This document outlines the current achievements and ongoing efforts within the SPACE CoE. The primary focus is on optimising and porting kernels or modules of the codes involved in SPACE CoE to GPU architectures and adapting them for efficient use on EuroHPC JU clusters. These advancements are crucial for addressing the increasing computational demands of large-scale, long-duration astrophysical simulations. The document provides detailed updates for each code involved in the project, covering the current status of GPU porting, optimisations, CI/CD implementation, and performance on EuroHPC JU clusters. All the seven codes have an open-source/public repository, allowing the progress within the code to be tracked in the respective repositories. The combined efforts in these areas demonstrate the project’s commitment to advancing HPC capabilities and supporting the computational needs of astrophysical research.
This document provides high-level descriptions for each code in the SPACE CoE. Within this code description the focus is on the main algorithms, which are used for each code. These variety of algorithms range from Smoothed-Particle Hydrodynamics, Adaptive-Mesh-Refinements, different Runge-Kutta schemes to Particle in Cell algorithms. In addition, the modules and kernels identified by each code for optimization are described as well as how the mini-apps will be created for each code.
The main goal of this report is to provide SPACE CoE codes scalability on JU systems, performance assessments and identification of the regions of the codes that can be potentially extracted as mini-applications or kernels and optimized in the following project activities. To evaluate the scalability and efficiency of specific performance aspects in the SPACE CoE parallel codes, a performance model and analysis methodology developed within the POP and POP2 Centres of Excellence is used.
This document encloses the details of the collaboration with other Centres of Excellence (as pointed by Euro-HPC and coordinated by CASTIEL2) as well as a number of dissemination and training activities. In this framework, SPACE will collaborate with National Competence Centres for High Performance Computing (NCC) and hosting entities in order to foster the network and get support for event organization.
This deliverable is a report on requirements and use cases for the SPACE Machine Learning and visualization tools, and topology-aware workflows for modular High Performance Computing (HPC) applications. The use cases and requirements have been collected to better identify post-processing analysis for the simulation data products and to integrate run-time modules suitable for coupling the exascale applications with visualization tools (such as VisIVO, Blender and Paraview) and Machine Learning techniques including representation learning, generative AI and Convolutional Neural Networks.
Energy efficiency is defined as the ratio of performance (floating-point operations per second) per Watt consumed by the application. In this deliverable, the energy efficiency of SPACE CoE applications has been evaluated with the aim of improving it by changing the selected power knobs of the underlying hardware.
This document illustrates several scientific use cases in astrophysics that demonstrate the power of data-driven research. The selected scientific cases have two main characteristics.
On the one hand, they represent current cutting-edge problems in the respective research fields and allow us to examine how different types of data, including observational, simulated, and experimental data, are used to address pressing questions in astrophysics. On the other hand, they pose relevant computational challenges to the CoE codes and stress their capability to scale in several respects.