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Article ; Online: Accelerating Machine Learning Inference with GPUs in ProtoDUNE Data Processing.

Cai, Tejin / Herner, Kenneth / Yang, Tingjun / Wang, Michael / Acosta Flechas, Maria / Harris, Philip / Holzman, Burt / Pedro, Kevin / Tran, Nhan

Computing and software for big science

2023 Volume 7, Issue 1, Page(s) 11

Abstract: We study the performance of a cloud-based GPU-accelerated inference server to speed up event reconstruction in neutrino data batch jobs. Using detector data from the ProtoDUNE experiment and employing the standard DUNE grid job submission tools, we ... ...

Abstract	We study the performance of a cloud-based GPU-accelerated inference server to speed up event reconstruction in neutrino data batch jobs. Using detector data from the ProtoDUNE experiment and employing the standard DUNE grid job submission tools, we attempt to reprocess the data by running several thousand concurrent grid jobs, a rate we expect to be typical of current and future neutrino physics experiments. We process most of the dataset with the GPU version of our processing algorithm and the remainder with the CPU version for timing comparisons. We find that a 100-GPU cloud-based server is able to easily meet the processing demand, and that using the GPU version of the event processing algorithm is two times faster than processing these data with the CPU version when comparing to the newest CPUs in our sample. The amount of data transferred to the inference server during the GPU runs can overwhelm even the highest-bandwidth network switches, however, unless care is taken to observe network facility limits or otherwise distribute the jobs to multiple sites. We discuss the lessons learned from this processing campaign and several avenues for future improvements.
Language	English
Publishing date	2023-10-27
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2908677-2
ISSN	2510-2044 ; 2510-2036
ISSN (online)	2510-2044
ISSN	2510-2036
DOI	10.1007/s41781-023-00101-0
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Accelerating Machine Learning Inference with GPUs in ProtoDUNE Data Processing

Cai, Tejin / Herner, Kenneth / Yang, Tingjun / Wang, Michael / Flechas, Maria Acosta / Harris, Philip / Holzman, Burt / Pedro, Kevin / Tran, Nhan

2023

Abstract	We study the performance of a cloud-based GPU-accelerated inference server to speed up event reconstruction in neutrino data batch jobs. Using detector data from the ProtoDUNE experiment and employing the standard DUNE grid job submission tools, we attempt to reprocess the data by running several thousand concurrent grid jobs, a rate we expect to be typical of current and future neutrino physics experiments. We process most of the dataset with the GPU version of our processing algorithm and the remainder with the CPU version for timing comparisons. We find that a 100-GPU cloud-based server is able to easily meet the processing demand, and that using the GPU version of the event processing algorithm is two times faster than processing these data with the CPU version when comparing to the newest CPUs in our sample. The amount of data transferred to the inference server during the GPU runs can overwhelm even the highest-bandwidth network switches, however, unless care is taken to observe network facility limits or otherwise distribute the jobs to multiple sites. We discuss the lessons learned from this processing campaign and several avenues for future improvements. Comment: 13 pages, 9 figures, matches accepted version
Keywords	High Energy Physics - Experiment ; Computer Science - Distributed ; Parallel ; and Cluster Computing ; Physics - Data Analysis ; Statistics and Probability
Publishing date	2023-01-11
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection

Ćiprijanović, Aleksandra / Lewis, Ashia / Pedro, Kevin / Madireddy, Sandeep / Nord, Brian / Perdue, Gabriel N. / Wild, Stefan

2022

Abstract: In the era of big astronomical surveys, our ability to leverage artificial intelligence algorithms simultaneously for multiple datasets will open new avenues for scientific discovery. Unfortunately, simply training a deep neural network on images from ... ...

Abstract	In the era of big astronomical surveys, our ability to leverage artificial intelligence algorithms simultaneously for multiple datasets will open new avenues for scientific discovery. Unfortunately, simply training a deep neural network on images from one data domain often leads to very poor performance on any other dataset. Here we develop a Universal Domain Adaptation method DeepAstroUDA, capable of performing semi-supervised domain alignment that can be applied to datasets with different types of class overlap. Extra classes can be present in any of the two datasets, and the method can even be used in the presence of unknown classes. For the first time, we demonstrate the successful use of domain adaptation on two very different observational datasets (from SDSS and DECaLS). We show that our method is capable of bridging the gap between two astronomical surveys, and also performs well for anomaly detection and clustering of unknown data in the unlabeled dataset. We apply our model to two examples of galaxy morphology classification tasks with anomaly detection: 1) classifying spiral and elliptical galaxies with detection of merging galaxies (three classes including one unknown anomaly class); 2) a more granular problem where the classes describe more detailed morphological properties of galaxies, with the detection of gravitational lenses (ten classes including one unknown anomaly class). Comment: 3 figures, 1 table; accepted to Machine Learning and the Physical Sciences - Workshop at the 36th conference on Neural Information Processing Systems (NeurIPS)
Keywords	Astrophysics - Astrophysics of Galaxies ; Computer Science - Artificial Intelligence ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
Subject code	006
Publishing date	2022-11-01
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: DeepAdversaries

Ćiprijanović, Aleksandra / Kafkes, Diana / Snyder, Gregory / Sánchez, F. Javier / Perdue, Gabriel Nathan / Pedro, Kevin / Nord, Brian / Madireddy, Sandeep / Wild, Stefan M.

Examining the Robustness of Deep Learning Models for Galaxy Morphology Classification

2021

Abstract: With increased adoption of supervised deep learning methods for processing and analysis of cosmological survey data, the assessment of data perturbation effects (that can naturally occur in the data processing and analysis pipelines) and the development ... ...

Abstract	With increased adoption of supervised deep learning methods for processing and analysis of cosmological survey data, the assessment of data perturbation effects (that can naturally occur in the data processing and analysis pipelines) and the development of methods that increase model robustness are increasingly important. In the context of morphological classification of galaxies, we study the effects of perturbations in imaging data. In particular, we examine the consequences of using neural networks when training on baseline data and testing on perturbed data. We consider perturbations associated with two primary sources: 1) increased observational noise as represented by higher levels of Poisson noise and 2) data processing noise incurred by steps such as image compression or telescope errors as represented by one-pixel adversarial attacks. We also test the efficacy of domain adaptation techniques in mitigating the perturbation-driven errors. We use classification accuracy, latent space visualizations, and latent space distance to assess model robustness. Without domain adaptation, we find that processing pixel-level errors easily flip the classification into an incorrect class and that higher observational noise makes the model trained on low-noise data unable to classify galaxy morphologies. On the other hand, we show that training with domain adaptation improves model robustness and mitigates the effects of these perturbations, improving the classification accuracy by 23% on data with higher observational noise. Domain adaptation also increases by a factor of ~2.3 the latent space distance between the baseline and the incorrectly classified one-pixel perturbed image, making the model more robust to inadvertent perturbations. Comment: 20 pages, 6 figures, 5 tables; accepted in MLST
Keywords	Computer Science - Machine Learning ; Astrophysics - Astrophysics of Galaxies ; Computer Science - Artificial Intelligence ; Computer Science - Computer Vision and Pattern Recognition
Subject code	006
Publishing date	2021-12-28
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: GPU-Accelerated Machine Learning Inference as a Service for Computing in Neutrino Experiments.

Wang, Michael / Yang, Tingjun / Flechas, Maria Acosta / Harris, Philip / Hawks, Benjamin / Holzman, Burt / Knoepfel, Kyle / Krupa, Jeffrey / Pedro, Kevin / Tran, Nhan

Frontiers in big data

2021 Volume 3, Page(s) 604083

Abstract: Machine learning algorithms are becoming increasingly prevalent and performant in the reconstruction of events in accelerator-based neutrino experiments. These sophisticated algorithms can be computationally expensive. At the same time, the data volumes ... ...

Abstract	Machine learning algorithms are becoming increasingly prevalent and performant in the reconstruction of events in accelerator-based neutrino experiments. These sophisticated algorithms can be computationally expensive. At the same time, the data volumes of such experiments are rapidly increasing. The demand to process billions of neutrino events with many machine learning algorithm inferences creates a computing challenge. We explore a computing model in which heterogeneous computing with GPU coprocessors is made available as a web service. The coprocessors can be efficiently and elastically deployed to provide the right amount of computing for a given processing task. With our approach, Services for Optimized Network Inference on Coprocessors (SONIC), we integrate GPU acceleration specifically for the ProtoDUNE-SP reconstruction chain without disrupting the native computing workflow. With our integrated framework, we accelerate the most time-consuming task, track and particle shower hit identification, by a factor of 17. This results in a factor of 2.7 reduction in the total processing time when compared with CPU-only production. For this particular task, only 1 GPU is required for every 68 CPU threads, providing a cost-effective solution.
Language	English
Publishing date	2021-01-14
Publishing country	Switzerland
Document type	Journal Article
ISSN	2624-909X
ISSN (online)	2624-909X
DOI	10.3389/fdata.2020.604083
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: GNN-based end-to-end reconstruction in the CMS Phase 2 High-Granularity Calorimeter

Bhattacharya, Saptaparna / Chernyavskaya, Nadezda / Ghosh, Saranya / Gray, Lindsey / Kieseler, Jan / Klijnsma, Thomas / Long, Kenneth / Nawaz, Raheel / Pedro, Kevin / Pierini, Maurizio / Pradhan, Gauri / Qasim, Shah Rukh / Viazlo, Oleksander / Zehetner, Philipp

2022

Abstract: We present the current stage of research progress towards a one-pass, completely Machine Learning (ML) based imaging calorimeter reconstruction. The model used is based on Graph Neural Networks (GNNs) and directly analyzes the hits in each HGCAL endcap. ... ...

Abstract	We present the current stage of research progress towards a one-pass, completely Machine Learning (ML) based imaging calorimeter reconstruction. The model used is based on Graph Neural Networks (GNNs) and directly analyzes the hits in each HGCAL endcap. The ML algorithm is trained to predict clusters of hits originating from the same incident particle by labeling the hits with the same cluster index. We impose simple criteria to assess whether the hits associated as a cluster by the prediction are matched to those hits resulting from any particular individual incident particles. The algorithm is studied by simulating two tau leptons in each of the two HGCAL endcaps, where each tau may decay according to its measured standard model branching probabilities. The simulation includes the material interaction of the tau decay products which may create additional particles incident upon the calorimeter. Using this varied multiparticle environment we can investigate the application of this reconstruction technique and begin to characterize energy containment and performance. Comment: 5 pages, 5 figures, proceedings for the 20th International Workshop on Advanced Computing and Analysis Techniques in Physics Research
Keywords	Physics - Instrumentation and Detectors ; High Energy Physics - Experiment
Subject code	621
Publishing date	2022-03-02
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: New directions for surrogate models and differentiable programming for High Energy Physics detector simulation

Adelmann, Andreas / Hopkins, Walter / Kourlitis, Evangelos / Kagan, Michael / Kasieczka, Gregor / Krause, Claudius / Shih, David / Mikuni, Vinicius / Nachman, Benjamin / Pedro, Kevin / Winklehner, Daniel

2022

Abstract: The computational cost for high energy physics detector simulation in future experimental facilities is going to exceed the current available resources. To overcome this challenge, new ideas on surrogate models using machine learning methods are being ... ...

Abstract	The computational cost for high energy physics detector simulation in future experimental facilities is going to exceed the current available resources. To overcome this challenge, new ideas on surrogate models using machine learning methods are being explored to replace computationally expensive components. Additionally, differentiable programming has been proposed as a complementary approach, providing controllable and scalable simulation routines. In this document, new and ongoing efforts for surrogate models and differential programming applied to detector simulation are discussed in the context of the 2021 Particle Physics Community Planning Exercise (`Snowmass'). Comment: contribution to Snowmass 2021
Keywords	High Energy Physics - Phenomenology ; Computer Science - Machine Learning ; High Energy Physics - Experiment ; Physics - Computational Physics ; Physics - Instrumentation and Detectors
Publishing date	2022-03-15
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics.

Frontiers in big data

2021 Volume 3, Page(s) 598927

Abstract: Graph neural networks have been shown to achieve excellent performance for several crucial tasks in particle physics, such as charged particle tracking, jet tagging, and clustering. An important domain for the application of these networks is the FGPA- ... ...

Abstract	Graph neural networks have been shown to achieve excellent performance for several crucial tasks in particle physics, such as charged particle tracking, jet tagging, and clustering. An important domain for the application of these networks is the FGPA-based first layer of real-time data filtering at the CERN Large Hadron Collider, which has strict latency and resource constraints. We discuss how to design distance-weighted graph networks that can be executed with a latency of less than one μs on an FPGA. To do so, we consider a representative task associated to particle reconstruction and identification in a next-generation calorimeter operating at a particle collider. We use a graph network architecture developed for such purposes, and apply additional simplifications to match the computing constraints of Level-1 trigger systems, including weight quantization. Using the hls4ml library, we convert the compressed models into firmware to be implemented on an FPGA. Performance of the synthesized models is presented both in terms of inference accuracy and resource usage.
Language	English
Publishing date	2021-01-12
Publishing country	Switzerland
Document type	Journal Article
ISSN	2624-909X
ISSN (online)	2624-909X
DOI	10.3389/fdata.2020.598927
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: GPU-accelerated machine learning inference as a service for computing in neutrino experiments

Wang, Michael / Yang, Tingjun / Flechas, Maria Acosta / Harris, Philip / Hawks, Benjamin / Holzman, Burt / Knoepfel, Kyle / Krupa, Jeffrey / Pedro, Kevin / Tran, Nhan

2020

Abstract	Machine learning algorithms are becoming increasingly prevalent and performant in the reconstruction of events in accelerator-based neutrino experiments. These sophisticated algorithms can be computationally expensive. At the same time, the data volumes of such experiments are rapidly increasing. The demand to process billions of neutrino events with many machine learning algorithm inferences creates a computing challenge. We explore a computing model in which heterogeneous computing with GPU coprocessors is made available as a web service. The coprocessors can be efficiently and elastically deployed to provide the right amount of computing for a given processing task. With our approach, Services for Optimized Network Inference on Coprocessors (SONIC), we integrate GPU acceleration specifically for the ProtoDUNE-SP reconstruction chain without disrupting the native computing workflow. With our integrated framework, we accelerate the most time-consuming task, track and particle shower hit identification, by a factor of 17. This results in a factor of 2.7 reduction in the total processing time when compared with CPU-only production. For this particular task, only 1 GPU is required for every 68 CPU threads, providing a cost-effective solution. Comment: 15 pages, 5 figures, 2 tables
Keywords	Physics - Computational Physics ; Computer Science - Distributed ; Parallel ; and Cluster Computing ; High Energy Physics - Experiment ; Physics - Data Analysis ; Statistics and Probability
Subject code	006
Publishing date	2020-09-09
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Coffea -- Columnar Object Framework For Effective Analysis

Smith, Nicholas / Gray, Lindsey / Cremonesi, Matteo / Jayatilaka, Bo / Gutsche, Oliver / Hall, Allison / Pedro, Kevin / Acosta, Maria / Melo, Andrew / Belforte, Stefano / Pivarski, Jim

2020

Abstract: The coffea framework provides a new approach to High-Energy Physics analysis, via columnar operations, that improves time-to-insight, scalability, portability, and reproducibility of analysis. It is implemented with the Python programming language, the ... ...

Abstract	The coffea framework provides a new approach to High-Energy Physics analysis, via columnar operations, that improves time-to-insight, scalability, portability, and reproducibility of analysis. It is implemented with the Python programming language, the scientific python package ecosystem, and commodity big data technologies. To achieve this suite of improvements across many use cases, coffea takes a factorized approach, separating the analysis implementation and data delivery scheme. All analysis operations are implemented using the NumPy or awkward-array packages which are wrapped to yield user code whose purpose is quickly intuited. Various data delivery schemes are wrapped into a common front-end which accepts user inputs and code, and returns user defined outputs. We will discuss our experience in implementing analysis of CMS data using the coffea framework along with a discussion of the user experience and future directions. Comment: As presented at CHEP 2019
Keywords	Computer Science - Distributed ; Parallel ; and Cluster Computing ; High Energy Physics - Experiment
Subject code	005
Publishing date	2020-08-28
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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