CloudHPCModelle

CloudHPCModelle is a term used to describe approaches for running high-performance computing workloads in cloud environments. They cover provisioning models such as on-demand, reserved, and spot/preemptible instances; deployment topologies including public cloud, private cloud, hybrid cloud, and multi-cloud; and access patterns ranging from batch processing to interactive or workflow-driven execution. Core stacks commonly involve batch schedulers like SLURM or PBS, or Kubernetes-based orchestration, and may run on virtual machines or bare-metal instances to reduce virtualization overhead.

Key components of CloudHPCModelle include high-performance compute resources with multi-core CPUs and accelerators such as GPUs or FPGAs, high-speed networking, and scalable storage options like parallel file systems and object stores. Storage and networking are paired with software environments capable of optimizing throughput, latency, and job scheduling, often with specialized drivers and libraries for scientific computing and data-intensive workloads.

Deployment patterns vary, with public cloud HPC as a service, hybrid configurations that burst to the cloud from on-prem clusters, and multi-cloud strategies that optimize price and performance across providers. Typical workloads span molecular dynamics, computational fluid dynamics, finite element analysis, genomics, large-scale AI training, and climate or weather simulations. These patterns benefit from elastic scaling, rapid provisioning, and access to diverse accelerators.

Benefits of CloudHPCModelle include improved scalability, flexibility, and cost efficiency through pay-as-you-go and spot-instance use. Challenges involve performance variability, data transfer and storage costs, licensing constraints for software, security and compliance considerations, and the complexity of orchestration and optimization across heterogeneous resources.

Governance emphasizes cost monitoring, service-level expectations, reproducibility, benchmarking, and data management practices to ensure predictable performance and traceable results across cloud environments.