of software that must be
adapted first to hardware changes. This is because DLA is both critical to the accuracy and …