Conveners
Workloads and Metrics
- Markus Schulz (CERN)
Workloads and Metrics
- Simone Campana (CERN)
The HEP Benchmark Suite provides a comprehensive framework for evaluating the performance and power consumption of computing resources within the Worldwide LHC Computing Grid (WLCG), supporting its sustainability objectives. This presentation will outline the toolkit's capabilities and present examples where the suite has successfully identified opportunities to improve both energy efficiency...
During the HL-HLC era the WLCG, according to the current estimates, will be requested to provide millions of CPU years of computation annually. The largest single computing loads are particle tracking, hard scatter event generation and calorimetry simulation. Improving the computational performance of these loads is a very active research area that includes creation of GPU based codes and...
The rapid advancement of artificial intelligence (AI) technologies is driving transformative changes across various sectors, including High Energy Physics (HEP). While the energy consumption associated with AI systems poses a challenge to sustainability, the integration of AI within HEP offers significant benefits in terms of efficiency and modernization of the HEP computing model. This talk...
The CMS FlashSim simulation framework is an end-to-end ML based simulation that can speed up the time for production of analysis samples of several orders of magnitude with a limited loss of accuracy. Detailed event simulation at the LHC is taking a large fraction of computing budget. As the CMS experiment is adopting a common analysis level format, the NANOAOD, for a larger number of...
For HL-LHC, recent studies have shown that around 20% of the total computing resources will be used for Monte-Carlo simulations. Without efficiency improvements the computing needs of the experiments will not be met by the available resources. Motivating research avenues in the realm of GPU acceleration.
In this talk, we will present the current status of the GPU and vectorised version of the...
Improvements in algorithmic efficiency are, in principle, the ‘free-est’ sources of energy-efficiency gains, as reducing CPU time at a constant power clearly reduces total energy. However, it is not clear a priori that the same applies to porting parts of a code to special purpose accelerators such as GPUs.
We investigated this case for the Celeritas project’s GPU offloading for Geant4’s EM...
