Pub Date : 2022-02-09DOI: 10.1007/s41781-022-00080-8
Hendrik Schwanekamp, Ramona Hohl, D. Chirkin, Tom Gibbs, A. Harnisch, C. Kopper, P. Messmer, Vishal Mehta, A. Olivas, B. Riedel, M. Rongen, D. Schultz, J. van Santen
{"title":"Accelerating IceCube’s Photon Propagation Code with CUDA","authors":"Hendrik Schwanekamp, Ramona Hohl, D. Chirkin, Tom Gibbs, A. Harnisch, C. Kopper, P. Messmer, Vishal Mehta, A. Olivas, B. Riedel, M. Rongen, D. Schultz, J. van Santen","doi":"10.1007/s41781-022-00080-8","DOIUrl":"https://doi.org/10.1007/s41781-022-00080-8","url":null,"abstract":"","PeriodicalId":36026,"journal":{"name":"Computing and Software for Big Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"53242085","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}
Pub Date : 2022-01-01DOI: 10.1007/s41781-022-00083-5
Ian Bird, Simone Campana, Graeme A Stewart
{"title":"Advances in Computing in High Energy and Nuclear Physics-Invited Papers from vCHEP 2021.","authors":"Ian Bird, Simone Campana, Graeme A Stewart","doi":"10.1007/s41781-022-00083-5","DOIUrl":"https://doi.org/10.1007/s41781-022-00083-5","url":null,"abstract":"","PeriodicalId":36026,"journal":{"name":"Computing and Software for Big Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9086653/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10468053","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}
Pub Date : 2022-01-01DOI: 10.1007/s41781-021-00076-w
Tobias Wegner, Mario Lassnig, Peer Ueberholz, Christian Zeitnitz
A common task in scientific computing is the data reduction. This workflow extracts the most important information from large input data and stores it in smaller derived data objects. The derived data objects can then be used for further analysis. Typically, these workflows use distributed storage and computing resources. A straightforward setup of storage media would be low-cost tape storage and higher-cost disk storage. The large, infrequently accessed input data are stored on tape storage. The smaller, frequently accessed derived data is stored on disk storage. In a best-case scenario, the large input data is only accessed very infrequently and in a well-planned pattern. However, practice shows that often the data has to be processed continuously and unpredictably. This can significantly reduce tape storage performance. A common approach to counter this is storing copies of the large input data on disk storage. This contribution evaluates an approach that uses cloud storage resources to serve as a flexible cache or buffer, depending on the computational workflow. The proposed model is explored for the case of continuously processed data. For the evaluation, a simulation tool was developed, which can be used to analyse models related to storage and network resources. We show that using commercial cloud storage can reduce on-premises disk storage requirements, while maintaining an equal throughput of jobs. Moreover, the key metrics of the model are discussed, and an approach is described, which uses the simulation to assist with the decision process of using commercial cloud storage. The goal is to investigate approaches and propose new evaluation methods to overcome future data challenges.
{"title":"Simulation and Evaluation of Cloud Storage Caching for Data Intensive Science.","authors":"Tobias Wegner, Mario Lassnig, Peer Ueberholz, Christian Zeitnitz","doi":"10.1007/s41781-021-00076-w","DOIUrl":"https://doi.org/10.1007/s41781-021-00076-w","url":null,"abstract":"<p><p>A common task in scientific computing is the data reduction. This workflow extracts the most important information from large input data and stores it in smaller derived data objects. The derived data objects can then be used for further analysis. Typically, these workflows use distributed storage and computing resources. A straightforward setup of storage media would be low-cost tape storage and higher-cost disk storage. The large, infrequently accessed input data are stored on tape storage. The smaller, frequently accessed derived data is stored on disk storage. In a best-case scenario, the large input data is only accessed very infrequently and in a well-planned pattern. However, practice shows that often the data has to be processed continuously and unpredictably. This can significantly reduce tape storage performance. A common approach to counter this is storing copies of the large input data on disk storage. This contribution evaluates an approach that uses cloud storage resources to serve as a flexible cache or buffer, depending on the computational workflow. The proposed model is explored for the case of continuously processed data. For the evaluation, a simulation tool was developed, which can be used to analyse models related to storage and network resources. We show that using commercial cloud storage can reduce on-premises disk storage requirements, while maintaining an equal throughput of jobs. Moreover, the key metrics of the model are discussed, and an approach is described, which uses the simulation to assist with the decision process of using commercial cloud storage. The goal is to investigate approaches and propose new evaluation methods to overcome future data challenges.</p>","PeriodicalId":36026,"journal":{"name":"Computing and Software for Big Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9805534/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10863954","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}
Pub Date : 2021-12-01DOI: 10.1007/s41781-021-00074-y
Domenico Giordano, M. Alef, Luca Atzori, J. Barbet, O. Datskova, M. Girone, C. Hollowell, M. Javurkova, Riccardo Maganza, M. F. Medeiros, M. Michelotto, L. Rinaldi, A. Sciabà, R. Sobie, D. Southwick, Tristan Sullivan, A. Valassi
{"title":"HEPiX Benchmarking Solution for WLCG Computing Resources","authors":"Domenico Giordano, M. Alef, Luca Atzori, J. Barbet, O. Datskova, M. Girone, C. Hollowell, M. Javurkova, Riccardo Maganza, M. F. Medeiros, M. Michelotto, L. Rinaldi, A. Sciabà, R. Sobie, D. Southwick, Tristan Sullivan, A. Valassi","doi":"10.1007/s41781-021-00074-y","DOIUrl":"https://doi.org/10.1007/s41781-021-00074-y","url":null,"abstract":"","PeriodicalId":36026,"journal":{"name":"Computing and Software for Big Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"53241781","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}
Pub Date : 2021-11-09DOI: 10.1007/s41781-021-00071-1
A. Peters, D. C. van der Ster
{"title":"Evaluating CephFS Performance vs. Cost on High-Density Commodity Disk Servers","authors":"A. Peters, D. C. van der Ster","doi":"10.1007/s41781-021-00071-1","DOIUrl":"https://doi.org/10.1007/s41781-021-00071-1","url":null,"abstract":"","PeriodicalId":36026,"journal":{"name":"Computing and Software for Big Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"53241710","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}
Pub Date : 2021-10-23DOI: 10.1007/s41781-021-00063-1
O. Freyermuth, K. Kohl, P. Wienemann
{"title":"Unleashing JupyterHub: Exploiting Resources Without Inbound Network Connectivity Using HTCondor","authors":"O. Freyermuth, K. Kohl, P. Wienemann","doi":"10.1007/s41781-021-00063-1","DOIUrl":"https://doi.org/10.1007/s41781-021-00063-1","url":null,"abstract":"","PeriodicalId":36026,"journal":{"name":"Computing and Software for Big Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"53241221","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}
Pub Date : 2021-10-05DOI: 10.1007/s41781-021-00065-z
Xiaocong Ai, Georgiana Mania, H. Gray, Michael Kuhn, N. Styles
{"title":"A GPU-Based Kalman Filter for Track Fitting","authors":"Xiaocong Ai, Georgiana Mania, H. Gray, Michael Kuhn, N. Styles","doi":"10.1007/s41781-021-00065-z","DOIUrl":"https://doi.org/10.1007/s41781-021-00065-z","url":null,"abstract":"","PeriodicalId":36026,"journal":{"name":"Computing and Software for Big Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"53241567","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}
Pub Date : 2021-09-28DOI: 10.1007/s41781-021-00064-0
W. Ikegami Andersson, A. Akram, T. Johansson, R. Kliemt, Michael Papenbrock, J. Regina, K. Schönning, T. Stockmanns
{"title":"A Generalized Approach to Longitudinal Momentum Determination in Cylindrical Straw Tube Detectors","authors":"W. Ikegami Andersson, A. Akram, T. Johansson, R. Kliemt, Michael Papenbrock, J. Regina, K. Schönning, T. Stockmanns","doi":"10.1007/s41781-021-00064-0","DOIUrl":"https://doi.org/10.1007/s41781-021-00064-0","url":null,"abstract":"","PeriodicalId":36026,"journal":{"name":"Computing and Software for Big Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"53241427","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}
Pub Date : 2021-09-06DOI: 10.1007/s41781-021-00079-7
G. Aad, B. Abbott, D. Abbott, A. A. Abud, K. Abeling, D. K. Abhayasinghe, S. H. Abidi, A. Aboulhorma, H. Abramowicz, H. Abreu, Y. Abulaiti, A. Hoffman, B. Acharya, B. Achkar, L. Adam, C. Bourdarios, L. Adamczyk, L. Adamek, S. Addepalli, J. Adelman, A. Adiguzel, S. Adorni, T. Adye, A. Affolder, Y. Afik, C. Agapopoulou, M. N. Agaras, J. Agarwala, A. Aggarwal, C. Agheorghiesei, J. A. Aguilar-Saavedra, A. Ahmad, F. Ahmadov, W. S. Ahmed, X. Ai, G. Aielli, I. Aizenberg, S. Akatsuka, M. Akbiyik, T. Åkesson, A. Akimov, K. Khoury, G. Alberghi, J. Albert, P. Albicocco, M. A. Verzini, S. Alderweireldt, M. Aleksa, I. Aleksandrov, C. Alexa, T. Alexopoulos, A. Alfonsi, F. Alfonsi, M. Alhroob, B. Ali, S. Ali, M. Aliev, G. Alimonti, C. Allaire, B. Allbrooke, P. Allport, A. Aloisio, F. Alonso, C. Alpigiani, E. Camelia, M. A. Estevez, M. Alviggi, Y. Coutinho, A. Ambler, L. Ambroz, C. Amelung, D. Amidei, S. D. Santos, S. Amoroso, K. Amos, C. Amrouche, V. Ananiev, C. Anastopoulos, N. Andari, T. Andeen, J. Anders, S. Y. Andre
{"title":"AtlFast3: The Next Generation of Fast Simulation in ATLAS","authors":"G. Aad, B. Abbott, D. Abbott, A. A. Abud, K. Abeling, D. K. Abhayasinghe, S. H. Abidi, A. Aboulhorma, H. Abramowicz, H. Abreu, Y. Abulaiti, A. Hoffman, B. Acharya, B. Achkar, L. Adam, C. Bourdarios, L. Adamczyk, L. Adamek, S. Addepalli, J. Adelman, A. Adiguzel, S. Adorni, T. Adye, A. Affolder, Y. Afik, C. Agapopoulou, M. N. Agaras, J. Agarwala, A. Aggarwal, C. Agheorghiesei, J. A. Aguilar-Saavedra, A. Ahmad, F. Ahmadov, W. S. Ahmed, X. Ai, G. Aielli, I. Aizenberg, S. Akatsuka, M. Akbiyik, T. Åkesson, A. Akimov, K. Khoury, G. Alberghi, J. Albert, P. Albicocco, M. A. Verzini, S. Alderweireldt, M. Aleksa, I. Aleksandrov, C. Alexa, T. Alexopoulos, A. Alfonsi, F. Alfonsi, M. Alhroob, B. Ali, S. Ali, M. Aliev, G. Alimonti, C. Allaire, B. Allbrooke, P. Allport, A. Aloisio, F. Alonso, C. Alpigiani, E. Camelia, M. A. Estevez, M. Alviggi, Y. Coutinho, A. Ambler, L. Ambroz, C. Amelung, D. Amidei, S. D. Santos, S. Amoroso, K. Amos, C. Amrouche, V. Ananiev, C. Anastopoulos, N. Andari, T. Andeen, J. Anders, S. Y. Andre","doi":"10.1007/s41781-021-00079-7","DOIUrl":"https://doi.org/10.1007/s41781-021-00079-7","url":null,"abstract":"","PeriodicalId":36026,"journal":{"name":"Computing and Software for Big Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43589718","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}
Pub Date : 2021-07-05DOI: 10.1007/s41781-021-00061-3
V. Kuznetsov, L. Giommi, D. Bonacorsi
{"title":"MLaaS4HEP: Machine Learning as a Service for HEP","authors":"V. Kuznetsov, L. Giommi, D. Bonacorsi","doi":"10.1007/s41781-021-00061-3","DOIUrl":"https://doi.org/10.1007/s41781-021-00061-3","url":null,"abstract":"","PeriodicalId":36026,"journal":{"name":"Computing and Software for Big Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s41781-021-00061-3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"53241140","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}
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