By Katrin Amunts, Lucio Grandinetti, Thomas Lippert, Nicolai Petkov
This booklet constitutes revised chosen papers from the second one foreign Workshop on Brain-Inspired Computing, BrainComp 2015, held in Cetraro, Italy, in July 2015.
The 14 papers provided during this quantity have been rigorously reviewed and chosen for inclusion during this publication. They take care of mind constitution and serve as; computational types and brain-inspired computing equipment with functional purposes; excessive functionality computing; and visualization for mind simulations.
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Extra info for Brain-Inspired Computing: Second International Workshop, BrainComp 2015, Cetraro, Italy, July 6-10, 2015, Revised Selected Papers
Overview of the computation and processing time (including time spent for the manual repairs) for the individual processing levels presented in Fig. 3. 5 years The maximum resolution for the nonlinear alignment, which was limited by the available amount of memory per core, was achieved on JUROPA at an inplane resolution of 40 µm. g. mask images), and the size and data type (which is double) of the nonlinear deformation vector ﬁeld. 5 TiB of disk space have been computed while the original version of the ﬁrst BigBrain required more than 2,000,000 core-h and 5 years of processing.
AB94,BJHD96,GSAW05,HFB+05,HFBF12, Hua92,HZ09,KB90,MG98,WMS09,ZCUM11]) and individually adopted to the characteristics of the input images, such as color mode and range, contrast, image size, texture. In the following, we will describe how we adopted the seeded region growing algorithm described in [AB94] for the speciﬁc requirements posed by 3DPLI. We semi-automized and parallelized the algorithm in a ﬂexible way enabling the usage of the same software on notebooks and desktop computers as well as on (GPU) clusters and supercomputers.
5 GHz 12-core processors and 128 GiB of main memory. In total, this led to a 24 H. Mohlberg et al. 8 Petaﬂops. In contrast to JUROPA, the new system uses a Slurm batch system with Parastation resource management. Python powered by mpi4py was used to implement the workﬂows, which scaled up well to the (so far) maximum allocation of 512 cores. Going from the bottom level to the highest stage of execution level that has already been reached for each execution level of the workﬂow, we executed a cold run, where no output ﬁles were generated.