Speaker
Description
The upcoming High-Luminosity Large Hadron Collider (HL-LHC) era will present significant computational challenges, demanding a substantial increase in data processing for the WLCG experiments at CERN. To meet these needs the WLCG is exploring strategies for resource optimization. This includes a paradigm shift towards heterogeneous hardware, recognizing that GPUs are superior to CPUs for certain applications especially if they are highly parallelizable. A key strategy is the opportunistic on-demand integration of High-Performance Computing (HPC) centers, which host vast pools of GPUs. This approach allows for dynamic, on-demand access to powerful resources, avoiding the cost of hosting and maintaining the GPU infrastructure as well as inefficient utilization during low demand time periods.
New developments in this direction are the successful integration of GPU resources from the HoreKa HPC center at KIT, part of Germany's National High Performance Computing Alliance (NHR). Using orchestration tools like COBalD and TARDIS, which now support CPU and GPU allocation, we have demonstrated the capability to run ATLAS and CMS workloads on HoreKa's powerful GPU partitions. Initial tests, including centrally submitted workflows and user analysis jobs, have confirmed the technical feasibility of this approach.