Delft High Performance Computing Centre

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DelftBlue is available!

Researchers can log in with their NetID (go to our documentation page for instructions) and students can request for an account via the Self Service Portal. 

About DHPC

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.

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News

24 November 2023

Expansion DelftBlue ready for use in January 2024

02 November 2023

Martin van Gijzen has been appointed as the new Scientific Director of the DHPC

Effective September 1, Martin van Gijzen has been appointed as the new Scientific Director of the Delft High Performance Computing Centre (DHPC). Van Gijzen brings a wealth of knowledge and expertise to the centre, and he is committed to further developing the centre into a knowledge hub and go-to portal for high performance computing expertise at the TU Delft.

25 August 2023

Fujitsu and TU Delft explore the horizon of High Performance Computing

In an era where computing power continues to grow at an exponential rate, the importance of high-performance computing (HPC) cannot be understated. Leading the charge in the HPC market is Fujitsu. This week, Professor Kees Vuik, chair of Delf High Performance Computing Centre, visited Fujitsu's headquarters to meet with the CTO Vivek Mahajan, discussing the future of HPC and potential applications of quantum computing.

Agenda

11 December 2023 09:30 till 16:30

Command Line and DelftBlue Basics December 2023

After taking this course, participants will be able to fully engage in remote HPC systems using the command line.

15 December 2023 12:30 till 13:15

[NA] Stefan Kurz: Observers in relativistic electrodynamics

"We introduce a relativistic splitting structure to map fields and equations of electromagnetism from four-dimensional spacetime to three-dimensional observer's space. We focus on a minimal set of mathematical structures that are directly motivated by the language of the physical theory. Space-time, world-lines, time translation, space platforms, and time synchronization all find their mathematical counterparts. The splitting structure is defined without recourse to coordinates or frames. This is noteworthy since, in much of the prevalent literature, observers are identified with adapted coordinates and frames. Among the benefits of the approach is a concise and insightful classification of observers. The application of the framework to Schiff's ""Question in General Relativity"" [1] further illustrates the advantages of the framework, enabling a compact, yet profound analysis of the problem at hand. 

[1] Schiff, L. I. ""A question in general relativity."" Proceedings of the National Academy of Sciences 25.7 (1939): 391-395.
Consider two concentric spheres with equal and opposite total charges uniformly distributed over their surfaces. When the spheres are at rest, the electric and magnetic fields outside the spheres vanish. [...] Then an observer traveling in a circular orbit around the spheres should find no field, for since all of the components of the electromagnetic field tensor vanish in one coordinate system, they must vanish in all coordinate systems. On the other hand, the spheres are rotating with respect to this observer, and so he should experience a magnetic field. [...] It is clear in the above arrangement that an observer A at rest with respect to the spheres does not obtain the same results from physical experiments as an observer B who is rotating about the spheres."

19 January 2024 12:30 till 13:15

[NA] Jakob Zech: Nonparametric Distribution Learning via Neural ODEs

In this talk, we explore approximation properties and statistical aspects of Neural Ordinary Differential Equations (Neural ODEs). Neural ODEs are a recently established technique in computational statistics and machine learning, that can be used to characterize complex distributions. Specifically, given a fixed set of independent and identically distributed samples from a target distribution, the goal is either to estimate the target density or to generate new samples. We first investigate the regularity properties of the velocity fields used to push forward a reference distribution to the target. This analysis allows us to deduce approximation rates achievable through neural network representations. We then derive a concentration inequality for the maximum likelihood estimator of general ODE-parametrized transport maps. By merging these findings, we are able to determine convergence rates in terms of both the network size and the number of required samples from the target distribution. Our discussion will particularly focus on target distributions within the class of positive \(C^k\) densities on the \(d\)-dimensional unit cube \([0,1]^d\).
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High Performance Computing has landed in Delft