Expansion supercomputer DelftBlue ready for use!
Phase 2 hardware has been installed and is available for all (new) users as of 8 January 2024! Go to the documentation page on how to you use it: https://doc.dhpc.tudelft.nl/delftblue/#status-news-and-maintenance-announcements
Scientists increasingly need extensive computing power to solve complex problems in physics, mechanics and dynamics. The Delft High Performance Computing Centre (DHPC) deploys the infrastructure (hardware, software and staff) for TU Delft that is capable of complex analysis and modelling for researchers. At the same time we provide Bachelor, Master and PhD students with hands-on experience using the tools they will need in their careers.
Both high-performance simulations and high-performance data science are evolving rapidly and the combination of these techniques will lead to completely new insights into science and engineering, an increase in innovation, and the training of high-performance computing engineers for the future.
Due to the rapidly evolving hardware and tools for numerical simulations, HPC has significantly changed the way fundamental research is conducted at universities. Simulations not only replace experiments, but also add very valuable fundamental insights. We see the results in all kinds of disciplines, such as materials science, fluid dynamics, quantum mechanics, design optimization, big data mining and artificial intelligence.
06 maart 2024 09:00 t/m 16:37
Parallel computing with Python on DelftBlue 6 March 2024This is an introductory course to programming for the DelftBlue supercomputer. It introduces the main concepts of efficient and parallel computing with examples using Python.
16 maart 2024 12:30 t/m 13:15
[NA] Carlos Pérez Arancibia: Fast, high-order numerical evaluation of volume potentials via polynomial density interpolationThis talk outlines a novel class of high-order methods for the efficient numerical evaluation of volume potentials (VPs) defined by volume integrals over complex geometries. Inspired by the Density Interpolation Method (DIM) for boundary integral operators, the proposed methodology leverages Green’s third identity and a local polynomial interpolation of the density function to recast a given VP as a linear combination of surface-layer potentials and a volume integral with a regularized (bounded or smoother) integrand. The layer potentials can be accurately and efficiently evaluated inside and outside the integration domain using existing methods (e.g. DIM), while the regularized volume integral can be accurately evaluated by applying elementary quadrature rules to integrate over structured or unstructured domain decompositions without local numerical treatment at and around the kernel singularity. The proposed methodology is flexible, easy to implement, and fully compatible with well-established fast algorithms such as the Fast Multipole Method and H-matrices, enabling VP evaluations to achieve linearithmic computational complexity. To demonstrate the merits of the proposed methodology, we applied it to the Nyström discretization of the Lippmann-Schwinger volume integral equation for frequency-domain Helmholtz scattering problems in piecewise-smooth variable media.
19 april 2024 12:30 t/m 13:15
[NA] Alena Kopaničáková : Enhancing Training of Deep Neural Networks Using Multilevel and Domain Decomposition StrategiesThe training of deep neural networks (DNNs) is traditionally accomplished using stochastic gradient descent or its variants. While these methods have demonstrated certain robustness and accuracy, their convergence speed deteriorates for large-scale, highly ill-conditioned, and stiff problems, such as ones arising in scientific machine learning applications. Consequently, there is a growing interest in adopting more sophisticated training strategies that can not only accelerate convergence but may also enable parallelism, convergence control, and automatic selection of certain hyper-parameters.
In this talk, we propose to enhance the training of DNNs by leveraging nonlinear multilevel and domain decomposition strategies. We will discuss how to construct a multilevel hierarchy and how to decompose the parameters of the network by exploring the structure of the DNN architecture, properties of the loss function, and characteristics of the dataset. Furthermore, the dependency on a large number of hyper-parameters will be reduced by employing a trust-region globalization strategy. The effectiveness of the proposed training strategies will be demonstrated through a series of numerical experiments from the field of image classification and physics-informed neural networks.
 A. Kopaničáková, H. Kothari, G. Karniadakis and R. Krause. Enhancing training of physics-informed neural networks using domain-decomposition based preconditioning strategies. Under review, 2023.
 S. Gratton, A. Kopaničáková, and Ph. Toint. Multilevel Objective-Function-Free Optimization with an Application to Neural Networks Training. SIAM, Journal on Optimization (Accepted), 2023.
 A. Kopaničáková. On the use of hybrid coarse-level models in multilevel minimization methods. Domain Decomposition Methods in Science and Engineering XXVII (Accepted), 2023.
 A. Kopaničáková, and R. Krause. Globally Convergent Multilevel Training of Deep Residual Networks. SIAM Journal on Scientific Computing, 2022.