Computing and Software for Big Science最新文献_第3页

Ntuple Wizard: An Application to Access Large-Scale Open Data from LHCb 一个从LHCb访问大规模开放数据的应用程序

Computing and Software for Big Science Pub Date : 2023-02-28 DOI: 10.1007/s41781-023-00099-5

C. Aidala, C. Burr, M. Cattaneo, D. Fitzgerald, A. Morris, S. Neubert, Donijor Tropmann

引用次数: 0

Accelerating Machine Learning Inference with GPUs in ProtoDUNE Data Processing. 在ProtoDUNE数据处理中使用GPU加速机器学习推理。

Computing and Software for Big Science Pub Date : 2023-01-01 Epub Date: 2023-10-27 DOI: 10.1007/s41781-023-00101-0

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

{"title":"Accelerating Machine Learning Inference with GPUs in ProtoDUNE Data Processing.","authors":"Tejin Cai, Kenneth Herner, Tingjun Yang, Michael Wang, Maria Acosta Flechas, Philip Harris, Burt Holzman, Kevin Pedro, Nhan Tran","doi":"10.1007/s41781-023-00101-0","DOIUrl":"https://doi.org/10.1007/s41781-023-00101-0","url":null,"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.","PeriodicalId":36026,"journal":{"name":"Computing and Software for Big Science","volume":"7 1","pages":"11"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10611601/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71414451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

GNN for Deep Full Event Interpretation and Hierarchical Reconstruction of Heavy-Hadron Decays in Proton-Proton Collisions. 质子-质子碰撞中重强子衰变的深度全事件解释和分层重建的GNN。

Computing and Software for Big Science Pub Date : 2023-01-01 Epub Date: 2023-11-17 DOI: 10.1007/s41781-023-00107-8

Julián García Pardiñas, Marta Calvi, Jonas Eschle, Andrea Mauri, Simone Meloni, Martina Mozzanica, Nicola Serra

{"title":"GNN for Deep Full Event Interpretation and Hierarchical Reconstruction of Heavy-Hadron Decays in Proton-Proton Collisions.","authors":"Julián García Pardiñas, Marta Calvi, Jonas Eschle, Andrea Mauri, Simone Meloni, Martina Mozzanica, Nicola Serra","doi":"10.1007/s41781-023-00107-8","DOIUrl":"10.1007/s41781-023-00107-8","url":null,"abstract":"The LHCb experiment at the Large Hadron Collider (LHC) is designed to perform high-precision measurements of heavy-hadron decays, which requires the collection of large data samples and a good understanding and suppression of multiple background sources. Both factors are challenged by a fivefold increase in the average number of proton-proton collisions per bunch crossing, corresponding to a change in the detector operation conditions for the LHCb Upgrade I phase, recently started. A further tenfold increase is expected in the Upgrade II phase, planned for the next decade. The limits in the storage capacity of the trigger will bring an inverse relationship between the number of particles selected to be stored per event and the number of events that can be recorded. In addition the background levels will rise due to the enlarged combinatorics. To tackle both challenges, we propose a novel approach, never attempted before in a hadronic collider: a Deep-learning based Full Event Interpretation (DFEI), to perform the simultaneous identification, isolation and hierarchical reconstruction of all the heavy-hadron decay chains per event. This strategy radically contrasts with the standard selection procedure used in LHCb to identify heavy-hadron decays, that looks individually at subsets of particles compatible with being products of specific decay types, disregarding the contextual information from the rest of the event. Following the DFEI approach, once the relevant particles in each event are identified, the rest can be safely removed to optimise the storage space and maximise the trigger efficiency. We present the first prototype for the DFEI algorithm, that leverages the power of Graph Neural Networks (GNN). This paper describes the design and development of the algorithm, and its performance in Upgrade I simulated conditions.","PeriodicalId":36026,"journal":{"name":"Computing and Software for Big Science","volume":"7 1","pages":"12"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10656322/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138463149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The ATLAS EventIndex ATLAS事件索引

Computing and Software for Big Science Pub Date : 2022-11-15 DOI: 10.1007/s41781-023-00096-8

D. Barberis, I. Alexandrov, E. Alexandrov, Z. Baranowski, L. Canali, E. Cherepanova, G. Dimitrov, A. Favareto, Alvaro Fernandez Casani, E. Gallas, Carlos García-Montoro, S. G. Hoz, J. Hrivnac, Alexander Iakovlev, A. Kazymov, M. Mineev, F. Prokoshin, G. Rybkin, J. Salt, Javier Sánchez, R. Sorokoletov, Rainer Töebbicke, P. Vasileva, M. V. Perez, Ruijun Yuan

引用次数: 1

Analyzing WLCG File Transfer Errors Through Machine Learning 通过机器学习分析WLCG文件传输错误

Computing and Software for Big Science Pub Date : 2022-10-22 DOI: 10.1007/s41781-022-00089-z

L. Clissa, M. Lassnig, L. Rinaldi

引用次数: 1

When, Where, and How to Open Data: a Personal Perspective 何时、何地以及如何开放数据:个人视角

Computing and Software for Big Science Pub Date : 2022-08-16 DOI: 10.1007/s41781-022-00090-6

B. Nachman

引用次数: 4

Fast Columnar Physics Analyses of Terabyte-Scale LHC Data on a Cache-Aware Dask Cluster 缓存感知Dask集群上tb级LHC数据的快速柱状物理分析

Computing and Software for Big Science Pub Date : 2022-07-18 DOI: 10.1007/s41781-023-00095-9

Niclas Eich, M. Erdmann, P. Fackeldey, B. Fischer, D. Noll, Y. Rath

引用次数: 2

Modelling Large-Scale Scientific Data Transfers 大规模科学数据传输建模

Computing and Software for Big Science Pub Date : 2022-07-06 DOI: 10.1007/s41781-022-00084-4

J. Bogado, M. Lassnig, F. Monticelli, Javier Díaz

引用次数: 1

Cait: Analysis Toolkit for Cryogenic Particle Detectors in Python Cait:Python中的低温粒子探测器分析工具包

Computing and Software for Big Science Pub Date : 2022-07-05 DOI: 10.1007/s41781-022-00092-4

F. Wagner, D. Bartolot, Damir Rizvanovic, F. Reindl, J. Schieck, W. Waltenberger

引用次数: 4

Future Trends in Nuclear Physics Computing 核物理计算的未来趋势

Computing and Software for Big Science Pub Date : 2022-06-22 DOI: 10.1007/s41781-022-00085-3

M. Diefenthaler, T. Wenaus

引用次数: 1