While systolic array architectures have the potential to deliver tremendous performance, it is
notoriously challenging to customize an efficient systolic array processor for a target …

Dense matrix multiply (MM) serves as one of the most heavily used kernels in deep learning
applications. To cope with the high computation demands of these applications …

Sparse-Matrix Dense-Matrix multiplication (SpMM) is the key operator for a wide range of
applications including scientific computing, graph processing, and deep learning …

Distributed memory programming is the established paradigm used in high-performance
computing (HPC) systems, requiring explicit communication between nodes and devices …

As the increasing complexity of Neural Network (NN) models leads to high demands for
computation, AMD introduces a heterogeneous programmable system-on-chip (SoC), ie …

Noctua 2 is a supercomputer operated at the Paderborn Center for Parallel Computing
(PC2) at Paderborn University in Germany. Noctua 2 was inaugurated in 2022 and is an …

N-body methods are one of the essential algorithmic building blocks of high-performance
and parallel computing. Previous research has shown promising performance for …

Knowledge graphs (KGs) have achieved significant attention in recent years, particularly in
the area of the Semantic Web as well as gaining popularity in other application domains …

We present the first FPGA implementation of the full simulation pipeline of a shallow water
code based on the discontinuous Galerkin method. Using OpenCL and following an …

The computation of electron repulsion integrals (ERIs) over Gaussian-type orbitals (GTOs) is
a challenging problem in quantum-mechanics-based atomistic simulations. In practical …