Replicating your on-premises environment is usually unnecessary and not optimal when you migrate workloads to the cloud. With cloud services, HPC workloads can run in new ways using cloud-native design patterns and solutions. Automating HPC cluster deployment allows you to quickly end one compute cluster and launch a new one.
Testing your workload in the cloud is the best way to know how it will perform. There are a lot of complex HPC applications, and you can’t run a simple test on their memory, CPU, and network patterns.
In addition, your infrastructure requirements depend on the algorithms you use as well as the size and complexity of your models. As a result, generic benchmarks can’t reliably predict HPC performance. In the cloud, you only pay for what you use so that you can create a realistic proof of concept. One advantage of a cloud-based platform is that you can run a full-scale test before you migrate.
Cost vs. Time
With high-performance computing architecture, you can analyze performance based on time and cost. You should optimize workloads that aren’t time-sensitive for cost. The least expensive way to run non-time-critical workloads is with spot or preemptible instances. Conversely, for time-critical workloads, performance should take precedence over cost optimization. In this case, you should pick the instance type, procurement model, and cluster size with the quickest execution time. When comparing cloud platforms, you should consider non-computing factors such as provisioning, staging data, or time spent in queues.