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Start Over Author "Tull CE" Publisher escholarship, university of california
10 results on '"Tull CE"'

1. The Mini-CAPTAIN liquid argon time projection chamber

Author

Taylor, CE, Bhandari, B, Bian, J, Bilton, K, Callahan, C, Chaves, J, Chen, H, Cline, D, Cooper, RL, Danielson, DL, Danielson, J, Dokania, N, Elliot, S, Fernandes, S, Gardiner, S, Garvey, G, Gehman, V, Giuliani, F, Glavin, S, Gold, M, Grant, C, Guardincerri, E, Haines, T, Higuera, A, Ji, JY, Kadel, R, Kamp, N, Karlin, A, Ketchum, W, Koerner, LW, Lee, D, Lee, K, Liu, Q, Locke, S, Louis, WC, Madigan, P, Manalaysay, A, Maricic, J, Martin, E, Martinez, MJ, Martynenko, S, Mauger, C, McGrew, C, Medina, J, Medina, PJ, Mills, A, Mills, G, Mirabal-Martinez, J, Olivier, A, Pantic, E, Philipbar, B, Pitcher, C, Radeka, V, Ramsey, J, Rielage, K, Rosen, M, Sanchez, AR, Shin, J, Sinnis, G, Smy, M, Sondheim, W, Stancu, I, Sterbenz, C, Sun, Y, Svoboda, R, Teymourian, A, Thorn, C, Tull, CE, Tzanov, M, Van de Water, R, Walsh, N, Wang, H, Wang, Y, Yanagisawa, C, Yarritu, A, and Yoo, J

Subjects
Liquid argon detector, Time projection chamber, Neutron measurement, Photon detection system, physics.ins-det, hep-ex, Nuclear & Particles Physics, Astronomical and Space Sciences, Atomic, Molecular, Nuclear, Particle and Plasma Physics, Other Physical Sciences
Abstract

This manuscript describes the commissioning of the Mini-CAPTAIN liquid argon detector in a neutron beam at the Los Alamos Neutron Science Center (LANSCE), which led to a first measurement of high-energy neutron interactions in argon. The Mini-CAPTAIN detector consists of a Time Projection Chamber (TPC) with an accompanying photomultiplier tube (PMT) array sealed inside a liquid-argon-filled cryostat. The liquid argon is constantly purified and recirculated in a closed-loop cycle during operation. The specifications and assembly of the detector subsystems and an overview of their performance in a neutron beam are reported.

Published
2021

2. First Measurement of the Total Neutron Cross Section on Argon between 100 and 800 MeV

Author

Bhandari, B, Bian, J, Bilton, K, Callahan, C, Chaves, J, Chen, H, Cline, D, Cooper, RL, Danielson, D, Danielson, J, Dokania, N, Elliott, S, Fernandes, S, Gardiner, S, Garvey, G, Gehman, V, Giuliani, F, Glavin, S, Gold, M, Grant, C, Guardincerri, E, Haines, T, Higuera, A, Ji, JY, Kadel, R, Kamp, N, Karlin, A, Ketchum, W, Koerner, LW, Lee, D, Lee, K, Liu, Q, Locke, S, Louis, WC, Manalaysay, A, Maricic, J, Martin, E, Martinez, MJ, Martynenko, S, Mauger, C, McGrew, C, Medina, J, Medina, PJ, Mills, A, Mills, G, Mirabal-Martinez, J, Olivier, A, Pantic, E, Philipbar, B, Pitcher, C, Radeka, V, Ramsey, J, Rielage, K, Rosen, M, Sanchez, AR, Shin, J, Sinnis, G, Smy, M, Sondheim, W, Stancu, I, Sterbenz, C, Sun, Y, Svoboda, R, Taylor, C, Teymourian, A, Thorn, C, Tull, CE, Tzanov, M, Van de Water, RG, Walker, D, Walsh, N, Wang, H, Wang, Y, Yanagisawa, C, Yarritu, A, and Yoo, J

Subjects
Nuclear and Plasma Physics, Synchrotrons and Accelerators, Physical Sciences, Bioengineering, CAPTAIN Collaboration, hep-ex, nucl-ex, physics.ins-det, Mathematical Sciences, Engineering, General Physics, Mathematical sciences, Physical sciences
Abstract

We report the first measurement of the neutron cross section on argon in the energy range of 100-800 MeV. The measurement was obtained with a 4.3-h exposure of the Mini-CAPTAIN detector to the WNR/LANSCE beam at LANL. The total cross section is measured from the attenuation coefficient of the neutron flux as it traverses the liquid argon volume. A set of 2631 candidate interactions is divided in bins of the neutron kinetic energy calculated from time-of-flight measurements. These interactions are reconstructed with custom-made algorithms specifically designed for the data in a time projection chamber the size of the Mini-CAPTAIN detector. The energy averaged cross section is 0.91±0.10(stat)±0.09(syst)  b. A comparison of the measured cross section is made to the GEANT4 and FLUKA event generator packages, where the energy averaged cross sections in this range are 0.60 and 0.68 b, respectively.

Published
2019

3. Modeling data transfers: Change point and anomaly detection

Author

Dao, C, Liu, X, Sim, A, Tull, CE, and Wu, K

Abstract

To help the operations and resource planning of a large experimental facility, we model the time needed for transferring the data files produced by the facility to a computer center, with the goals of predicting expected file transfer time and identifying unusually slow transfers that might require attention from human operators. The file transfer time can be thought of having two parts: a base time depending on the hardware and software involved, and a congestion part due to uncontrollable interferences from other operations on the shared resources including network links, disk storage systems, and CPU involved in the transfers. Since many parameters important to the transfer time are not available to us, we employ a change point detection algorithm to separate the data records into time periods (called segments) with relatively stable behavior. Within each segment, we apply a non-parametric model to describe the congestion time. When the observed file transfer time is significantly longer than typical expected time, we declare the particular file transfer to be unusually slow. When many of these unusually slow file transfers are observed, it is worthwhile to notify the human operators to investigate the abnormal behavior of the system.

Published
2018

4. LUX-ZEPLIN (LZ) Technical Design Report

Author

Mount, BJ, Hans, S, Rosero, R, Yeh, M, Chan, C, Gaitskell, RJ, Huang, DQ, Makkinje, J, Malling, DC, Pangilinan, M, Rhyne, CA, Taylor, WC, Verbus, JR, Kim, YD, Lee, HS, Lee, J, Leonard, DS, Li, J, Belle, J, Cottle, A, Lippincott, WH, Markley, DJ, Martin, TJ, Sarychev, M, Tope, TE, Utes, M, Wang, R, Young, I, Araújo, HM, Bailey, AJ, Bauer, D, Colling, D, Currie, A, Fayer, S, Froborg, F, Greenwood, S, Jones, WG, Kasey, V, Khaleeq, M, Olcina, I, Paredes, B López, Richards, A, Sumner, TJ, Tomás, A, Vacheret, A, Brás, P, Lindote, A, Lopes, MI, Neves, F, Rodrigues, JP, Silva, C, Solovov, VN, Barry, MJ, Cole, A, Dobi, A, Edwards, WR, Faham, CH, Fiorucci, S, Gantos, NJ, Gehman, VM, Gilchriese, MGD, Hanzel, K, Hoff, MD, Kamdin, K, Lesko, KT, McConnell, CT, O'Sullivan, K, Oliver-Mallory, KC, Patton, SJ, Saba, JS, Sorensen, P, Thomas, KJ, Tull, CE, Waldron, WL, Witherell, MS, Bernstein, A, Kazkaz, K, Xu, J, Akimov, D Yu, Bolozdynya, AI, Khromov, AV, Konovalov, AM, Kumpan, AV, Sosnovtsev, VV, Dahl, CE, Temples, D, Carmona-Benitez, MC, Viveiros, L de, Akerib, DS, Auyeung, H, Biesiadzinski, TP, Breidenbach, M, Bramante, R, Conley, R, Craddock, WW, Fan, A, Hau, A, Ignarra, CM, Ji, W, and Krebs, HJ

Subjects
physics.ins-det, astro-ph.IM, hep-ex
Abstract

In this Technical Design Report (TDR) we describe the LZ detector to be builtat the Sanford Underground Research Facility (SURF). The LZ dark matterexperiment is designed to achieve sensitivity to a WIMP-nucleonspin-independent cross section of three times ten to the negative forty-eighthsquare centimeters.

Published
2017

5. Making advanced scientific algorithms and big scientific data management more accessible

Author

Venkatakrishnan, SV, Mohan, KA, Beattie, K, Correa, J, Dart, E, Deslippe, JR, Hexemer, A, Krishnan, H, MacDowell, AA, Marchesini, S, Patton, SJ, Perciano, T, Sethian, JA, Stromsness, R, Tierney, BL, Tull, CE, Ushizima, D, and Parkinson, DY

Abstract

Synchrotrons such as the Advanced Light Source (ALS) at Lawrence Berkeley National Laboratory are user facilities - they are sources of extremely bright X-ray beams, and scientists come from all over the world to perform experiments that require these beams. As the complexity of experiments has increased, and the size and rates of data sets has exploded, managing, analyzing and presenting the data collected at synchrotrons has been an increasing challenge. The ALS has partnered with high performance computing, fast networking, and applied mathematics groups to create a"super-facility", giving users simultaneous access to the experimental, computational, and algorithmic resources to overcome this challenge. This combination forms an efficient closed loop, where data despite its high rate and volume is transferred and processed, in many cases immediately and automatically, on appropriate compute resources, and results are extracted, visualized, and presented to users or to the experimental control system, both to provide immediate insight and to guide decisions about subsequent experiments during beam-time. In this paper, We will present work done on advanced tomographic reconstruction algorithms to support users of the 3D micron-scale imaging instrument (Beamline 8.3.2, hard X-ray micro-tomography).

Published
2016

6. GANGA: a user-Grid interface for Atlas and LHCb

Author

Harrison, K, Lavrijsen, WTLP, Mato, P, Soroko, A, Tan, CL, Tull, CE, Brook, N, and Jones, RWL

Subjects
cs.SE, D.2.6
Abstract

The Gaudi/Athena and Grid Alliance (GANGA) is a front-end for theconfiguration, submission, monitoring, bookkeeping, output collection, andreporting of computing jobs run on a local batch system or on the grid. Inparticular, GANGA handles jobs that use applications written for the Gaudisoftware framework shared by the Atlas and LHCb experiments. GANGA exploits thecommonality of Gaudi-based computing jobs, while insulating against grid-,batch- and framework-specific technicalities, to maximize end-user productivityin defining, configuring, and executing jobs. Designed for a python-basedcomponent architecture, GANGA has a modular underpinning and is therefore wellplaced for contributing to, and benefiting from, work in related projects. Itsfunctionality is accessible both from a scriptable command-line interface, forexpert users and automated tasks, and through a graphical interface, whichsimplifies the interaction with GANGA for beginning and c1asual users. This paper presents the GANGA design and implementation, the development ofthe underlying software bus architecture, and the functionality of the firstpublic GANGA release.

Published
2003

7. Study of the wave packet treatment of neutrino oscillation at Daya Bay

Author

An, FP, Balantekin, AB, Band, HR, Bishai, M, Blyth, S, Cao, D, Cao, GF, Cao, J, Cen, WR, Chan, YL, Chang, JF, Chang, LC, Chang, Y, Chen, HS, Chen, QY, Chen, SM, Chen, YX, Chen, Y, Cheng, J-H, Cheng, J, Cheng, YP, Cheng, ZK, Cherwinka, JJ, Chu, MC, Chukanov, A, Cummings, JP, Arcos, JD, Deng, ZY, Ding, XF, Ding, YY, Diwan, MV, Dolgareva, M, Dove, J, Dwyer, DA, Edwards, WR, Gill, R, Gonchar, M, Gong, GH, Gong, H, Grassi, M, Gu, WQ, Guan, MY, Guo, L, Guo, XH, Guo, Z, Hackenburg, RW, Han, R, Hans, S, He, M, Heeger, KM, Heng, YK, Higuera, A, Hor, YK, Hsiung, YB, Hu, BZ, Hu, T, Hu, W, Huang, EC, Huang, HX, Huang, XT, Huber, P, Huo, W, Hussain, G, Jaffe, DE, Jaffke, P, Jen, KL, Jetter, S, Ji, XP, Ji, XL, Jiao, JB, Johnson, RA, Joshi, J, Kang, L, Kettell, SH, Kohn, S, Kramer, M, Kwan, KK, Kwok, MW, Kwok, T, Langford, TJ, Lau, K, Lebanowski, L, Lee, J, Lee, JHC, Lei, RT, Leitner, R, Leung, JKC, Li, C, Li, DJ, Li, F, Li, GS, Li, QJ, Li, S, Li, SC, Li, WD, Li, XN, Li, YF, Li, ZB, Liang, H, Lin, CJ, Lin, GL, Lin, S, Lin, SK, Lin, Y-C, Ling, JJ, Link, JM, Littenberg, L, Littlejohn, BR, Liu, DW, Liu, JL, Liu, JC, Loh, CW, Lu, C, Lu, HQ, Lu, JS, Luk, KB, Lv, Z, Ma, QM, Ma, XY, Ma, XB, Ma, YQ, Malyshkin, Y, Caicedo, DAM, McKeown, RD, Mitchell, I, Mooney, M, Nakajima, Y, Napolitano, J, Naumov, D, Naumova, E, Ngai, HY, Ning, Z, Ochoa-Ricoux, JP, Olshevskiy, A, Pan, H-R, Park, J, Patton, S, Pec, V, Peng, JC, Pinsky, L, Pun, CSJ, Qi, FZ, Qi, M, Qian, X, Raper, N, Ren, J, Rosero, R, Roskovec, B, Ruan, XC, Steiner, H, Sun, GX, Sun, JL, Tang, W, Taychenachev, D, Treskov, K, Tsang, KV, Tull, CE, Viaux, N, Viren, B, Vorobel, V, Wang, CH, Wang, M, Wang, NY, Wang, RG, Wang, W, Wang, X, Wang, YF, Wang, Z, Wang, ZM, Wei, HY, Wen, LJ, Whisnant, K, White, CG, Whitehead, L, Wise, T, Wong, HLH, Wong, SCF, Worcester, E, Wu, C-H, Wu, Q, Wu, WJ, Xia, DM, Xia, JK, Xing, ZZ, Xu, JY, Xu, JL, Xu, Y, Xue, T, Yang, CG, Yang, H, Yang, L, Yang, MS, Yang, MT, Ye, M, Ye, Z, Yeh, M, Young, BL, Yu, ZY, Zeng, S, Zhan, L, Zhang, C, Zhang, HH, Zhang, JW, Zhang, QM, Zhang, XT, Zhang, YM, Zhang, YX, Zhang, ZJ, Zhang, ZY, Zhang, ZP, Zhao, J, Zhao, QW, Zhao, YB, Zhong, WL, Zhou, L, Zhou, N, Zhuang, HL, Zou, JH, An, FP, Balantekin, AB, Band, HR, Bishai, M, Blyth, S, Cao, D, Cao, GF, Cao, J, Cen, WR, Chan, YL, Chang, JF, Chang, LC, Chang, Y, Chen, HS, Chen, QY, Chen, SM, Chen, YX, Chen, Y, Cheng, J-H, Cheng, J, Cheng, YP, Cheng, ZK, Cherwinka, JJ, Chu, MC, Chukanov, A, Cummings, JP, Arcos, JD, Deng, ZY, Ding, XF, Ding, YY, Diwan, MV, Dolgareva, M, Dove, J, Dwyer, DA, Edwards, WR, Gill, R, Gonchar, M, Gong, GH, Gong, H, Grassi, M, Gu, WQ, Guan, MY, Guo, L, Guo, XH, Guo, Z, Hackenburg, RW, Han, R, Hans, S, He, M, Heeger, KM, Heng, YK, Higuera, A, Hor, YK, Hsiung, YB, Hu, BZ, Hu, T, Hu, W, Huang, EC, Huang, HX, Huang, XT, Huber, P, Huo, W, Hussain, G, Jaffe, DE, Jaffke, P, Jen, KL, Jetter, S, Ji, XP, Ji, XL, Jiao, JB, Johnson, RA, Joshi, J, Kang, L, Kettell, SH, Kohn, S, Kramer, M, Kwan, KK, Kwok, MW, Kwok, T, Langford, TJ, Lau, K, Lebanowski, L, Lee, J, Lee, JHC, Lei, RT, Leitner, R, Leung, JKC, Li, C, Li, DJ, Li, F, Li, GS, Li, QJ, Li, S, Li, SC, Li, WD, Li, XN, Li, YF, Li, ZB, Liang, H, Lin, CJ, Lin, GL, Lin, S, Lin, SK, Lin, Y-C, Ling, JJ, Link, JM, Littenberg, L, Littlejohn, BR, Liu, DW, Liu, JL, Liu, JC, Loh, CW, Lu, C, Lu, HQ, Lu, JS, Luk, KB, Lv, Z, Ma, QM, Ma, XY, Ma, XB, Ma, YQ, Malyshkin, Y, Caicedo, DAM, McKeown, RD, Mitchell, I, Mooney, M, Nakajima, Y, Napolitano, J, Naumov, D, Naumova, E, Ngai, HY, Ning, Z, Ochoa-Ricoux, JP, Olshevskiy, A, Pan, H-R, Park, J, Patton, S, Pec, V, Peng, JC, Pinsky, L, Pun, CSJ, Qi, FZ, Qi, M, Qian, X, Raper, N, Ren, J, Rosero, R, Roskovec, B, Ruan, XC, Steiner, H, Sun, GX, Sun, JL, Tang, W, Taychenachev, D, Treskov, K, Tsang, KV, Tull, CE, Viaux, N, Viren, B, Vorobel, V, Wang, CH, Wang, M, Wang, NY, Wang, RG, Wang, W, Wang, X, Wang, YF, Wang, Z, Wang, ZM, Wei, HY, Wen, LJ, Whisnant, K, White, CG, Whitehead, L, Wise, T, Wong, HLH, Wong, SCF, Worcester, E, Wu, C-H, Wu, Q, Wu, WJ, Xia, DM, Xia, JK, Xing, ZZ, Xu, JY, Xu, JL, Xu, Y, Xue, T, Yang, CG, Yang, H, Yang, L, Yang, MS, Yang, MT, Ye, M, Ye, Z, Yeh, M, Young, BL, Yu, ZY, Zeng, S, Zhan, L, Zhang, C, Zhang, HH, Zhang, JW, Zhang, QM, Zhang, XT, Zhang, YM, Zhang, YX, Zhang, ZJ, Zhang, ZY, Zhang, ZP, Zhao, J, Zhao, QW, Zhao, YB, Zhong, WL, Zhou, L, Zhou, N, Zhuang, HL, and Zou, JH

Abstract

The disappearance of reactor $\bar{\nu}_e$ observed by the Daya Bay experiment is examined in the framework of a model in which the neutrino is described by a wave packet with a relative intrinsic momentum dispersion $\sigma_\text{rel}$. Three pairs of nuclear reactors and eight antineutrino detectors, each with good energy resolution, distributed among three experimental halls, supply a high-statistics sample of $\bar{\nu}_e$ acquired at nine different baselines. This provides a unique platform to test the effects which arise from the wave packet treatment of neutrino oscillation. The modified survival probability formula was used to fit Daya Bay data, providing the first experimental limits: $2.38 \cdot 10^{-17} < \sigma_{\rm rel} < 0.23$. Treating the dimensions of the reactor cores and detectors as constraints, the limits are improved: $10^{-14} \lesssim \sigma_{\rm rel} < 0.23$, and an upper limit of $\sigma_{\rm rel} <0.20$ is obtained. All limits correspond to a 95\% C.L. Furthermore, the effect due to the wave packet nature of neutrino oscillation is found to be insignificant for reactor antineutrinos detected by the Daya Bay experiment thus ensuring an unbiased measurement of the oscillation parameters $\sin^22\theta_{13}$ and $\Delta m^2_{32}$ within the plane wave model.

Published
2017

8. GANGA: a user-Grid interface for Atlas and LHCb

Author

Harrison, K, Lavrijsen, WTLP, Mato, P, Soroko, A, Tan, CL, Tull, CE, Brook, N, Jones, RWL, Harrison, K, Lavrijsen, WTLP, Mato, P, Soroko, A, Tan, CL, Tull, CE, Brook, N, and Jones, RWL

Abstract

The Gaudi/Athena and Grid Alliance (GANGA) is a front-end for the configuration, submission, monitoring, bookkeeping, output collection, and reporting of computing jobs run on a local batch system or on the grid. In particular, GANGA handles jobs that use applications written for the Gaudi software framework shared by the Atlas and LHCb experiments. GANGA exploits the commonality of Gaudi-based computing jobs, while insulating against grid-, batch- and framework-specific technicalities, to maximize end-user productivity in defining, configuring, and executing jobs. Designed for a python-based component architecture, GANGA has a modular underpinning and is therefore well placed for contributing to, and benefiting from, work in related projects. Its functionality is accessible both from a scriptable command-line interface, for expert users and automated tasks, and through a graphical interface, which simplifies the interaction with GANGA for beginning and c1asual users. This paper presents the GANGA design and implementation, the development of the underlying software bus architecture, and the functionality of the first public GANGA release.

Published
2017

9. Independent measurement of the neutrino mixing angle theta(13) via neutron capture on hydrogen at Daya Bay

Author

An, FP, Balantekin, AB, Band, HR, Beriguete, W, Bishai, M, Blyth, S, Butorov, I, Cao, GF, Cao, J, Chan, YL, Chang, JF, Chang, LC, Chang, Y, Chasman, C, Chen, H, Chen, QY, Chen, SM, Chen, X, Chen, YX, Chen, Y, Cheng, YP, Cherwinka, JJ, Chu, MC, Cummings, JP, de Arcos, J, Deng, ZY, Ding, YY, Diwan, MV, Draeger, E, Du, XF, Dwyer, DA, Edwards, WR, Ely, SR, Fu, JY, Ge, LQ, Gill, R, Gonchar, M, Gong, GH, Gong, H, Gu, WQ, Guan, MY, Guo, XH, Hackenburg, RW, Han, GH, Hans, S, He, M, Heeger, M, Heng, YK, Hinrichs, P, Hor, YK, Hsiung, YB, Hu, BZ, Hu, LM, Hu, LJ, Hu, T, Hu, W, Huang, EC, Huang, H, Huang, XT, Huber, P, Hussain, G, Isvan, Z, Jaffe, DE, Jaffke, P, Jen, KL, Jetter, S, Ji, XP, Ji, XL, Jiang, HJ, Jiao, JB, Johnson, RA, Kang, L, Kettell, SH, Kramer, M, Kwan, KK, Kwok, MW, Kwok, T, Lai, WC, Lau, K, Lebanowski, L, Lee, J, Lei, RT, Leitner, R, Leung, A, Leung, JKC, Lewis, CA, Li, DJ, Li, F, Li, GS, Li, QJ, Li, WD, Li, XN, Li, XQ, Li, YF, Li, ZB, Liang, H, Lin, CJ, Lin, GL, Lin, PY, Lin, SK, Lin, YC, Ling, JJ, Link, JM, Littenberg, L, Littlejohn, BR, Liu, DW, Liu, H, Liu, JL, Liu, JC, Liu, SS, Liu, YB, Lu, C, Lu, HQ, Luk, KB, Ma, QM, Ma, XY, Ma, XB, Ma, YQ, McDonald, KT, McFarlane, MC, McKeown, RD, Meng, Y, Mitchell, I, Kebwaro, JM, Nakajima, Y, Napolitano, J, Naumov, D, Naumova, E, Nemchenok, I, Ngai, HY, Ning, Z, Ochoa-Ricoux, JP, Olshevski, A, Patton, S, Pec, V, Peng, JC, Piilonen, LE, Pinsky, L, Pun, CSJ, Qi, FZ, Qi, M, Qian, X, Raper, N, Ren, B, Ren, J, Rosero, R, Roskovec, B, Ruan, XC, Shao, BB, Steiner, H, Sun, GX, Sun, JL, Tam, YH, Tang, X, Themann, H, Tsang, KV, Tsang, RHM, Tull, CE, Tung, YC, Viren, B, Vorobel, V, Wang, CH, Wang, LS, Wang, LY, Wang, M, Wang, NY, Wang, RG, Wang, W, Wang, WW, Wang, X, Wang, YF, Wang, Z, Wang, ZM, Webber, DM, Wei, HY, Wei, YD, Wen, LJ, Whisnant, K, White, CG, Whitehead, L, Wise, T, Wong, HLH, Wong, SCF, Worcester, E, Wu, Q, Xia, DM, Xia, JK, Xia, X, Xing, ZZ, Xu, JY, Xu, JL, Xu, J, Xu, Y, Xue, T, Yan, J, Yang, CC, Yang, L, Yang, MS, Yang, MT, Ye, M, Yeh, M, Yeh, YS, Young, BL, Yu, GY, Yu, JY, Yu, ZY, Zang, SL, Zeng, B, Zhan, L, Zhang, C, Zhang, FH, Zhang, JW, Zhang, QM, Zhang, Q, Zhang, SH, Zhang, YC, Zhang, YM, Zhang, YH, Zhang, YX, Zhang, ZJ, Zhang, ZY, Zhang, ZP, Zhao, J, Zhao, QW, Zhao, Y, Zhao, YB, Zheng, L, Zhong, WL, Zhou, L, Zhou, ZY, Zhuang, HL, Zou, JH, and Collaboration, DB

Published
2014

10. GMA Instrumentation of the Athena Framework using NetLogger

Author

Tull, CE, Gunter, D, Lavrijsen, W, Quarrie, D, and Tierney, B

Subjects
FOS: Computer and information sciences, C.2.2, cs.DC, Computer Science - Distributed, Parallel, and Cluster Computing, cs.IR, Distributed, Parallel, and Cluster Computing (cs.DC), Information Retrieval (cs.IR), Computing and Computers, Computer Science - Information Retrieval
Abstract

Grid applications are, by their nature, wide-area distributed applications. This WAN aspect of Grid applications makes the use of conventional monitoring and instrumentation tools (such as top, gprof, LSF Monitor, etc) impractical for verification that the application is running correctly and efficiently. To be effective, monitoring data must be "end-to-end", meaning that all components between the Grid application endpoints must be monitored. Instrumented applications can generate a large amount of monitoring data, so typically the instrumentation is off by default. For jobs running on a Grid, there needs to be a general mechanism to remotely activate the instrumentation in running jobs. The NetLogger Toolkit Activation Service provides this mechanism. To demonstrate this, we have instrumented the ATLAS Athena Framework with NetLogger to generate monitoring events. We then use a GMA-based activation service to control NetLogger's trigger mechanism. The NetLogger trigger mechanism allows one to easily start, stop, or change the logging level of a running program by modifying a trigger file. We present here details of the design of the NetLogger implementation of the GMA-based activation service and the instrumentation service for Athena. We also describe how this activation service allows us to non-intrusively collect and visualize the ATLAS Athena Framework monitoring data. Grid applications are, by their nature, wide-area distributed applications. This WAN aspect of Grid applications makes the use of conventional monitoring and instrumentation tools (such as top, gprof, LSF Monitor, etc) impractical for verification that the application is running correctly and efficiently. To be effective, monitoring data must be end-to-end, meaning that all components between the Grid application endpoints must be monitored. Instrumented applications can generate a large amount of monitoring data, so typically the instrumentation is off by default. For jobs running on a Grid, there needs to be a general mechanism to remotely activate the instrumentation in running jobs. The NetLogger Toolkit Activation Service provides this mechanism. To demonstrate this, we have instrumented the ATLAS Athena Framework with NetLogger to generate monitoring events. We then use a GMA-based activation service to control NetLogger's trigger mechanism. The NetLogger trigger mechanism allows one to easily start, stop, or change the logging level of a running program by modifying a trigger file. We present here details of the design of the NetLogger implementation of the GMA-based activation service and the instrumentation service for Athena. We also describe how this activation service allows us to non-intrusively collect and visualize the ATLAS Athena Framework monitoring data.

Published
2003

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