Related papers: Evaluation of disconnected contributions using GPU…
A number of stochastic methods developed for the calculation of fermion loops are investigated and compared, in particular with respect to their efficiency when implemented on Graphics Processing Units (GPUs). We assess the performance of…
We present results on the disconnected contributions to three point functions entering in studies of hadron structure. We use $N_F = 2+1+1$ twisted mass fermions and give a detailed description on the results of the nucleon {\sigma}-terms,…
The exact evaluation of the disconnected diagram contributions to the flavor-singlet pseudoscalar meson mass, the nucleon sigma term and the nucleon electromagnetic form factors, is carried out utilizing GPGPU technology with the NVIDIA…
We compare several methods for computing disconnected fermion loops contributing to nucleon three-point functions. The comparison is carried out using one ensemble of $N_f=2+1+1$ twisted mass fermions with pion mass of 373 MeV. The complete…
We present an implementation of the disconnected diagram contributions to quantities such as the flavor-singlet pseudoscalar meson mass which are accelerated by GPGPU technology utilizing the NVIDIA CUDA platform. To enable the exact…
We compute the disconnected quark loops contributions entering the determination of nucleon observables, by using a $N_f = 2$ ensemble of twisted mass fermions with a clover term at a pion mass $m_\pi = 133$ MeV. We employ exact deflation…
We present results on the isovector and isoscalar nucleon axial form factors including disconnected contributions,using an ensemble of $N_f =2$ twisted mass clover- improved Wilson fermions simulated with approximately the physical value of…
The latest results from the Twisted-Mass collaboration on disconnected diagrams at the physical value of the pion mass are presented. In particular, we focus on the sigma terms, the axial charges and the momentum fraction, all of them for…
We propose a GPU-based distributed optimization algorithm, aimed at controlling optimal power flow in multi-phase and unbalanced distribution systems. Typically, conventional distributed optimization algorithms employed in such scenarios…
This paper describes a new method for representing embedding tables of graph neural networks (GNNs) more compactly via tensor-train (TT) decomposition. We consider the scenario where (a) the graph data that lack node features, thereby…
Gaussian graphical models (GGMs) are widely used for statistical modeling, because of ease of inference and the ubiquitous use of the normal distribution in practical approximations. However, they are also known for their limited modeling…
We present generalized evolution equations and factorization in terms of the truncated Mellin moments (TMM) of the parton distributions and structure functions. We illustrate the $x$ and $Q^2$ dependence of TMM in the polarized case. Using…
We present how we ported the Hybrid Monte Carlo implementation in the tmLQCD software suite to GPUs through offloading its most expensive parts to the QUDA library. We discuss our motivations and some of the technical challenges that we…
Although QUDA allows for an efficient computation of many QCD quantities, it is surprinsingly lacking tools to evaluate disconnected diagrams, for which GPUs are specially well suited. We aim to fill this gap by creating our own branch of…
Graphics Processing Unit (GPU) computing is becoming an alternate computing platform for numerical simulations. However, it is not clear which numerical scheme will provide the highest computational efficiency for different types of…
We study an extention of total variation denoising over images to over Cartesian power graphs and its applications to estimating non-parametric network models. The power graph fused lasso (PGFL) segments a matrix by exploiting a known…
A promising new algebraic approach to weighted model counting makes use of tensor networks, following a reduction from weighted model counting to tensor-network contraction. Prior work has focused on analyzing the single-core performance of…
Discontinuous Galerkin (DG) methods for the numerical solution of partial differential equations have enjoyed considerable success because they are both flexible and robust: They allow arbitrary unstructured geometries and easy control of…
We introduce the truncated Gaussian graphical model (TGGM) as a novel framework for designing statistical models for nonlinear learning. A TGGM is a Gaussian graphical model (GGM) with a subset of variables truncated to be nonnegative. The…
We present results on the axial and the electromagnetic form factors of the nucleon, as well as, on the first moments of the nucleon generalized parton distributions using maximally twisted mass fermions. We analyze two N_f=2+1+1 ensembles…