Speakers
Description
Over the past several years, machine learning algorithms at the Large Hadron Collider have become increasingly more prevalent. Because of their highly parallelized design, Machine Learning-based algorithms can be sped up dramatically when using coprocessors, such as GPUs. With increasing computational demands coming from future LHC upgrades, there is a need to enhance the overall computational power of the next generation of LHC reconstruction. In this talk, we demonstrate a strategy to port deep learning algorithms to GPUs efficiently. By exploiting the as-a-service paradigm to port algorithms to GPU, we are able to optimally use GPU resources, allowing for a path towards efficient GPU adoption at the LHC as more algorithms become parallelizable. In this talk, we present this path and demonstrate an end-to-end workflow with current reconstruction using the Compact Muon Solenoid.
