The NVIDIA Volta GPU microarchitecture introduces a specialized unit, called Tensor Core
that performs one matrix-multiply-and-accumulate on 4x4 matrices per clock cycle. The …

Convolutional neural networks (CNNs) have recently attracted considerable attention due to
their outstanding accuracy in applications, such as image recognition and natural language …

Estimating the structure of directed acyclic graphs (DAGs) from observational data remains a
significant challenge in machine learning. Most research in this area concentrates on …

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 …

In keeping with the trend of heterogeneity in high-performance computing, hardware
manufacturers and vendors are developing new architectures and associated software …

From FORTRAN to NumPy, arrays have revolutionized how we express computation.
However, arrays in these, and almost all prominent systems, can only handle dense …

The solving of tridiagonal systems is one of the most computationally expensive parts in
many applications, so that multiple studies have explored the use of NVIDIA GPUs to …

Deep learning (DL) is one of the most prominent branches of machine learning. Due to the
immense computational cost of DL workloads, industry and academia have developed DL …

Generally, floating-point computations comprise rounding errors; the result may be
inaccurate and not identical (non-reproducible). Particularly, heterogeneous computing has …

Hardware technological advances are struggling to match scientific ambition, and a key
question is how we can use the transistors that we already have more effectively. This is …