Speaker
Description
With the upcoming upgrade of High Luminosity LHC, the need for computation
power will increase in the ATLAS trigger system by more than an order of
magnitude. Therefore, new particle track reconstruction techniques are explored
by the ATLAS collaboration, including the usage of Graph Neural Networks (GNN).
The project focusing on that research, GNN4ITk, considers several heterogeneous
computing options, including the usage of Graphics Processing Units (GPU). The
framework can reconstruct tracks with high efficiency, however, the computing
requirements of the pipeline are high. We will report on the efforts to reduce
the memory consumption and inference time enough to enable the usage of
commercially available and affordable GPUs for the future ATLAS trigger system
while maintaining high tracking performance.
