[Bf-cycles] split kernel and CUDA - Bf-cycles Digest, Vol 61, Issue 9
marc at binsted.plus.com
Thu May 19 13:36:59 CEST 2016
I first contacted Ton, mid-last year for advice on how to approach you
guys with a project I am working on.
I would be very interested in being involved with the development of
Cycles, in particular for performance rendering and baking of physics
and simulation systems within the GPU. I am currently developing a GPU
based scalable cluster model for CGI to take advantage of many core
processing, utilising Blender Cycles, Lux Render, Furry Ball and soon
Octane, which has taken 18 months so far to research and build. I have
been working on BVH building(QVBH and SVBH) and sorting algorithms,
which could possibly be utilised with existing kernels. I have also
experimented successfully with multi-GPU out-of-core memory and
inter-GPU communication across PCIe and utilising various RDMA
techniques, which can be developed as driver-aware with transparent
operation under the cover however I have not been able to succesfully
test through application recompiling as yet.
I would like to see the benefits of efficient resource handling and,
with full GPU utilisation, hence I would be interested to experiment
with the simulation systems within Blender if anybody is developing GPU
kernels for it via CUDA/OpenGL and also working with developers involved
in the Cycles engine, with possible development of a micro-kernel
library, or run-time compile for more efficient handling of the GPUs,
scene geometry and work division, optimal tiling dimensions per
architecture and improved memory handling for large scene files which
will often defeat the GPU. I have been working with CUDA for a couple of
years now, and have some knowledge of Nvidia Optix.
I would also like to offer the potential access to the system for
development, testing and trial renders. A further commercialised system
will go online later in the year.
I look forward to hearing from you.
On 19/05/2016 11:00, bf-cycles-request at blender.org wrote:
> Re: split kernel and CUDA
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