TY  - CONF
AU  - Kumbhar, Pramod
AU  - Hines, Michael
AU  - Ovcharenko, Aleksandr
AU  - Alvarez, Damian
AU  - King, James
AU  - Sainz, Florentino
AU  - Schürmann, Felix
AU  - Delalondre, Fabien
TI  - Leveraging a Cluster-Booster Architecture for Brain-Scale Simulations
VL  - 9697
CY  - Cham
PB  - Springer International Publishing
M1  - FZJ-2016-03899
SN  - 978-3-319-41320-4 (print)
T2  - Lecture Notes in Computer Science
SP  - 363 - 380
PY  - 2016
AB  - The European Dynamical Exascale Entry Platform (DEEP) is an example of a new type of heterogeneous supercomputing architecture that include both a standard multicore-based “Cluster” used to run less scalable parts of an application, and an Intel MIC-based “Booster” used to run highly scalable compute kernels. In this paper we describe how the compute engine of the widely used NEURON scientific application has been ported on both the DEEP and the Intel MIC platform. We discuss the design and implementation of the core simulator with an emphasis on the development workflow and implementation details that enable the efficient use of the new “Cluster-Booster” type of architectures. We describe optimizations of the data structures and algorithms tailored to the Intel Xeon Phi coprocessor which contributed to improve the overall performance of NEURON by a factor 5. Validation of our implementation has first been done on STAMPEDE supercomputer in order to emulate the DEEP architecture performance. Building on these results, we then explored opportunities offered by the DEEP platform to efficiently support complex scientific workflow.
T2  - 31st International Conference High Performance Computing
CY  - 19 Jun 2016 - 23 Jun 2016, Frankfurt (Germany)
Y2  - 19 Jun 2016 - 23 Jun 2016
M2  - Frankfurt, Germany
LB  - PUB:(DE-HGF)8 ; PUB:(DE-HGF)7
UR  - <Go to ISI:>//WOS:000386513900019
DO  - DOI:10.1007/978-3-319-41321-1_19
UR  - https://juser.fz-juelich.de/record/811415
ER  -