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000863837 0247_ $$2doi$$a10.1109/EMPDP.2019.8671630
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000863837 037__ $$aFZJ-2019-03816
000863837 1001_ $$0P:(DE-Juel1)172093$$aOden, Lena$$b0$$eCorresponding author$$ufzj
000863837 1112_ $$a2019 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)$$cPavia$$d2019-02-13 - 2019-02-15$$gPDP 2019$$wItaly
000863837 245__ $$aIO Challenges for Human Brain Atlasing Using Deep Learning Methods - An In-Depth Analysis
000863837 260__ $$bIEEE$$c2019
000863837 300__ $$a291-298
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000863837 520__ $$aThe use of Deep Learning methods have been identified as a key opportunity for enabling processing of extreme-scale scientific datasets. Feeding data into compute nodes equipped with several high-end GPUs at sufficiently high rate is a known challenge. Facilitating processing of these datasets thus requires the ability to store petabytes of data as well as to access the data with very high bandwidth. In this work, we look at two Deep Learning use cases for cytoarchitectonic brain mapping. These applications are very challenging for the underlying IO system. We present an in depth analysis of their IO requirements and performance. Both applications are limited by the IO performance, as the training processes often have to wait several seconds for new training data. Both applications read random patches from a collection of large HDF5 datasets or TIFF files, which result in many small non-consecutive accesses to the parallel file systems. By using a chunked data format or storing temporally copies of the required patches, the IO performance can be improved significantly. These leads to a decrease of the total runtime of up to 80%.
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000863837 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x2
000863837 536__ $$0G:(EU-Grant)720270$$aHBP SGA1 - Human Brain Project Specific Grant Agreement 1 (720270)$$c720270$$fH2020-Adhoc-2014-20$$x3
000863837 588__ $$aDataset connected to CrossRef Conference
000863837 7001_ $$0P:(DE-Juel1)170068$$aSchiffer, Christian$$b1$$ufzj
000863837 7001_ $$0P:(DE-Juel1)167110$$aSpitzer, Hannah$$b2$$ufzj
000863837 7001_ $$0P:(DE-Juel1)165746$$aDickscheid, Timo$$b3$$ufzj
000863837 7001_ $$0P:(DE-Juel1)144441$$aPleiter, Dirk$$b4$$ufzj
000863837 773__ $$a10.1109/EMPDP.2019.8671630
000863837 8564_ $$uhttps://juser.fz-juelich.de/record/863837/files/08671630.pdf$$yRestricted
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000863837 9131_ $$0G:(DE-HGF)POF3-511$$1G:(DE-HGF)POF3-510$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lSupercomputing & Big Data$$vComputational Science and Mathematical Methods$$x0
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000863837 9141_ $$y2019
000863837 9201_ $$0I:(DE-Juel1)INM-1-20090406$$kINM-1$$lStrukturelle und funktionelle Organisation des Gehirns$$x0
000863837 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x1
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