Modern applications in engineering and data science are increasingly based on
multidimensional data of exceedingly high volume, variety, and structural richness …

Machine learning and data mining algorithms are becoming increasingly important in
analyzing large volume, multi-relational and multi--modal datasets, which are often …

The deployment of many-body quantum chemistry methods onto massively parallel high-
performance computing (HPC) platforms is reviewed. The particular focus is on highly …

We present “GEMM-like Tensor–Tensor multiplication”(GETT), a novel approach for dense
tensor contractions that mirrors the design of a high-performance general matrix–matrix …

Tensor computations---in particular tensor contraction (TC)---are important kernels in many
scientific computing applications. Due to the fundamental similarity of TC to matrix …

Tensor contractions constitute a key computational ingredient of numerical multi-linear
algebra. However, as the order and dimension of tensors grow, the time and space …

The efficiency of tensor contraction is of great importance. Compilers cannot optimize it well
enough to come close to the performance of expert-tuned implementations. All existing …

This work optimizes tensor-times-dense matrix multiply (Ttm) for general sparse and semi-
sparse tensors on CPU and NVIDIA GPU platforms. Ttm is a computational kernel in tensor …

This paper presents the optimized design and implementation of sparse tensor-times-dense
matrix multiply (SpTTM) for CPU and GPU platforms. This primitive is a critical bottleneck in …

We present an efficient implementation of dynamically pruned quantum dynamics, both in
coordinate space and in phase space. We combine the ideas behind the biorthogonal von …