GPU coprocessors as a service for deep learning inference in high energy physics
arXiv:2007.10359 · doi:10.1088/2632-2153/abec21
Abstract
In the next decade, the demands for computing in large scientific experiments are expected to grow tremendously. During the same time period, CPU performance increases will be limited. At the CERN Large Hadron Collider (LHC), these two issues will confront one another as the collider is upgraded for high luminosity running. Alternative processors such as graphics processing units (GPUs) can resolve this confrontation provided that algorithms can be sufficiently accelerated. In many cases, algorithmic speedups are found to be largest through the adoption of deep learning algorithms. We present a comprehensive exploration of the use of GPU-based hardware acceleration for deep learning inference within the data reconstruction workflow of high energy physics. We present several realistic examples and discuss a strategy for the seamless integration of coprocessors so that the LHC can maintain, if not exceed, its current performance throughout its running.
arXiv comment26 pages, 7 figures, 2 tables
References in corpus (43)
- Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC
- Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC
- Observation of a new boson with mass near 125 GeV in pp collisions at sqrt(s) = 7 and 8 TeV
- Electron and photon performance measurements with the ATLAS detector using the 2015-2017 LHC proton-proton collision data
- ParticleNet: Jet Tagging via Particle Clouds
- Dark Matter Benchmark Models for Early LHC Run-2 Searches: Report of the ATLAS/CMS Dark Matter Forum
- Deep Learning and its Application to LHC Physics
- Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning
- Anomaly Detection with Density Estimation
- Classification without labels: Learning from mixed samples in high energy physics
- Deep learning in color: towards automated quark/gluon jet discrimination
- Searching for New Physics with Deep Autoencoders
- Searching for long-lived particles beyond the Standard Model at the Large Hadron Collider
- Anomaly Detection for Resonant New Physics with Machine Learning
- The Machine Learning Landscape of Top Taggers
- Extending the Bump Hunt with Machine Learning
- QCD or What?
- Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques
- Search for new physics in final states with an energetic jet or a hadronically decaying W or Z boson and transverse momentum imbalance at 13 TeV
- Simulation Assisted Likelihood-free Anomaly Detection
- Search for high mass dijet resonances with a new background prediction method in proton-proton collisions at 13 TeV
- Learning representations of irregular particle-detector geometry with distance-weighted graph networks
- Machine and Deep Learning Applications in Particle Physics
- Electron and photon energy calibration with the ATLAS detector using 2015-2016 LHC proton-proton collision data
- JEDI-net: a jet identification algorithm based on interaction networks
- Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics
- ABCNet: An attention-based method for particle tagging
- Allen: A high level trigger on GPUs for LHCb
- MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks
- Interaction networks for the identification of boosted decays
- FPGA-accelerated machine learning inference as a service for particle physics computing
- A deep neural network to search for new long-lived particles decaying to jets
- End-to-End Jet Classification of Quarks and Gluons with the CMS Open Data
- Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph and image data
- Search for low mass vector resonances decaying into quark-antiquark pairs in proton-proton collisions at 13 TeV
- HEPCloud, a New Paradigm for HEP Facilities: CMS Amazon Web Services Investigation
- Real-time data processing in the ALICE High Level Trigger at the LHC
- Measurement of charged particle spectra in minimum-bias events from proton-proton collisions at 13 TeV
- Bringing heterogeneity to the CMS software framework
- GPU-accelerated machine learning inference as a service for computing in neutrino experiments
- Running a Pre-Exascale, Geographically Distributed, Multi-Cloud Scientific Simulation
- HEPCloud, an Elastic Hybrid HEP Facility using an Intelligent Decision Support System
- Real-time data processing with GPUs in high energy physics
Cited by in corpus (5)
- MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks
- Charged particle tracking via edge-classifying interaction networks
- Portable acceleration of CMS computing workflows with coprocessors as a service
- Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Tracking
- SuperSONIC: Cloud-Native Infrastructure for ML Inferencing