Hybrid parallelization of a seeded region growing segmentation of brain images for a GPU cluster

The introduction of novel imaging technologies always carries new challenges regarding the processing of the captured images. Polarized Light Imaging (PLI) is such a new technique. It enables the mapping of single nerve fibers in postmortem human brains in unprecedented detail. Due to the very high resolution at sub-millimeter scale, an immense amount of image data has to be reconstructed three-dimensionally before it can be analyzed. Some of the steps in the reconstruction pipeline require a previous segmentation of the large images. This task of image processing creates black-and-white masks indicating the object and background pixels of the original images. It has turned out that a seeded region growing approach achieves segmentation masks of the desired quality. To be able to process the immense number of images acquired with PLI, the region growing has to be parallelized for a supercomputer. However, the choice of the seeds has to be automated in order to enable a parallel execution. A hybrid parallelization has been applied to the automated seeded region growing to exploit the architecture of a GPU cluster. The hybridity consists of an MPI parallelization and the execution of some well-chosen, data-parallel subtasks on GPUs. This approach achieves a linear speedup behavior so that the runtime can be reduced to a reasonable amount

Scaling up your research - Supercomputing in a nutshell

The NEST simulator is designed to run on a wide range of systems, from notebooks to supercomputers, as demonstrated in several publications like [Jordan et al 2018]. Using supercomputers as simulation backend enables the scientists to simulate significantly larger neuronal networks as compared to what is possible on a notebook or desktop PC.This talk will give an introduction to supercomputing, the European High-Performance Computing (HPC) landscape, how to get access to a suitable HPC system, and how the Simulation Lab Neuroscience supports neuroscientists in getting started with HPC. Moreover, it will be described which additional benefits and opportunities the High Performance Analytics and Computing Platform of the Human Brain Project provides to NEST users and developers. [Jordan et al 2018] https://doi.org/10.3389/fninf.2018.0000

Comment on "Are the robots coming for our brain?" by Dr. Peter Asaro

What can we actually expect from possible intelligent machines, and what should we anticipate? Can we agree on basic rules for designing robots, and could they become smart enough to override them? How would desirable robots and robotics look like? What is strong AI and panpsychism, and what does it have to do with each other? What are the challenges and possibilities of brain-computer interfaces?The Human Brain Project (HBP) has steadily moved forward over the past few years.The HBP has:- Launched the high-performance computing platform that allows easy access to high performance computing for the scientific community;- Made neuromorphic computing experiments available without complex hardware experience;- Enabled researchers to use models of the brain in virtual robotics experiments.All are technological advances with great promises for the future.On the 9th of October 2015, The HBP had invited prominent external experts to explore ethical, social and technical issues related to these recent technological and scientific advances. We invite you to explore the conversations that ensued below:The online debate was organised by the Danish Board of Technology Foundation in collaboration with the HBP Foresight Lab at King’s College, London. Both partners of the HBP in the “Ethics and Society” subproject of the Human Brain Project (HBP)