Computing and Software for Big Science最新文献_第2页

Potential of the Julia Programming Language for High Energy Physics Computing 高能物理计算中Julia编程语言的潜力

Computing and Software for Big Science Pub Date : 2023-10-05 DOI: 10.1007/s41781-023-00104-x

Jonas Eschle, Tamás Gál, Mosè Giordano, Philippe Gras, Benedikt Hegner, Lukas Heinrich, Uwe Hernandez Acosta, Stefan Kluth, Jerry Ling, Pere Mato, Mikhail Mikhasenko, Alexander Moreno Briceño, Jim Pivarski, Konstantinos Samaras-Tsakiris, Oliver Schulz, Graeme Andrew Stewart, Jan Strube, Vassil Vassilev

{"title":"Potential of the Julia Programming Language for High Energy Physics Computing","authors":"Jonas Eschle, Tamás Gál, Mosè Giordano, Philippe Gras, Benedikt Hegner, Lukas Heinrich, Uwe Hernandez Acosta, Stefan Kluth, Jerry Ling, Pere Mato, Mikhail Mikhasenko, Alexander Moreno Briceño, Jim Pivarski, Konstantinos Samaras-Tsakiris, Oliver Schulz, Graeme Andrew Stewart, Jan Strube, Vassil Vassilev","doi":"10.1007/s41781-023-00104-x","DOIUrl":"https://doi.org/10.1007/s41781-023-00104-x","url":null,"abstract":"Abstract Research in high energy physics (HEP) requires huge amounts of computing and storage, putting strong constraints on the code speed and resource usage. To meet these requirements, a compiled high-performance language is typically used; while for physicists, who focus on the application when developing the code, better research productivity pleads for a high-level programming language. A popular approach consists of combining Python, used for the high-level interface, and C++, used for the computing intensive part of the code. A more convenient and efficient approach would be to use a language that provides both high-level programming and high-performance. The Julia programming language, developed at MIT especially to allow the use of a single language in research activities, has followed this path. In this paper the applicability of using the Julia language for HEP research is explored, covering the different aspects that are important for HEP code development: runtime performance, handling of large projects, interface with legacy code, distributed computing, training, and ease of programming. The study shows that the HEP community would benefit from a large scale adoption of this programming language. The HEP-specific foundation libraries that would need to be consolidated are identified.","PeriodicalId":36026,"journal":{"name":"Computing and Software for Big Science","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135482671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Jet Energy Calibration with Deep Learning as a Kubeflow Pipeline 以深度学习为Kubeflow管道的射流能量校准

Computing and Software for Big Science Pub Date : 2023-08-23 DOI: 10.1007/s41781-023-00103-y

D. Holmberg, D. Golubović, Henning Kirschenmann

引用次数: 0

Convergent Approaches to AI Explainability for HEP Muonic Particles Pattern Recognition HEPμ介子模式识别中AI解释性的收敛方法

Computing and Software for Big Science Pub Date : 2023-08-03 DOI: 10.1007/s41781-023-00102-z

Leandro Maglianella, Lorenzo Nicoletti, S. Giagu, Christian Napoli, Simone Scardapane

引用次数: 0

Lightweight Integration of a Data Cache for Opportunistic Usage of HPC Resources in HEP Workflows 数据缓存的轻量级集成，用于HEP工作流中HPC资源的机会使用

Computing and Software for Big Science Pub Date : 2023-07-05 DOI: 10.1007/s41781-023-00100-1

D. Sammel, M. Boehler, A. Gamel, M. Schumacher

引用次数: 0

Photon Reconstruction in the Belle II Calorimeter Using Graph Neural Networks 利用图神经网络在贝尔 II 热量计中进行光子重构

Computing and Software for Big Science Pub Date : 2023-06-07 DOI: 10.1007/s41781-023-00105-w

F. Wemmer, I. Haide, J. Eppelt, T. Ferber, A. Beaubien, P. Branchini, M. Campajola, C. Cecchi, P. Cheema, G. De Nardo, C. Hearty, A. Kuzmin, S. Longo, E. Manoni, F. Meier, M. Merola, K. Miyabayashi, S. Moneta, M. Remnev, J. Roney, J. Shiu, B. Shwartz, Y. Unno, R. van Tonder, R. Volpe

引用次数: 0

Snowmass 2021 Computational Frontier CompF4 Topical Group Report Storage and Processing Resource Access Snowmass 2021计算前沿CompF4专题小组报告存储和处理资源访问

Computing and Software for Big Science Pub Date : 2023-04-26 DOI: 10.1007/s41781-023-00097-7

W. Bhimji, D. Carder, E. Dart, Javier M. Duarte, I. Fisk, R. Gardner, C. Guok, B. Jayatilaka, T. Lehman, M. Lin, C. Maltzahn, S. McKee, M. Neubauer, O. Rind, Oksana Shadura, N. Tran, P. Gemmeren, G. Watts, B. Weaver, F. Würthwein

引用次数: 0

Parametric Optimization on HPC Clusters with Geneva 基于Geneva的HPC集群参数优化

Computing and Software for Big Science Pub Date : 2023-04-21 DOI: 10.1007/s41781-023-00098-6

Jonas Weßner, R. Berlich, K. Schwarz, Matthias Lutz

引用次数: 0

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