guest ::
| Home > Publications database > IO Challenges for Human Brain Atlasing Using Deep Learning Methods - An In-Depth Analysis > print |
| Home > Publications database > IO Challenges for Human Brain Atlasing Using Deep Learning Methods - An In-Depth Analysis > print |
| 001 | 863837 | ||
| 005 | 20210130002329.0 | ||
| 024 | 7 | _ | |a 10.1109/EMPDP.2019.8671630 |2 doi |
| 024 | 7 | _ | |a WOS:000467257000041 |2 WOS |
| 037 | _ | _ | |a FZJ-2019-03816 |
| 100 | 1 | _ | |a Oden, Lena |0 P:(DE-Juel1)172093 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a 2019 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) |g PDP 2019 |c Pavia |d 2019-02-13 - 2019-02-15 |w Italy |
| 245 | _ | _ | |a IO Challenges for Human Brain Atlasing Using Deep Learning Methods - An In-Depth Analysis |
| 260 | _ | _ | |c 2019 |b IEEE |
| 300 | _ | _ | |a 291-298 |
| 336 | 7 | _ | |a CONFERENCE_PAPER |2 ORCID |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Conference Paper |2 DataCite |
| 336 | 7 | _ | |a Contribution to a conference proceedings |b contrib |m contrib |0 PUB:(DE-HGF)8 |s 1563201981_11237 |2 PUB:(DE-HGF) |
| 520 | _ | _ | |a The 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%. |
| 536 | _ | _ | |a 511 - Computational Science and Mathematical Methods (POF3-511) |0 G:(DE-HGF)POF3-511 |c POF3-511 |f POF III |x 0 |
| 536 | _ | _ | |a 574 - Theory, modelling and simulation (POF3-574) |0 G:(DE-HGF)POF3-574 |c POF3-574 |f POF III |x 1 |
| 536 | _ | _ | |a HBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907) |0 G:(EU-Grant)785907 |c 785907 |f H2020-SGA-FETFLAG-HBP-2017 |x 2 |
| 536 | _ | _ | |a HBP SGA1 - Human Brain Project Specific Grant Agreement 1 (720270) |0 G:(EU-Grant)720270 |c 720270 |f H2020-Adhoc-2014-20 |x 3 |
| 588 | _ | _ | |a Dataset connected to CrossRef Conference |
| 700 | 1 | _ | |a Schiffer, Christian |0 P:(DE-Juel1)170068 |b 1 |u fzj |
| 700 | 1 | _ | |a Spitzer, Hannah |0 P:(DE-Juel1)167110 |b 2 |u fzj |
| 700 | 1 | _ | |a Dickscheid, Timo |0 P:(DE-Juel1)165746 |b 3 |u fzj |
| 700 | 1 | _ | |a Pleiter, Dirk |0 P:(DE-Juel1)144441 |b 4 |u fzj |
| 773 | _ | _ | |a 10.1109/EMPDP.2019.8671630 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/863837/files/08671630.pdf |y Restricted |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/863837/files/08671630.pdf?subformat=pdfa |x pdfa |y Restricted |
| 909 | C | O | |o oai:juser.fz-juelich.de:863837 |p openaire |p VDB |p ec_fundedresources |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)172093 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 1 |6 P:(DE-Juel1)170068 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)167110 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 3 |6 P:(DE-Juel1)165746 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 4 |6 P:(DE-Juel1)144441 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |1 G:(DE-HGF)POF3-510 |0 G:(DE-HGF)POF3-511 |2 G:(DE-HGF)POF3-500 |v Computational Science and Mathematical Methods |x 0 |4 G:(DE-HGF)POF |3 G:(DE-HGF)POF3 |l Supercomputing & Big Data |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Decoding the Human Brain |1 G:(DE-HGF)POF3-570 |0 G:(DE-HGF)POF3-574 |2 G:(DE-HGF)POF3-500 |v Theory, modelling and simulation |x 1 |4 G:(DE-HGF)POF |3 G:(DE-HGF)POF3 |
| 914 | 1 | _ | |y 2019 |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-1-20090406 |k INM-1 |l Strukturelle und funktionelle Organisation des Gehirns |x 0 |
| 920 | 1 | _ | |0 I:(DE-Juel1)JSC-20090406 |k JSC |l Jülich Supercomputing Center |x 1 |
| 980 | _ | _ | |a contrib |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)INM-1-20090406 |
| 980 | _ | _ | |a I:(DE-Juel1)JSC-20090406 |
| 980 | _ | _ | |a UNRESTRICTED |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|