6 citations · 19 across the 18 of their papers we have counts for
9 papers · 1 filter
EmuGEMM: Fused Tensor Core Kernels for Precision Emulation in Matrix Multiplication
Denghui Lu, Alexander Maeder, Mathieu Luisier +1
Modern GPUs devote an increasing silicon budget to low-precision matrix-multiplication units, widening the precision-throughput gap for scientific computing workloads. Ozaki Scheme…
ADELIA: Automatic Differentiation for Efficient Laplace Inference Approximations
Afif Boudaoud, Lisa Gaedke-Merzhäuser, Alexandros Nikolaos Ziogas +6
Spatio-temporal Bayesian inference drives environmental and health sciences using latent Gaussian models. Integrated Nested Laplace Approximations (INLA) enable inference for these…
Parallel Quadratic Selected Inversion in Quantum Transport Simulation
Vincent Maillou, Matthias Bollhofer, Olaf Schenk +2
Driven by Moore's Law, the dimensions of transistors have been pushed down to the nanometer scale. Advanced quantum transport (QT) solvers are required to accurately simulate such…
Ab-initio Quantum Transport with the GW Approximation, 42,240 Atoms, and Sustained Exascale Performance
Nicolas Vetsch, Alexander Maeder, Vincent Maillou +7
Designing nanoscale electronic devices such as the currently manufactured nanoribbon field-effect transistors (NRFETs) requires advanced modeling tools capturing all relevant quant…
Serinv: A Scalable Library for the Selected Inversion of Block-Tridiagonal with Arrowhead Matrices
Vincent Maillou, Lisa Gaedke-Merzhaeuser, Alexandros Nikolaos Ziogas +2
The inversion of structured sparse matrices is a key but computationally and memory-intensive operation in many scientific applications. There are cases, however, where only partic…
Arrow Matrix Decomposition: A Novel Approach for Communication-Efficient Sparse Matrix Multiplication
Lukas Gianinazzi, Alexandros Nikolaos Ziogas, Langwen Huang +9
We propose a novel approach to iterated sparse matrix dense matrix multiplication, a fundamental computational kernel in scientific computing and graph neural network training. In…