Speakers
Description
Optimizing the inference of Graph Neural Networks (GNNs) for track finding is very important for improving how well particle collision event reconstruction works. In high-energy physics experiments, like at the Large Hadron Collider (LHC), detectors generate a ton of complicated and noisy data from particles colliding at extremely high speeds. Track finding is about reconstructing the paths of particles based on this data, and GNNs help by representing the hits from the detector as a graph. In this graph, the hits are nodes, and the edges show possible connections between them. To speed up the process and make real-time event reconstruction possible, optimizing how GNNs perform during inference is key.
One of the big challenges is reducing the computational workload. GNN models used for track finding can be slow because the data is large and complex. It is essential to adjust the model architecture to reduce computational complexity while keeping track reconstruction accurate. Cutting down on unnecessary calculations can make things faster without hurting performance too much. Additional techniques like pruning and quantization can also contribute. Pruning gets rid of unnecessary connections in the network, while quantization reduces the precision of the model's weights, cutting down on memory usage and making the GNN run more efficiently.
Using hardware accelerators like GPUs or TPUs is another big part of speeding up GNN inference. These accelerators allow for parallel processing, which is a huge boost when working with large datasets. By taking advantage of the power of GPUs, the process of running GNNs can be sped up enough to handle data in real-time, or close to it. Combining these model optimization strategies with hardware acceleration can really improve how well GNNs perform when reconstructing particle trajectories.
| Focus areas | HEP |
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