Brayns for neuro-robotics

Last tuesday, I presented how Brayns could be used to render high quality images for our colleagues from the neuro-robotics team. Brayns is hardware agnostic and it takes no more than one command line argument to switch between OSPRay (CPU) and OptiX (GPU) backends. The following video shows Brayns in action, on a 24MPixel display wall! Brayns is running on 1 machine powered by 2 Quadro K5000 NVIDIA GPUs.

SIMD accelerated voxelizer

SIMDVoxelizer is a CPU-based voxalizer taking advantage of vectorization units to speed up creation of 8bit raw volumes.

usage: SIMDVoxelizer <voxel_size> <cutoff_distance> <input_file> <output_file>

Input file is a binary array of floats: x, y, z, radius and value of elements. Each voxel of the final volume contains the sum of all elements with a weight that correspond to the value of the element divided by its squared distance to the voxel. Note that in the final volume, values are normalized.

This is currently a brute force implementation that produces accurate 8bit volumes.

The <output_file> is suffixed by the size of the volume.

SIMDVoxelizer makes use of the Intel ISPC compiler and requires ispc to be in the PATH.

To build SIMDVoxelizer, simply run make in the source folder.

Source code available on github.

Volume rendering and raytracing ... merged!

It's there, merged and operational in Brayns, and the first results are quite exciting.

Mixing volume rendering and ray-tracing allows more control on atmospheric effects which, in the case of neurosciences, can be used to represent the electromagnetic fields surrounding the neurons.

Volume Rendering coupled to Ray-Tracing: a win-win combination

I am currently working at adding Volume Rendering to the existing Brayns implementation. Using volumes clearly is the way to go to represent the activity that takes place outside of the geometry. Ray-tracing, on the other hand, concentrates on high quality surface rendering, processing shadows and other global illumination.

Many thanks to my colleagues  Raphael and Grigori without whom that development would have taken a few more days!


Currently in my volume branch, but soon to be merged into master!!

Framework for efficient synthesis of spatially embedded morphologies

I was delighted to contribute to this paper:

Framework for efficient synthesis of spatially embedded morphologies

 

Abstract:

Many problems in science and engineering require the ability to grow tubular or polymeric structures up to large volume fractions within a bounded region of three-dimensional space. Examples range from the construction of fibrous materials and biological cells such as neurons, to the creation of initial configurations for molecular simulations. A common feature of these problems is the need for the growing structures to wind throughout space without intersecting. At any time, the growth of a morphology depends on the current state of all the others, as well as the environment it is growing in, which makes the problem computationally intensive. Neuron synthesis has the additional constraint that the morphologies should reliably resemble biological cells, which possess nonlocal structural correlations, exhibit high packing fractions, and whose growth responds to anatomical boundaries in the synthesis volume. We present a spatial framework for simultaneous growth of an arbitrary number of nonintersecting morphologies that presents the growing structures with information on anisotropic and inhomogeneous properties of the space. The framework is computationally efficient because intersection detection is linear in the mass of growing elements up to high volume fractions and versatile because it provides functionality for environmental growth cues to be accessed by the growing morphologies. We demonstrate the framework by growing morphologies of various complexity.

Proud to appear in that paper!

OSPRay – A CPU Ray Tracing Framework for Scientific Visualization

In the news: Top500.org

Brayns in the news, thanks to a amazing collaboration with the Intel team.

Using Intel’s Xeon Phi for Brain Research Visualisation

https://www.top500.org/news/using-intels-xeon-phi-for-brain-research-visualization/

Bringing interactive ray-tracing to neuroscience

Brayns, the application I've been working on for 6 months at EPFL, is now open-source!

http://github.com/bluebrain/brayns

To me, this application represents the first step to bringing interactive ray-tracing to neuroscience. Very exciting moments and millions of ideas are on their way. Stay tuned!

ISC 2016 Tech Demo on Intel's new KNL architecture

Had a great time presenting Brayns at the Intel booth ISC2016.

 

Brayns is finally open-source, star it, fork it and contribute to introducing ray-tracing to the world of Neuro-Science. 

https://github.com/bluebrain/brayns

Brayns was presented running on 4 KNL nodes and was running amazingly fast! Up to 60fps in Full-HD on a 500 million geometries (spheres, cones and cylinders) model.

Press release: https://www.hpcwire.com/2016/07/11/isc-2016-highlights-didnt-make-missed-intel/

Short trip in the virtual brain

Work in progress... Cool camera lens effects coupled with ambient occlusion and light emitting objects, thanks to ray-tracing.

Souce code is meant to go Open-source in the next few weeks on the Blue Brain github repository.

Stay tuned!

Stepping into the virtual brain

Abstract:

One of the keys towards understanding how the brain works as a whole is visualisation of how the individual cells function; photo-realistic rendering is therefore important.

Ray-tracing can help to highlight areas of the circuits where cells touch each other and where synapses are being created. In combination with ‘global illumination’, which uses light, shadow, and depth of field effects to simulate photo-realistic images, this technique makes it easier to visualise how the neurons function.

Slides of the talk given in Tokyo, about interactive ray-tracing in the context of brain visualization.