With the increasing scale of High-Performance Computing (HPC) systems and a new awareness of the environmental impact of HPC, new strategies are required to improve the efficiency of resource usage on these systems. One such strategy is Dynamic Resource Management (DRM), which allows changing the resources assigned to a job dynamically during its execution. This increased flexibility in resource allocation and job scheduling can lead to improvements in several system efficiency metrics. Despite these benefits, DRM has not yet been established as a ready-to-use technology for production HPC systems. This is caused by the significant changes required in all the layers of the HPC system software stack, which are only achievable with an extensive and holistic co-design process between resource management software and applications. In this work, we demonstrate the applicability of a recently introduced, generic design approach for dynamic resources called Dynamic Processes with PSets (DPP), to enable DRM in realworld systems. To this end, we developed an exemplary, dynamic system software stack implementation following the DPP design principles throughout all layers. Based on this, we assess the applicability and performance of our approach using both synthetic benchmarks and job mixes consisting of several dynamic, real-world applications. On up to$\mathbf{1 0 0}$nodes, we measure moderate overheads for process reconfiguration in applications while significantly improving the system throughput and average job turnaround time compared to static scheduling in crowded system scenarios.

With the increase of complex scientific simulations driven by workflows and heterogeneous workload profiles, managing system resources effectively is essential for improving performance and system throughput, especially due to trends like heterogeneous HPC and deeply integrated systems with on-chip accelerators. For optimal resource utilization, dynamic resource allocation can improve productivity across all system and application levels, by adapting the applications' configurations to the system's resources. In this context, malleable jobs, which can change resources at runtime, can increase the system throughput and resource utilization while bringing various advantages for HPC users (e.g., shorter waiting time). Malleability has received much attention recently, even though it has been an active research area for almost two decades [1]. This paper presents the state-of-the-art of malleable implementations in HPC systems, targeting mainly malleability in compute and I/O resources. Based on our experiences, we state our current concerns and list future opportunities for research.

Job management software on peta- and exascale supercomputers continues to provide static resource allocations, from a program’s start until its end. Dynamic resource allocation and management is a research direction that has the potential to improve the efficiency of HPC systems and applications by dynamically adapting the resources of an application during its runtime. Resources can be adapted based on past, current or even future system conditions and matching optimization targets. However, the implementation of dynamic resource management is challenging as it requires support across many layers of the software stack, including the programming model.

Achieving high scalability for dynamical adaptive algorithms in HPC is a non-trivial task. In this contex, explicit data migration is typically applied which comes with several challenges such as criteria when and where to migrate data or additional bandwidth requirements. In our work, we use an alternative to data-migration, namely compute-migration on cache-coherent memory systems. We focus on migrating computational power for simulations with dynamical adaptive grids with invasive paradigm. An extension to our existing core-distribution scheduler is presented, distributing the cores depending on the requirements specified by each parallel program instance. In addition, a programming pattern for the hybrid parallel application code is presented to overcome deadlock issues created by applying invasive computing. We validate our approach with benchmark computations for a simulation with artificial workload and furthermore for a realistic scenario based on dynamical adaptive shallow water simulations. Those benchmarks are conducted within a hybrid parallelization environment on a 40-core HPC node. On this shared-memory system, our invasive approach results in faster execution times and higher hardware utilization than the non-invasive approach. Our approach can be applied in general to similar classes of simulations on dynamical adaptive grids.

In this work we present an approach for the parallelization of hyperbolic simulations on shared-memory architectures running on fully-adaptive grids. We tackle the parallelization problem with a dynamic sub-tree split- and join-approach by running computations on those split sub-trees in parallel using lightweight tasks. The traversal of sub-trees created by tree-splittings is built upon an inherently cache efficient approach for solving hyperbolic PDEs on dynamically adaptive triangular grids using a Sierpiński space filling curve. Our communication scheme among sub-trees stores the exchange-data to/from adjacent sub-trees in a consecutive memory area which is further utilized for an improved run-length-encoded data exchange. To give results for a concrete scenario, we implemented a solver for the shallow water equations which demands for fully-adaptive grid refinement and coarsening after each time-step. Our results give detailed statistics about optimization of the split size, parallelization overhead and also strong scalability results for a simulation running on multi-socket Intel and AMD architectures.