# Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Tracking

arXiv: https://arxiv.org/abs/2103.06995 · HTML: https://mlsanity.com/paper/2103.06995/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 70 references that are in the map.
- First posted on arXiv 11 Mar 2021.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Geometric deep learning: going beyond Euclidean data](https://mlsanity.com/paper/1611.08097/) (2016)
- [Accelerating 3D Deep Learning with PyTorch3D](https://mlsanity.com/paper/2007.08501/) (2020)
- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [In-Datacenter Performance Analysis of a Tensor Processing Unit](https://mlsanity.com/paper/1704.04760/) (2017)
- [Interaction Networks for Learning about Objects, Relations and Physics](https://mlsanity.com/paper/1612.00222/) (2016)
- [The Effectiveness of Data Augmentation in Image Classification using Deep Learning](https://mlsanity.com/paper/1712.04621/) (2017)
- [Horovod: fast and easy distributed deep learning in TensorFlow](https://mlsanity.com/paper/1802.05799/) (2018)
- [Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis](https://mlsanity.com/paper/1802.09941/) (2018)
- [Smart Mining for Deep Metric Learning](https://mlsanity.com/paper/1704.01285/) (2017)
- [hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices](https://mlsanity.com/paper/2103.05579/) (2021)
- [Dissecting the Graphcore IPU Architecture via Microbenchmarking](https://mlsanity.com/paper/1912.03413/) (2019)
- [Learning representations of irregular particle-detector geometry with distance-weighted graph networks](https://mlsanity.com/paper/1902.07987/) (2019)
- [MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks](https://mlsanity.com/paper/2101.08578/) (2021)
- [Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs](https://mlsanity.com/paper/2012.01563/) (2020)
- [Distributed Training of Graph Convolutional Networks](https://mlsanity.com/paper/2007.06281/) (2020)
- [TFLMS: Large Model Support in TensorFlow by Graph Rewriting](https://mlsanity.com/paper/1807.02037/) (2018)
- [Track Seeding and Labelling with Embedded-space Graph Neural Networks](https://mlsanity.com/paper/2007.00149/) (2020)
- [GPU coprocessors as a service for deep learning inference in high energy physics](https://mlsanity.com/paper/2007.10359/) (2020)
- [Beyond 4D Tracking: Using Cluster Shapes for Track Seeding](https://mlsanity.com/paper/2012.04533/) (2020)
- [Hashing and metric learning for charged particle tracking](https://mlsanity.com/paper/2101.06428/) (2021)

## Built on by (most cited first, 30 of 30)

- [Charged particle tracking via edge-classifying interaction networks](https://mlsanity.com/paper/2103.16701/) (2021)
- [Finetuning Foundation Models for Joint Analysis Optimization](https://mlsanity.com/paper/2401.13536/) (2024)
- [Graph Neural Networks for Charged Particle Tracking on FPGAs](https://mlsanity.com/paper/2112.02048/) (2021)
- [The Tracking Machine Learning challenge : Throughput phase](https://mlsanity.com/paper/2105.01160/) (2021)
- [Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges](https://mlsanity.com/paper/2203.12852/) (2022)
- [LL-GNN: Low Latency Graph Neural Networks on FPGAs for High Energy Physics](https://mlsanity.com/paper/2209.14065/) (2022)
- [A Language Model for Particle Tracking](https://mlsanity.com/paper/2402.10239/) (2024)
- [High Pileup Particle Tracking with Object Condensation](https://mlsanity.com/paper/2312.03823/) (2023)
- [Hierarchical Graph Neural Networks for Particle Track Reconstruction](https://mlsanity.com/paper/2303.01640/) (2023)
- [End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networks](https://mlsanity.com/paper/2204.01681/) (2022)
- [FM4NPP: A Scaling Foundation Model for Nuclear and Particle Physics](https://mlsanity.com/paper/2508.14087/) (2025)
- [A General Framework for Robust G-Invariance in G-Equivariant Networks](https://mlsanity.com/paper/2310.18564/) (2023)
- [TrackFormers: In Search of Transformer-Based Particle Tracking for the High-Luminosity LHC Era](https://mlsanity.com/paper/2407.07179/) (2024)
- [Learning Tree Structures from Leaves For Particle Decay Reconstruction](https://mlsanity.com/paper/2208.14924/) (2022)
- [PASCL: Supervised Contrastive Learning with Perturbative Augmentation for Particle Decay Reconstruction](https://mlsanity.com/paper/2402.11538/) (2024)
- [ColliderML: The First Release of an OpenDataDetector High-Luminosity Physics Benchmark Dataset](https://mlsanity.com/paper/2512.15230/) (2025)
- [Graph Structure from Point Clouds: Geometric Attention is All You Need](https://mlsanity.com/paper/2307.16662/) (2023)
- [Graph Neural Network for Neutrino Physics Event Reconstruction](https://mlsanity.com/paper/2403.11872/) (2024)
- [Prompting Particle Physics: Tokenized Multi-modal Foundation Models for Combinatorially Many Tasks](https://mlsanity.com/paper/2609.31862/) (2026)
- [Combined track finding with GNN & CKF](https://mlsanity.com/paper/2401.16016/) (2024)
- [Towards Understanding Sensitive and Decisive Patterns in Explainable AI: A Case Study of Model Interpretation in Geometric Deep Learning](https://mlsanity.com/paper/2407.00849/) (2024)
- [EggNet: An Evolving Graph-based Graph Attention Network for Particle Track Reconstruction](https://mlsanity.com/paper/2407.13925/) (2024)
- [Comparative Analysis of FPGA and GPU Performance for Machine Learning-Based Track Reconstruction at LHCb](https://mlsanity.com/paper/2502.02304/) (2025)
- [Scaling Graph Neural Networks for Particle Track Reconstruction](https://mlsanity.com/paper/2504.04670/) (2025)
- [Locality-Sensitive Hashing-Based Efficient Point Transformer for Charged Particle Reconstruction](https://mlsanity.com/paper/2510.07594/) (2025)
- [Exploring End-to-end Differentiable Neural Charged Particle Tracking -- A Loss Landscape Perspective](https://mlsanity.com/paper/2407.13420/) (2024)
- [HEPTv2: End-to-End Efficient Point Transformer for Charged Particle Reconstruction](https://mlsanity.com/paper/2606.20437/) (2026)
- [Learning Standard Model structure from LHC data with Riemannian flow matching](https://mlsanity.com/paper/2607.16144/) (2026)
- [Goal-Persistent Coding Agents as Scientific Performance Engineers: A Fixed-Radius Nearest-Neighbor Case Study](https://mlsanity.com/paper/2609.31980/) (2026)
- [Using Line Segment Tracking to enable ML-based Track Reconstruction in CMS](https://mlsanity.com/paper/2610.15963/) (2026)

## Abstract (arXiv)

The Exa.TrkX project has applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking. Exa.TrkX's tracking pipeline groups detector measurements to form track candidates and filters them. The pipeline, originally developed using the TrackML dataset (a simulation of an LHC-inspired tracking detector), has been demonstrated on other detectors, including DUNE Liquid Argon TPC and CMS High-Granularity Calorimeter. This paper documents new developments needed to study the physics and computing performance of the Exa.TrkX pipeline on the full TrackML dataset, a first step towards validating the pipeline using ATLAS and CMS data. The pipeline achieves tracking efficiency and purity similar to production tracking algorithms. Crucially for future HEP applications, the pipeline benefits significantly from GPU acceleration, and its computational requirements scale close to linearly with the number of particles in the event.