Kokkos supporting for complex data discretization and unstructured meshes

Niveau de diplôme exigé : Thèse ou équivalent Contexte et atouts du poste PEPR NumPEx & KOKTAILS The transition to Exascale computing architectures requires a renewal of programming paradigms to efficiently leverage accelerators (GPUs, TPUs, and others). This transition presents a significant challenge for existing application codes, as a complete rewrite is often a massive undertaking. The KOKTAILS project aims to address these challenges by proposing an advanced programming environment that facilitates the porting of codes to heterogeneous architectures while ensuring performance portability. The KOKTAILS project aims to develop a sovereign software stack tailored for GPU-based Exascale supercomputing. It addresses the critical challenges of software portability and performance optimization across diverse hardware architectures, ensuring seamless adaptation of scientific applications to future computing infrastructures. By integrating and enhancing existing open-source frameworks, KOKTAILS will provide a robust middleware layer enabling French and European applications to fully exploit Exascale resources while reducing dependence on foreign software ecosystems. This project aligns fully with the PEPR NumPEx strategy, closely interacting with Exa-SofT's work on software tool evolution and Exa-DI to ensure integration into application demonstrators. By ensuring the sustainability of software developments and facilitating their adoption by a wide range of applications, KOKTAILS will directly contribute to France's digital sovereignty and to scientific and technological excellence in HPC. Kokkos Internationally, the United States has significantly invested in Exascale software development through initiatives like the Exascale Computing Project (ECP), which has focused on co-design efforts between hardware, software, and applications. Kokkos, an open-source C++ parallel programming model, has emerged as a leading solution for portable performance across heterogeneous architectures and is widely adopted in worldwide supercomputing centers. Europe has made progress in HPC software development through programs like EuroHPC and PEPR NumPEx, and needs to ensure that a production-ready software stack is ready for Exascale architectures that will be deployed in member states. Although the Kokkos ecosystem is mature, it lacks several key aspects to fully address the needs of the European computing communities. Porting legacy codes with complex data structures remain a significant challenge and although Kokkos is well-suited for GPUs, its use relies on advanced meta-programming, making its adoption challenging for some scientists. Mission confiée Unstructured and high-dimensional meshes pose challenges for GPU optimization due to irregular memory access, load imbalance, and inefficient parallelism. Techniques like Reverse Cuthill- McKee (RCM) reordering, optimal loop ordering, and hierarchical memory use aim to improve performance. Adaptive mesh partitioning based on connectivity strength also helps reduce load imbalance in domain decomposition. However, these strategies depend heavily on mesh topology, numerical methods, and hardware, so no one-size-fits-all solution exists. Profiling and adaptive tuning are essential to find optimal configurations. Libraries like GMlib, OP2, and TNL offer support for unstructured meshes on GPUs but lack tools for selecting the best optimization strategies. Future work should focus on auto-tuning frameworks integrated with portability layers like Kokkos to provide scalable, efficient solutions for Exascale computing. The KOKTAILS project will address these limitations by:   - Extending Kokkos with enhanced support for European architectures, ensuring its applicability in the French and European HPC landscape,   - Improving data structures in the Kokkos ecosystem to support specific meshes required in key French and European applications,   - Improving automatic code translation and transformation tool, to facilitate the migration of legacy scientific codes to modern GPU-optimized frameworks such as the Kokkos ecosystem.   - Addressing challenges in Python-Kokkos interoperability, enabling domain-specific scientists to leverage Kokkos through a Python interface and enabling also a seamless integration of Python codes ad AI models into C++ HPC codes for efficient execution on heterogeneous architectures. Principales activités Efficient mesh management is crucial for many scientific applications. We propose to develop optimized Kokkos data structures for high-dimensional or unstructured meshes. These data structures aim to reduce computational costs by leveraging optimized memory management for modern GPU-based architectures. The innovation lies in designing mesh data structures that are both portable and adaptable to the specific constraints of Exascale architectures, ensuring scalability and optimal efficiency.   - Some scientific applications - plasma physics, quantum simula