Adapt and Scale Your Data Center for HPC Workloads
A Practical Path to Transform Existing Facilities into HPC-Ready Data Centers
How AHEAD, Dell, and NVIDIA enable organizations to adapt their existing infrastructure for HPC workloads
Although AI has taken the spotlight recently, the demand for high performance computing (HPC) workloads like simulations, modeling, and analytics is also growing faster than many existing environments can accommodate. In fact, HPC workloads and dense enterprise workload consolidation architectures have similar power densities as AI workloads, which means they can strain existing power, cooling, and space capacity in much the same way.
The trend toward increasing power density per rack is even impacting traditional compute. For example, a fully populated 42U dual-socket enterprise server rack now draws 40kW or more, up sharply from the sub-15kW racks that were typical for enterprise computing in the past. This means data centers need new approaches to handle modern workloads regardless of whether AI or HPC is the primary driver.
The good news is HPC readiness does not require an all-at-once infrastructure replacement or an entirely new data center. Organizations can create an adaptable foundation by understanding their current constraints, separating workloads by need, adding capacity in modular waves, modernizing data and networking, and embedding lifecycle and operational practices that make future expansion predictable.
According to a recent report from IDC, AI and HPC workloads may also share data center space with other types of lower-power density workloads to make the best use of total available facility power capacity. This means existing facilities can be adapted to more easily run various types of infrastructure as business needs evolve to optimize the overall efficiency of enterprise infrastructure.
In this whitepaper, we’ll outline a practical roadmap to assess, prioritize, pilot, integrate, operate, and expand HPC capacity at existing facilities. This approach can lead to more usable compute capacity, better utilization, lower deployment risk, and a clearer path to future workloads.