Computing and Software for Big Science最新文献

{"title":"Optimal Operation of Cryogenic Calorimeters Through Deep Reinforcement Learning.","authors":"G Angloher, S Banik, G Benato, A Bento, A Bertolini, R Breier, C Bucci, J Burkhart, L Canonica, A D'Addabbo, S Di Lorenzo, L Einfalt, A Erb, F V Feilitzsch, S Fichtinger, D Fuchs, A Garai, V M Ghete, P Gorla, P V Guillaumon, S Gupta, D Hauff, M Ješkovský, J Jochum, M Kaznacheeva, A Kinast, S Kuckuk, H Kluck, H Kraus, A Langenkämper, M Mancuso, L Marini, B Mauri, L Meyer, V Mokina, K Niedermayer, M Olmi, T Ortmann, C Pagliarone, L Pattavina, F Petricca, W Potzel, P Povinec, F Pröbst, F Pucci, F Reindl, J Rothe, K Schäffner, J Schieck, S Schönert, C Schwertner, M Stahlberg, L Stodolsky, C Strandhagen, R Strauss, I Usherov, F Wagner, V Wagner, M Willers, V Zema, C Heitzinger, W Waltenberger","doi":"10.1007/s41781-024-00119-y","DOIUrl":"10.1007/s41781-024-00119-y","url":null,"abstract":"<p><p>Cryogenic phonon detectors with transition-edge sensors achieve the best sensitivity to sub-GeV/c <math><mmultiscripts><mrow></mrow> <mrow></mrow> <mn>2</mn></mmultiscripts> </math> dark matter interactions with nuclei in current direct detection experiments. In such devices, the temperature of the thermometer and the bias current in its readout circuit need careful optimization to achieve optimal detector performance. This task is not trivial and is typically done manually by an expert. In our work, we automated the procedure with reinforcement learning in two settings. First, we trained on a simulation of the response of three Cryogenic Rare Event Search with Superconducting Thermometers (CRESST) detectors used as a virtual reinforcement learning environment. Second, we trained live on the same detectors operated in the CRESST underground setup. In both cases, we were able to optimize a standard detector as fast and with comparable results as human experts. Our method enables the tuning of large-scale cryogenic detector setups with minimal manual interventions.</p>","PeriodicalId":36026,"journal":{"name":"Computing and Software for Big Science","volume":"8 1","pages":"10"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11557640/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142629776","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}

{"title":"One Flow to Correct Them all: Improving Simulations in High-Energy Physics with a Single Normalising Flow and a Switch.","authors":"Caio Daumann, Mauro Donega, Johannes Erdmann, Massimiliano Galli, Jan Lukas Späh, Davide Valsecchi","doi":"10.1007/s41781-024-00125-0","DOIUrl":"10.1007/s41781-024-00125-0","url":null,"abstract":"<p><p>Simulated events are key ingredients in almost all high-energy physics analyses. However, imperfections in the simulation can lead to sizeable differences between the observed data and simulated events. The effects of such mismodelling on relevant observables must be corrected either effectively via scale factors, with weights or by modifying the distributions of the observables and their correlations. We introduce a correction method that transforms one multidimensional distribution (simulation) into another one (data) using a simple architecture based on a single normalising flow with a boolean condition. We demonstrate the effectiveness of the method on a physics-inspired toy dataset with non-trivial mismodelling of several observables and their correlations.</p>","PeriodicalId":36026,"journal":{"name":"Computing and Software for Big Science","volume":"8 1","pages":"15"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11316724/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141971963","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}

{"title":"Portable Acceleration of CMS Computing Workflows with Coprocessors as a Service.","authors":"A Hayrapetyan, A Tumasyan, W Adam, J W Andrejkovic, T Bergauer, S Chatterjee, K Damanakis, M Dragicevic, P S Hussain, M Jeitler, N Krammer, A Li, D Liko, I Mikulec, J Schieck, R Schöfbeck, D Schwarz, M Sonawane, S Templ, W Waltenberger, C-E Wulz, M R Darwish, T Janssen, P Van Mechelen, E S Bols, J D'Hondt, S Dansana, A De Moor, M Delcourt, H El Faham, S Lowette, I Makarenko, D Müller, A R Sahasransu, S Tavernier, M Tytgat, G P Van Onsem, S Van Putte, D Vannerom, B Clerbaux, A K Das, G De Lentdecker, L Favart, P Gianneios, D Hohov, J Jaramillo, A Khalilzadeh, F A Khan, K Lee, M Mahdavikhorrami, A Malara, S Paredes, L Thomas, M Vanden Bemden, C Vander Velde, P Vanlaer, M De Coen, D Dobur, Y Hong, J Knolle, L Lambrecht, G Mestdach, K Mota Amarilo, C Rendón, A Samalan, K Skovpen, N Van Den Bossche, J van der Linden, L Wezenbeek, A Benecke, A Bethani, G Bruno, C Caputo, C Delaere, I S Donertas, A Giammanco, K Jaffel, Sa Jain, V Lemaitre, J Lidrych, P Mastrapasqua, K Mondal, T T Tran, S Wertz, G A Alves, E Coelho, C Hensel, T Menezes De Oliveira, A Moraes, P Rebello Teles, M Soeiro, W L Aldá Júnior, M Alves Gallo Pereira, M Barroso Ferreira Filho, H Brandao Malbouisson, W Carvalho, J Chinellato, E M Da Costa, G G Da Silveira, D De Jesus Damiao, S Fonseca De Souza, R Gomes De Souza, J Martins, C Mora Herrera, L Mundim, H Nogima, J P Pinheiro, A Santoro, A Sznajder, M Thiel, A Vilela Pereira, C A Bernardes, L Calligaris, T R Fernandez Perez Tomei, E M Gregores, P G Mercadante, S F Novaes, B Orzari, Sandra S Padula, A Aleksandrov, G Antchev, R Hadjiiska, P Iaydjiev, M Misheva, M Shopova, G Sultanov, A Dimitrov, L Litov, B Pavlov, P Petkov, A Petrov, E Shumka, S Keshri, S Thakur, T Cheng, T Javaid, L Yuan, Z Hu, J Liu, K Yi, G M Chen, H S Chen, M Chen, F Iemmi, C H Jiang, A Kapoor, H Liao, Z-A Liu, R Sharma, J N Song, J Tao, C Wang, J Wang, Z Wang, H Zhang, A Agapitos, Y Ban, A Levin, C Li, Q Li, Y Mao, S J Qian, X Sun, D Wang, H Yang, L Zhang, C Zhou, Z You, N Lu, G Bauer, X Gao, D Leggat, H Okawa, Z Lin, C Lu, M Xiao, C Avila, D A Barbosa Trujillo, A Cabrera, C Florez, J Fraga, J A Reyes Vega, J Mejia Guisao, F Ramirez, M Rodriguez, J D Ruiz Alvarez, D Giljanovic, N Godinovic, D Lelas, A Sculac, M Kovac, T Sculac, P Bargassa, V Brigljevic, B K Chitroda, D Ferencek, S Mishra, A Starodumov, T Susa, A Attikis, K Christoforou, S Konstantinou, J Mousa, C Nicolaou, F Ptochos, P A Razis, H Rykaczewski, H Saka, A Stepennov, M Finger, M Finger, A Kveton, E Ayala, E Carrera Jarrin, A A Abdelalim, E Salama, M A Mahmoud, Y Mohammed, K Ehataht, M Kadastik, T Lange, S Nandan, C Nielsen, J Pata, M Raidal, L Tani, C Veelken, H Kirschenmann, K Osterberg, M Voutilainen, S Bharthuar, E Brücken, F Garcia, K T S Kallonen, R Kinnunen, T Lampén, K Lassila-Perini, S Lehti, T Lindén, L Martikainen, M Myllymäki, M M Rantanen, H Siikonen, E Tuominen, J Tuominiemi, P Luukka, H Petrow, M Besancon, F Couderc, M Dejardin, D Denegri, J L Faure, F Ferri, S Ganjour, P Gras, G Hamel de Monchenault, V Lohezic, J Malcles, J Rander, A Rosowsky, M Ö Sahin, A Savoy-Navarro, P Simkina, M Titov, M Tornago, C Baldenegro Barrera, F Beaudette, A Buchot Perraguin, P Busson, A Cappati, C Charlot, M Chiusi, F Damas, O Davignon, A De Wit, B A Fontana Santos Alves, S Ghosh, A Gilbert, R Granier de Cassagnac, A Hakimi, B Harikrishnan, L Kalipoliti, G Liu, J Motta, M Nguyen, C Ochando, L Portales, R Salerno, J B Sauvan, Y Sirois, A Tarabini, E Vernazza, A Zabi, A Zghiche, J-L Agram, J Andrea, D Apparu, D Bloch, J-M Brom, E C Chabert, C Collard, S Falke, U Goerlach, C Grimault, R Haeberle, A-C Le Bihan, M Meena, G Saha, M A Sessini, P Van Hove, S Beauceron, B Blancon, G Boudoul, N Chanon, J Choi, D Contardo, P Depasse, C Dozen, H El Mamouni, J Fay, S Gascon, M Gouzevitch, C Greenberg, G Grenier, B Ille, I B Laktineh, M Lethuillier, L Mirabito, S Perries, A Purohit, M Vander Donckt, P Verdier, J Xiao, I Bagaturia, I Lomidze, Z Tsamalaidze, V Botta, L Feld, K Klein, M Lipinski, D Meuser, A Pauls, N Röwert, M Teroerde, S Diekmann, A Dodonova, N Eich, D Eliseev, F Engelke, J Erdmann, M Erdmann, P Fackeldey, B Fischer, T Hebbeker, K Hoepfner, F Ivone, A Jung, M Y Lee, F Mausolf, M Merschmeyer, A Meyer, S Mukherjee, D Noll, F Nowotny, A Pozdnyakov, Y Rath, W Redjeb, F Rehm, H Reithler, U Sarkar, V Sarkisovi, A Schmidt, A Sharma, J L Spah, A Stein, F Torres Da Silva De Araujo, L Vigilante, S Wiedenbeck, S Zaleski, C Dziwok, G Flügge, W Haj Ahmad, T Kress, A Nowack, O Pooth, A Stahl, T Ziemons, A Zotz, H Aarup Petersen, M Aldaya Martin, J Alimena, S Amoroso, Y An, S Baxter, M Bayatmakou, H Becerril Gonzalez, O Behnke, A Belvedere, S Bhattacharya, F Blekman, K Borras, A Campbell, A Cardini, C Cheng, F Colombina, S Consuegra Rodríguez, G Correia Silva, M De Silva, G Eckerlin, D Eckstein, L I Estevez Banos, O Filatov, E Gallo, A Geiser, A Giraldi, V Guglielmi, M Guthoff, A Hinzmann, A Jafari, L Jeppe, N Z Jomhari, B Kaech, M Kasemann, C Kleinwort, R Kogler, M Komm, D Krücker, W Lange, D Leyva Pernia, K Lipka, W Lohmann, R Mankel, I-A Melzer-Pellmann, M Mendizabal Morentin, A B Meyer, G Milella, A Mussgiller, L P Nair, A Nürnberg, Y Otarid, J Park, D Pérez Adán, E Ranken, A Raspereza, B Ribeiro Lopes, J Rübenach, A Saggio, M Scham, S Schnake, P Schütze, C Schwanenberger, D Selivanova, K Sharko, M Shchedrolosiev, R E Sosa Ricardo, D Stafford, F Vazzoler, A Ventura Barroso, R Walsh, Q Wang, Y Wen, K Wichmann, L Wiens, C Wissing, Y Yang, A Zimermmane Castro Santos, A Albrecht, S Albrecht, M Antonello, S Bein, L Benato, S Bollweg, M Bonanomi, P Connor, M Eich, K El Morabit, Y Fischer, C Garbers, E Garutti, A Grohsjean, J Haller, H R Jabusch, G Kasieczka, P Keicher, R Klanner, W Korcari, T Kramer, V Kutzner, F Labe, J Lange, A Lobanov, C Matthies, A Mehta, L Moureaux, M Mrowietz, A Nigamova, Y Nissan, A Paasch, K J Pena Rodriguez, T Quadfasel, B Raciti, M Rieger, D Savoiu, J Schindler, P Schleper, M Schröder, J Schwandt, M Sommerhalder, H Stadie, G Steinbrück, A Tews, M Wolf, S Brommer, M Burkart, E Butz, T Chwalek, A Dierlamm, A Droll, N Faltermann, M Giffels, A Gottmann, F Hartmann, R Hofsaess, M Horzela, U Husemann, J Kieseler, M Klute, R Koppenhöfer, J M Lawhorn, M Link, A Lintuluoto, S Maier, S Mitra, M Mormile, Th Müller, M Neukum, M Oh, M Presilla, G Quast, K Rabbertz, B Regnery, N Shadskiy, I Shvetsov, H J Simonis, M Toms, N Trevisani, R F Von Cube, M Wassmer, S Wieland, F Wittig, R Wolf, X Zuo, G Anagnostou, G Daskalakis, A Kyriakis, A Papadopoulos, A Stakia, P Kontaxakis, G Melachroinos, A Panagiotou, I Papavergou, I Paraskevas, N Saoulidou, K Theofilatos, E Tziaferi, K Vellidis, I Zisopoulos, G Bakas, T Chatzistavrou, G Karapostoli, K Kousouris, I Papakrivopoulos, E Siamarkou, G Tsipolitis, A Zacharopoulou, K Adamidis, I Bestintzanos, I Evangelou, C Foudas, C Kamtsikis, P Katsoulis, P Kokkas, P G Kosmoglou Kioseoglou, N Manthos, I Papadopoulos, J Strologas, M Bartók, C Hajdu, D Horvath, K Márton, F Sikler, V Veszpremi, M Csanád, K Farkas, M M A Gadallah, Á Kadlecsik, P Major, K Mandal, G Pásztor, A J Rádl, G I Veres, P Raics, B Ujvari, G Zilizi, G Bencze, S Czellar, J Molnar, Z Szillasi, T Csorgo, F Nemes, T Novak, J Babbar, S Bansal, S B Beri, V Bhatnagar, G Chaudhary, S Chauhan, N Dhingra, A Kaur, A Kaur, H Kaur, M Kaur, S Kumar, K Sandeep, T Sheokand, J B Singh, A Singla, A Ahmed, A Bhardwaj, A Chhetri, B C Choudhary, A Kumar, A Kumar, M Naimuddin, K Ranjan, S Saumya, S Baradia, S Barman, S Bhattacharya, S Dutta, S Dutta, S Sarkar, M M Ameen, P K Behera, S C Behera, S Chatterjee, P Jana, P Kalbhor, J R Komaragiri, D Kumar, P R Pujahari, N R Saha, A Sharma, A K Sikdar, S Verma, S Dugad, M Kumar, G B Mohanty, P Suryadevara, A Bala, S Banerjee, R M Chatterjee, R K Dewanjee, M Guchait, Sh Jain, A Jaiswal, S Karmakar, S Kumar, G Majumder, K Mazumdar, S Parolia, A Thachayath, S Bahinipati, C Kar, D Maity, P Mal, T Mishra, V K Muraleedharan Nair Bindhu, K Naskar, A Nayak, P Sadangi, P Saha, S K Swain, S Varghese, D Vats, S Acharya, A Alpana, S Dube, B Gomber, B Kansal, A Laha, B Sahu, S Sharma, K Y Vaish, H Bakhshiansohi, E Khazaie, M Zeinali, S Chenarani, S M Etesami, M Khakzad, M Mohammadi Najafabadi, M Grunewald, M Abbrescia, R Aly, A Colaleo, D Creanza, B D'Anzi, N De Filippis, M De Palma, A Di Florio, W Elmetenawee, L Fiore, G Iaselli, M Louka, G Maggi, M Maggi, I Margjeka, V Mastrapasqua, S My, S Nuzzo, A Pellecchia, A Pompili, G Pugliese, R Radogna, G Ramirez-Sanchez, D Ramos, A Ranieri, L Silvestris, F M Simone, Ü Sözbilir, A Stamerra, R Venditti, P Verwilligen, A Zaza, G Abbiendi, C Battilana, D Bonacorsi, L Borgonovi, R Campanini, P Capiluppi, A Castro, F R Cavallo, M Cuffiani, G M Dallavalle, T Diotalevi, A Fanfani, D Fasanella, P Giacomelli, L Giommi, C Grandi, L Guiducci, S Lo Meo, L Lunerti, S Marcellini, G Masetti, F L Navarria, A Perrotta, F Primavera, A M Rossi, T Rovelli, G P Siroli, S Costa, A Di Mattia, R Potenza, A Tricomi, C Tuve, P Assiouras, G Barbagli, G Bardelli, B Camaiani, A Cassese, R Ceccarelli, V Ciulli, C Civinini, R D'Alessandro, E Focardi, T Kello, G Latino, P Lenzi, M Lizzo, M Meschini, S Paoletti, A Papanastassiou, G Sguazzoni, L Viliani, L Benussi, S Bianco, S Meola, D Piccolo, P Chatagnon, F Ferro, E Robutti, S Tosi, A Benaglia, G Boldrini, F Brivio, F Cetorelli, F De Guio, M E Dinardo, P Dini, S Gennai, R Gerosa, A Ghezzi, P Govoni, L Guzzi, M T Lucchini, M Malberti, S Malvezzi, A Massironi, D Menasce, L Moroni, M Paganoni, D Pedrini, B S Pinolini, S Ragazzi, T Tabarelli de Fatis, D Zuolo, S Buontempo, A Cagnotta, F Carnevali, N Cavallo, F Fabozzi, A O M Iorio, L Lista, P Paolucci, B Rossi, C Sciacca, R Ardino, P Azzi, N Bacchetta, D Bisello, P Bortignon, A Bragagnolo, P Checchia, T Dorigo, U Gasparini, E Lusiani, M Margoni, F Marini, A T Meneguzzo, M Migliorini, M Passaseo, J Pazzini, P Ronchese, R Rossin, M Sgaravatto, F Simonetto, G Strong, M Tosi, A Triossi, S Ventura, H Yarar, M Zanetti, P Zotto, A Zucchetta, G Zumerle, S Abu Zeid, C Aimè, A Braghieri, S Calzaferri, D Fiorina, P Montagna, V Re, C Riccardi, P Salvini, I Vai, P Vitulo, S Ajmal, G M Bilei, D Ciangottini, L Fanò, M Magherini, G Mantovani, V Mariani, M Menichelli, F Moscatelli, A Rossi, A Santocchia, D Spiga, T Tedeschi, P Asenov, P Azzurri, G Bagliesi, R Bhattacharya, L Bianchini, T Boccali, E Bossini, D Bruschini, R Castaldi, M A Ciocci, M Cipriani, V D'Amante, R Dell'Orso, S Donato, A Giassi, F Ligabue, D Matos Figueiredo, A Messineo, M Musich, F Palla, A Rizzi, G Rolandi, S Roy Chowdhury, T Sarkar, A Scribano, P Spagnolo, R Tenchini, G Tonelli, N Turini, A Venturi, P G Verdini, P Barria, C Basile, M Campana, F Cavallari, L Cunqueiro Mendez, D Del Re, E Di Marco, M Diemoz, F Errico, E Longo, P Meridiani, J Mijuskovic, G Organtini, F Pandolfi, R Paramatti, C Quaranta, S Rahatlou, C Rovelli, F Santanastasio, L 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Watson, S Ha, H D Yoo, M Choi, M R Kim, H Lee, Y Lee, I Yu, T Beyrouthy, Y Maghrbi, K Dreimanis, A Gaile, G Pikurs, A Potrebko, M Seidel, V Veckalns, N R Strautnieks, M Ambrozas, A Juodagalvis, A Rinkevicius, G Tamulaitis, N Bin Norjoharuddeen, I Yusuff, Z Zolkapli, J F Benitez, A Castaneda Hernandez, H A Encinas Acosta, L G Gallegos Maríñez, M León Coello, J A Murillo Quijada, A Sehrawat, L Valencia Palomo, G Ayala, H Castilla-Valdez, H Crotte Ledesma, E De La Cruz-Burelo, I Heredia-De La Cruz, R Lopez-Fernandez, C A Mondragon Herrera, A Sánchez Hernández, C Oropeza Barrera, M Ramírez García, I Bautista, I Pedraza, H A Salazar Ibarguen, C Uribe Estrada, I Bubanja, N Raicevic, P H Butler, A Ahmad, M I Asghar, A Awais, M I M Awan, H R Hoorani, W A Khan, V Avati, L Grzanka, M Malawski, H Bialkowska, M Bluj, B Boimska, M Górski, M Kazana, M Szleper, P Zalewski, K Bunkowski, K Doroba, A Kalinowski, M Konecki, J Krolikowski, A Muhammad, K Pozniak, W Zabolotny, M Araujo, D Bastos, C Beirão 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Andreev, T Aushev, M Azarkin, A Babaev, A Belyaev, V Blinov, E Boos, V Borshch, D Budkouski, M Chadeeva, V Chekhovsky, R Chistov, A Demiyanov, A Dermenev, T Dimova, D Druzhkin, M Dubinin, L Dudko, A Ershov, G Gavrilov, V Gavrilov, S Gninenko, V Golovtcov, N Golubev, I Golutvin, I Gorbunov, A Gribushin, Y Ivanov, V Kachanov, V Karjavine, A Karneyeu, V Kim, M Kirakosyan, D Kirpichnikov, M Kirsanov, V Klyukhin, O Kodolova, V Korenkov, A Kozyrev, N Krasnikov, A Lanev, P Levchenko, N Lychkovskaya, V Makarenko, A Malakhov, V Matveev, V Murzin, A Nikitenko, S Obraztsov, V Oreshkin, V Palichik, V Perelygin, S Petrushanko, S Polikarpov, V Popov, O Radchenko, M Savina, V Savrin, V Shalaev, S Shmatov, S Shulha, Y Skovpen, S Slabospitskii, V Smirnov, A Snigirev, D Sosnov, V Sulimov, E Tcherniaev, A Terkulov, O Teryaev, I Tlisova, A Toropin, L Uvarov, A Uzunian, A Vorobyev, N Voytishin, B S Yuldashev, A Zarubin, I Zhizhin, A Zhokin","doi":"10.1007/s41781-024-00124-1","DOIUrl":"10.1007/s41781-024-00124-1","url":null,"abstract":"<p><p>Computing demands for large scientific experiments, such as the CMS experiment at the CERN LHC, will increase dramatically in the next decades. To complement the future performance increases of software running on central processing units (CPUs), explorations of coprocessor usage in data processing hold great potential and interest. Coprocessors are a class of computer processors that supplement CPUs, often improving the execution of certain functions due to architectural design choices. We explore the approach of Services for Optimized Network Inference on Coprocessors (SONIC) and study the deployment of this as-a-service approach in large-scale data processing. In the studies, we take a data processing workflow of the CMS experiment and run the main workflow on CPUs, while offloading several machine learning (ML) inference tasks onto either remote or local coprocessors, specifically graphics processing units (GPUs). With experiments performed at Google Cloud, the Purdue Tier-2 computing center, and combinations of the two, we demonstrate the acceleration of these ML algorithms individually on coprocessors and the corresponding throughput improvement for the entire workflow. This approach can be easily generalized to different types of coprocessors and deployed on local CPUs without decreasing the throughput performance. We emphasize that the SONIC approach enables high coprocessor usage and enables the portability to run workflows on different types of coprocessors.</p>","PeriodicalId":36026,"journal":{"name":"Computing and Software for Big Science","volume":"8 1","pages":"17"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11374919/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142156222","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}

{"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}

{"title":"Jet Energy Calibration with Deep Learning as a Kubeflow Pipeline","authors":"D. Holmberg, D. Golubović, Henning Kirschenmann","doi":"10.1007/s41781-023-00103-y","DOIUrl":"https://doi.org/10.1007/s41781-023-00103-y","url":null,"abstract":"","PeriodicalId":36026,"journal":{"name":"Computing and Software for Big Science","volume":"7 1","pages":"1-16"},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47081719","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}

{"title":"Convergent Approaches to AI Explainability for HEP Muonic Particles Pattern Recognition","authors":"Leandro Maglianella, Lorenzo Nicoletti, S. Giagu, Christian Napoli, Simone Scardapane","doi":"10.1007/s41781-023-00102-z","DOIUrl":"https://doi.org/10.1007/s41781-023-00102-z","url":null,"abstract":"","PeriodicalId":36026,"journal":{"name":"Computing and Software for Big Science","volume":"7 1","pages":"1-18"},"PeriodicalIF":0.0,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45211367","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}

{"title":"Lightweight Integration of a Data Cache for Opportunistic Usage of HPC Resources in HEP Workflows","authors":"D. Sammel, M. Boehler, A. Gamel, M. Schumacher","doi":"10.1007/s41781-023-00100-1","DOIUrl":"https://doi.org/10.1007/s41781-023-00100-1","url":null,"abstract":"","PeriodicalId":36026,"journal":{"name":"Computing and Software for Big Science","volume":"7 1","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2023-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48222563","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}

{"title":"Photon Reconstruction in the Belle II Calorimeter Using Graph Neural Networks","authors":"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","doi":"10.1007/s41781-023-00105-w","DOIUrl":"https://doi.org/10.1007/s41781-023-00105-w","url":null,"abstract":"","PeriodicalId":36026,"journal":{"name":"Computing and Software for Big Science","volume":"16 1","pages":"1-15"},"PeriodicalIF":0.0,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139370671","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}

{"title":"Snowmass 2021 Computational Frontier CompF4 Topical Group Report Storage and Processing Resource Access","authors":"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","doi":"10.1007/s41781-023-00097-7","DOIUrl":"https://doi.org/10.1007/s41781-023-00097-7","url":null,"abstract":"","PeriodicalId":36026,"journal":{"name":"Computing and Software for Big Science","volume":"7 1","pages":"1-26"},"PeriodicalIF":0.0,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47142391","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}

{"title":"Parametric Optimization on HPC Clusters with Geneva","authors":"Jonas Weßner, R. Berlich, K. Schwarz, Matthias Lutz","doi":"10.1007/s41781-023-00098-6","DOIUrl":"https://doi.org/10.1007/s41781-023-00098-6","url":null,"abstract":"","PeriodicalId":36026,"journal":{"name":"Computing and Software for Big Science","volume":"7 1","pages":"1-15"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45220925","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}