Speaker
Description
Detailed simulation is one of the most expensive tasks, in terms of time and computing resources for High Energy Physics experiments. The need for simulated events will dramatically increase for the next generation experiments, like the ones that will run at the High Luminosity LHC. The computing model must evolve and in this context, alternative fast simulation solutions are being studied. 3DGAN represent a successful example across the several R&D activities focusing on the use of deep generative models to particle detector simulation: physics results in terms of agreement to standard Monte Carlo techniques are already very promising. Optimisation of the computing resources needed to train these models, and consequently to deploy them efficiently during the inference phase will be essential to exploit the added-value of their full capabilities.
In this context, CERN openlab has a collaboration with the researchers at SHREC at the University of Florida and with Intel to accelerate the 3DGAN inferencing stage using FPGAs. This contribution will describe the efforts ongoing at the University of Florida to develop an efficient heterogeneous computing (HGC) framework, CPUs integrated with accelerators such as GPUs and FPGAs, in order to accelerate Deep Learning. The HGC framework uses Intel distribution of OpenVINO, running on an Intel Programmable Acceleration Card (PAC) equipped with an Arria 10 GX FPGA.
Integration of the 3DGAN use case in the HGC framework has required development and optimisation of new FPGA primitives using the Intel Deep Learning Acceleration (DLA) development suite.
A number of details of this work and preliminary results will be presented, specifically in terms of speedup, stimulating a discussion for future development.
| Consider for promotion | Yes |
|---|
