6 citations · 33 across the 24 of their papers we have counts for
26 papers
Ab initio Modeling of MoS2/Oxide Device Interfaces with Machine Learned Electronic Structures
Manasa Kaniselvan, Mauro Dossena, Denghui Lu +4
We introduce a new ab initio approach to simulate semiconductor devices that integrates scalable machine-learned (ML) electronic structure models with an advanced quantum transport…
MALOQ: Massively Accelerated Learning of Operators for Quantum Transport
Manasa Kaniselvan, Alexander Maeder, Denghui Lu +2
Machine-learned (ML) operator models can be trained to predict density functional theory (DFT) Hamiltonian/density matrices at significantly reduced computational cost, thus extend…
Optimizing Semiconductor Device Simulations through Low-Precision Arithmetic
Alexander Maeder, Denghui Lu, Nicolas Vetsch +6
Architectural changes in GPUs, especially the promotion of low-precision computational units, pose significant challenges to traditional, FP64-based high-performance computing (HPC…
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…
Acceleration of Atomistic NEGF: Algorithms, Parallelization, and Machine Learning
Mathieu Luisier, Nicolas Vetsch, Alexander Maeder +8
The Non-equilibrium Green's function (NEGF) formalism is a particularly powerful method to simulate the quantum transport properties of nanoscale devices such as transistors, photo…
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…
Accelerated Spatio-Temporal Bayesian Modeling for Multivariate Gaussian Processes
Lisa Gaedke-Merzhäuser, Vincent Maillou, Fernando Rodriguez Avellaneda +5
Multivariate Gaussian processes (GPs) offer a powerful probabilistic framework to represent complex interdependent phenomena. They pose, however, significant computational challeng…
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…
Distributed Equivariant Graph Neural Networks for Large-Scale Electronic Structure Prediction
Manasa Kaniselvan, Alexander Maeder, Chen Hao Xia +2
Equivariant Graph Neural Networks (eGNNs) trained on density-functional theory (DFT) data can potentially perform electronic structure prediction at unprecedented scales, enabling…
Comparing Parallel Functional Array Languages: Programming and Performance
David van Balen, Tiziano De Matteis, Clemens Grelck +12
Parallel functional array languages are an emerging class of programming languages that promise to combine low-effort parallel programming with good performance and performance por…
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…
Learning the Electronic Hamiltonian of Large Atomic Structures
Chen Hao Xia, Manasa Kaniselvan, Alexandros Nikolaos Ziogas +4
Graph neural networks (GNNs) have shown promise in learning the ground-state electronic properties of materials, subverting ab initio density functional theory (DFT) calculations w…
Electron-Electron Interactions in Device Simulation via Non-equilibrium Green's Functions and the GW Approximation
Leonard Deuschle, Jiang Cao, Alexandros Nikolaos Ziogas +4
The continuous scaling of metal-oxide-semiconductor field-effect transistors (MOSFETs) has led to device geometries where charged carriers are increasingly confined to ever smaller…
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…
Single Neuromorphic Memristor closely Emulates Multiple Synaptic Mechanisms for Energy Efficient Neural Networks
Christoph Weilenmann, Alexandros Ziogas, Till Zellweger +6
Biological neural networks do not only include long-term memory and weight multiplication capabilities, as commonly assumed in artificial neural networks, but also more complex fun…
FuzzyFlow: Leveraging Dataflow To Find and Squash Program Optimization Bugs
Philipp Schaad, Timo Schneider, Tal Ben-Nun +3
The current hardware landscape and application scale is driving performance engineers towards writing bespoke optimizations. Verifying such optimizations, and generating minimal fa…
Deinsum: Practically I/O Optimal Multilinear Algebra
Alexandros Nikolaos Ziogas, Grzegorz Kwasniewski, Tal Ben-Nun +2
Multilinear algebra kernel performance on modern massively-parallel systems is determined mainly by data movement. However, deriving data movement-optimal distributed schedules for…
Fast Arbitrary Precision Floating Point on FPGA
Johannes de Fine Licht, Christopher A. Pattison, Alexandros Nikolaos Ziogas +2
Numerical codes that require arbitrary precision floating point (APFP) numbers for their core computation are dominated by elementary arithmetic operations due to the super-linear…
On the Parallel I/O Optimality of Linear Algebra Kernels: Near-Optimal Matrix Factorizations
Grzegorz Kwasniewski, Marko Kabić, Tal Ben-Nun +8
Matrix factorizations are among the most important building blocks of scientific computing. State-of-the-art libraries, however, are not communication-optimal, underutilizing curre…
Pebbles, Graphs, and a Pinch of Combinatorics: Towards Tight I/O Lower Bounds for Statically Analyzable Programs
Grzegorz Kwasniewski, Tal Ben-Nun, Lukas Gianinazzi +5
Determining I/O lower bounds is a crucial step in obtaining communication-efficient parallel algorithms, both across the memory hierarchy and between processors. Current approaches…
On the Parallel I/O Optimality of Linear Algebra Kernels: Near-Optimal LU Factorization
Grzegorz Kwasniewski, Tal Ben-Nun, Alexandros Nikolaos Ziogas +3
Dense linear algebra kernels, such as linear solvers or tensor contractions, are fundamental components of many scientific computing applications. In this work, we present a novel…
A Data-Centric Approach to Extreme-Scale Ab initio Dissipative Quantum Transport Simulations
Alexandros Nikolaos Ziogas, Tal Ben-Nun, Guillermo Indalecio Fernández +3
The computational efficiency of a state of the art ab initio quantum transport (QT) solver, capable of revealing the coupled electro-thermal properties of atomically-resolved nano-…
Optimizing the Data Movement in Quantum Transport Simulations via Data-Centric Parallel Programming
Alexandros Nikolaos Ziogas, Tal Ben-Nun, Guillermo Indalecio Fernández +3
Designing efficient cooling systems for integrated circuits (ICs) relies on a deep understanding of the electro-thermal properties of transistors. To shed light on this issue in cu…