TY - GEN
T1 - ASAGI - A parallel server for adaptive geoinformation
AU - Rettenberger, Sebastian
AU - Meister, Oliver
AU - Bader, Michael
AU - Gabriely, Alice Agnes
PY - 2016/4/26
Y1 - 2016/4/26
N2 - We present ASAGI, an open-source library with a simple interface to access Cartesian material and geographic datasets in massively parallel simulations with dynamically adaptive mesh refinement (AMR). ASAGI distributes geographic datasets over all compute nodes storing only a portion of the dataset on each node. An automatic replication mechanism copies the data between nodes to assure fast local access even after load migration in the application. We demonstrate ASAGI's preparedness for up-to-petascale simulations in three use cases. We simulate a Tsunami on 512 cores and a porous media ow on up to 8,192 cores of SuperMUC with the AMR framework sam(oa)2. We also run an earthquake simulation with SeiSol on 65,536 cores. For all applications, ASAGI provides large complex 3D material datasets required for the realistic scenarios. The NUMA-awareness of ASAGI turned out to be especially useful for the hybrid MPI+OpenMP parallelization of both codes.
AB - We present ASAGI, an open-source library with a simple interface to access Cartesian material and geographic datasets in massively parallel simulations with dynamically adaptive mesh refinement (AMR). ASAGI distributes geographic datasets over all compute nodes storing only a portion of the dataset on each node. An automatic replication mechanism copies the data between nodes to assure fast local access even after load migration in the application. We demonstrate ASAGI's preparedness for up-to-petascale simulations in three use cases. We simulate a Tsunami on 512 cores and a porous media ow on up to 8,192 cores of SuperMUC with the AMR framework sam(oa)2. We also run an earthquake simulation with SeiSol on 65,536 cores. For all applications, ASAGI provides large complex 3D material datasets required for the realistic scenarios. The NUMA-awareness of ASAGI turned out to be especially useful for the hybrid MPI+OpenMP parallelization of both codes.
KW - Adaptive mesh refinement
KW - Geoinformation
KW - Large scale applications
KW - Realistic simulations
UR - http://www.scopus.com/inward/record.url?scp=85014774555&partnerID=8YFLogxK
U2 - 10.1145/2938615.2938618
DO - 10.1145/2938615.2938618
M3 - Conference contribution
AN - SCOPUS:85014774555
T3 - ACM International Conference Proceeding Series
BT - Proceedings of the 2016 Exascale Applications and Software Conference, EASC 2016
PB - Association for Computing Machinery
T2 - 2016 Exascale Applications and Software Conference, EASC 2016
Y2 - 25 April 2016 through 29 April 2016
ER -