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Idle waves in high-performance computing
KTH, School of Computer Science and Communication (CSC), High Performance Computing and Visualization (HPCViz).ORCID iD: 0000-0003-0639-0639
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).
KTH, School of Computer Science and Communication (CSC), High Performance Computing and Visualization (HPCViz).ORCID iD: 0000-0003-1603-5294
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2015 (English)In: Physical Review E. Statistical, Nonlinear, and Soft Matter Physics, ISSN 1539-3755, E-ISSN 1550-2376, Vol. 91, no 1, 013306- p.Article in journal (Refereed) Published
Abstract [en]

The vast majority of parallel scientific applications distributes computation among processes that are in a busy state when computing and in an idle state when waiting for information from other processes. We identify the propagation of idle waves through processes in scientific applications with a local information exchange between the two processes. Idle waves are nondispersive and have a phase velocity inversely proportional to the average busy time. The physical mechanism enabling the propagation of idle waves is the local synchronization between two processes due to remote data dependency. This study provides a description of the large number of processes in parallel scientific applications as a continuous medium. This work also is a step towards an understanding of how localized idle periods can affect remote processes, leading to the degradation of global performance in parallel scientific applications.

Place, publisher, year, edition, pages
2015. Vol. 91, no 1, 013306- p.
Keyword [en]
Continuous medium, Global performance, High performance computing, Local information, Local synchronizations, Parallel scientific applications, Physical mechanism, Scientific applications
National Category
Physical Sciences
Identifiers
URN: urn:nbn:se:kth:diva-160745DOI: 10.1103/PhysRevE.91.013306ISI: 000348330600020PubMedID: 25679738Scopus ID: 2-s2.0-84921638180OAI: oai:DiVA.org:kth-160745DiVA: diva2:791873
Note

QC 20150302

Available from: 2015-03-02 Created: 2015-02-27 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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Markidis, StefanoAkhmetova, DanaLaure, Erwin

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