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A Data streaming model in MPI
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).ORCID iD: 0000-0003-0639-0639
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).ORCID iD: 0000-0002-9901-9857
Show others and affiliations
2015 (English)In: Proceedings of the 3rd ExaMPI Workshop at the International Conference on High Performance Computing, Networking, Storage and Analysis, SC 2015, ACM Digital Library, 2015Conference paper, Published paper (Refereed)
Abstract [en]

Data streaming model is an effective way to tackle the chal-lenge of data-intensive applications. As traditional HPC applications generate large volume of data and more data-intensive applications move to HPC infrastructures, it is nec-essary to investigate the feasibility of combining message-passing and streaming programming models. MPI, the de facto standard for programming on HPC, cannot intuitively express the communication pattern and the functional op-erations required in streaming models. In this work, we de-signed and implemented a data streaming library MPIStream atop MPI to allocate data producers and consumers, to stream data continuously or irregularly and to process data at run-Time. In the same spirit as the STREAM benchmark, we developed a parallel stream benchmark to measure data processing rate. The performance of the library largely de-pends on the size of the stream element, the number of data producers and consumers and the computational intensity of processing one stream element. With 2,048 data produc-ers and 2,048 data consumers in the parallel benchmark, MPIStream achieved 200 GB/s processing rate on a Blue Gene/Q supercomputer. We illustrate that a streaming li-brary for HPC applications can effectively enable irregular parallel I/O, application monitoring and threshold collective operations. © 2015 ACM.

Place, publisher, year, edition, pages
ACM Digital Library, 2015.
Keywords [en]
Data-intensive, HPC, MPI, Streaming model, Data reduction, Digital storage, Functional programming, Message passing, Supercomputers, Application monitoring, Collective operations, Communication pattern, Computational intensity, Data intensive, Data-intensive application, Parallel benchmarks, Data handling
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-201832DOI: 10.1145/2831129.2831131Scopus ID: 2-s2.0-85009188222ISBN: 9781450339988 (print)OAI: oai:DiVA.org:kth-201832DiVA, id: diva2:1074952
Conference
3rd Workshop on Exascale MPI, ExaMPI 2015, 15 November 2015
Note

QC 20170216

Available from: 2017-02-16 Created: 2017-02-16 Last updated: 2017-11-27Bibliographically 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. p. 116
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: 2018-01-13Bibliographically approved

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