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 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. In short, each code presented between one and six different regions. The selected regions address the main functions of the different codes, providing large potential for total wall time gains in the light of future optimisations. One aspect all codes and regions focus on is the optimization of the different communication and work load balancing schemes since this is a crucial component towards the exa-scale.
The identification of the modules and kernels is the first step towards the further development of the different codes.
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.
This deliverable aims to provide performance assessments of the SPACE CoE codes and identify the regions of the codes that can be potentially extracted as mini-applications or kernels and optimized in the following activities of the WP1 and WP2.
To evaluate the scalability and efficiency of specific performance aspects in the SPACE CoE parallel codes, we use a performance model and analysis methodology developed within the POP and POP2 Centers of Excellence.
This approach is crucial for main reasons. The first one is that the POP methodology can be considered a standardized approach to evaluate the performance of parallel codes and, as such, it allows one to compare the parallel performance metrics between different applications coming from different scientific domains and also using different programming models. Furthermore, we are immediately ready to collaborate in a very efficient way with POP3 CoE, once an in-depth analysis will start, as we can directly provide the traces produced in the frame of this study to POP experts.
In astrophysics and cosmology (A&C), high-performance computing (HPC)-based numerical simulations are invaluable
instruments to support scientific discovery.
The efficient and effective exploitation of exascale (and beyond) computing capabilities will be key to paving the way for scientific discovery in this scientific domain, but requires a coordinated effort towards adapting A&C codes for space applications on exascale HPC systems.
The collaboration plan defines the common objective and activities including milestones with complementary grants. This document 6.1 encloses the details of the collaboration with other CoEs (as appointed by Euro-HPC and coordinated by CASTIEL-2) as well as a number of dissemination and training activities. From code development and profiling activities, in fact, SPACE identified a number of dissemination topics built from technical know-how which are likely to be relevant in other research area, such as: i) Parallelization, data communication and integration with MPI, ii) memory layout of multi-dimensional arrays, iii) Energy efficiency, iv) Artificial Intelligence and Machine Learning, v) High Performance visualization. These topics will be addressed in training workshop and other events. In this framework, SPACE will also collaborate with NCC and hosting entities in order to foster the network and get support for event organization.