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Energetic particles in magnetotail reconnection
KTH, School of Computer Science and Communication (CSC), High Performance Computing and Visualization (HPCViz).
KTH, School of Computer Science and Communication (CSC), High Performance Computing and Visualization (HPCViz).
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2015 (English)In: Journal of Plasma Physics, ISSN 0022-3778, E-ISSN 1469-7807, Vol. 81, 325810202Article in journal (Refereed) Published
Abstract [en]

We carried out a 3D fully kinetic simulation of Earth's magnetotail magnetic reconnection to study the dynamics of energetic particles. We developed and implemented a new relativistic particle mover in iPIC3D, an implicit Particle-in-Cell code, to correctly model the dynamics of energetic particles. Before the onset of magnetic reconnection, energetic electrons are found localized close to current sheet and accelerated by lower hybrid drift instability. During magnetic reconnection, energetic particles are found in the reconnection region along the x-line and in the separatrices region. The energetic electrons are first present in localized stripes of the separatrices and finally cover all the separatrix surfaces. Along the separatrices, regions with strong electron deceleration are found. In the reconnection region, two categories of electron trajectory are identified. First, part of the electrons are trapped in the reconnection region, bouncing a few times between the outflow jets. Second, part of the electrons pass the reconnection region without being trapped. Different from electrons, energetic ions are localized on the reconnection fronts of the outflow jets.

Place, publisher, year, edition, pages
2015. Vol. 81, 325810202
National Category
Physical Sciences
Identifiers
URN: urn:nbn:se:kth:diva-166497DOI: 10.1017/S0022377814001123ISI: 000352194000020Scopus ID: 2-s2.0-84925863043OAI: oai:DiVA.org:kth-166497DiVA: diva2:812239
Funder
Swedish Research Council
Note

QC 20150518

Available from: 2015-05-18 Created: 2015-05-11 Last updated: 2017-12-04Bibliographically approved
In thesis
1. Data Movement on Emerging Large-Scale Parallel Systems
Open this publication in new window or tab >>Data Movement on Emerging Large-Scale Parallel Systems
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Large-scale HPC systems are an important driver for solving computational problems in scientific communities. Next-generation HPC systems will not only grow in scale but also in heterogeneity. This increased system complexity entails more challenges to data movement in HPC applications. Data movement on emerging HPC systems requires asynchronous fine-grained communication and efficient data placement in the main memory. This thesis proposes an innovative programming model and algorithm to prepare HPC applications for the next computing era: (1) a data streaming model that supports emerging data-intensive applications on supercomputers, (2) a decoupling model that improves parallelism and mitigates the impact of imbalance in applications, (3) a new framework and methodology for predicting the impact of largescale heterogeneous memory systems on HPC applications, and (4) a data placement algorithm that uses a set of rules and a decision tree to determine the data-to-memory mapping in heterogeneous main memory.

The proposed approaches in this thesis are evaluated on multiple supercomputers with different processors and interconnect networks. The evaluation uses a diverse set of applications that represent conventional scientific applications and emerging data-analytic workloads on HPC systems. The experimental results on the petascale testbed show that the approaches obtain increasing performance improvements as system scale increases and this trend supports the approaches as a valuable contribution towards future HPC systems.

Abstract [sv]

Storskaliga HPC-system är en viktig drivkraft för att lösa datorproblem i vetenskapliga samhällen. Nästa generations HPC-system kommer inte bara att växa i skala utan också i heterogenitet. Denna ökade systemkomplexitet medför flera utmaningar för dataförflyttning i HPC-applikationer. Dataförflyttning på nya HPC-system kräver asynkron, finkorrigerad kommunikation och en effektiv dataplacering i huvudminnet.

Denna avhandling föreslår en innovativ programmeringsmodell och algoritm för att förbereda HPC-applikationer för nästa generation: (1) en dataströmningsmodell som stöder nya dataintensiva applikationer på superdatorer, (2) en kopplingsmodell som förbättrar parallelliteten och minskar obalans i applikationer, (3) en ny metologi och struktur för att förutse effekten av storskaliga, heterogena minnessystem på HPC-applikationer, och (4) en datalägesalgoritm som använder en uppsättning av regler och ett beslutsträd för att bestämma kartläggningen av data-till-minnet i det heterogena huvudminnet.

Den föreslagna programmeringsmodellen i denna avhandling är utvärderad på flera superdatorer med olika processorer och sammankopplingsnät. Utvärderingen använder en mängd olika applikationer som representerar konventionella vetenskapliga applikationer och nya dataanalyser på HPC-system. Experimentella resultat på testbädden i petascala visar att programmeringsmodellen förbättrar prestandan när systemskalan ökar. Denna trend indikerar att modellen är ett värdefullt bidrag till framtida HPC-system.

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2017. 116 p.
Series
TRITA-CSC-A, ISSN 1653-5723 ; 2017:25
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-218338 (URN)978-91-7729-592-1 (ISBN)
Public defence
2017-12-18, F3, Lindstedtsvägen 26, Stockholm, 10:00 (English)
Opponent
Supervisors
Note

QC 20171128

Available from: 2017-11-28 Created: 2017-11-27 Last updated: 2017-11-28Bibliographically approved

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Laure, ErwinMarkidis, Stefano

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