Discover and read the best of Twitter Threads about #CUDA
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On #biocompute news, $TSLA Tesla AI day gave some technical details on how they do their training and video labelling. It seems $NVDA Nvidia GPUs are the norm, with a 14,000 HPC heavily optimised on the software side. #pytorch #AVX2 #CUDA #SMT #smol #CUDNN 
None of this is for the purpose of #Bioinformatics applications, rather here it's for Full Self-Driving software, but the technical details show some choices for high-throughput #AI training that one could compare to the #ComputationalBiology #ComputeAcceleration world.
More at bit.ly/biocompute
NVIDIA GTC starts today! There are tons of exciting topics and webinars covered. This year again the whole conference is online and free, so go and register if you have not done so already.
Here are a few special highlight sessions:
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Here are a few special highlight sessions:
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GTC 2022 Keynote - September: lnkd.in/gYNqxsnr
How CUDA Programming Works: lnkd.in/gKmdjZub
Building the Future of Work with AI-powered Digital Humans: lnkd.in/gXJWk6vz
Building Future-Ready Intelligence for Cars: lnkd.in/gJ9BJMGM
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How CUDA Programming Works: lnkd.in/gKmdjZub
Building the Future of Work with AI-powered Digital Humans: lnkd.in/gXJWk6vz
Building Future-Ready Intelligence for Cars: lnkd.in/gJ9BJMGM
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A Deep Dive into RAPIDS for Accelerated Data Science and Data Engineering: lnkd.in/gM7mquwc
A Deep Dive into the Latest HPC Software: lnkd.in/ghXxGmar
Cross-Framework Model Evaluation and Accelerated Training with NVIDIA Merlin: lnkd.in/gXUEdajH
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A Deep Dive into the Latest HPC Software: lnkd.in/ghXxGmar
Cross-Framework Model Evaluation and Accelerated Training with NVIDIA Merlin: lnkd.in/gXUEdajH
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"Floating Worlds" simulation
Full HD version:
See comments for more info.
#ArtificialLife #ALife #GenerativeArt #CUDA
Full HD version:
See comments for more info.
#ArtificialLife #ALife #GenerativeArt #CUDA
Behind the scenes:
The thermo-mechanics is driven by a particle engine while the #ALife stuff is computed on top of it by an information transportation and action layer.
The behavior of the tiny machines results from the interaction of their building blocks.
The thermo-mechanics is driven by a particle engine while the #ALife stuff is computed on top of it by an information transportation and action layer.
The behavior of the tiny machines results from the interaction of their building blocks.
This demo video was created to give you an overview of the state of development of the upcoming major version (still in progress!).
The already implemented features can be tried out here: github.com/chrxh/alien
The simulation tool has been renewed and should now be easy to use.
The already implemented features can be tried out here: github.com/chrxh/alien
The simulation tool has been renewed and should now be easy to use.
(1) Possible buyout of #ARM by #NVIDIA does have an effect on the #Bioinformatics field: many applications now are deployable on CPU/GPUs with #ARM and/or #NVIDIA chips on them. Some recent examples are:
(a) the Oxford @nanopore MinION Mk1c device, which originally was specced at Jetson TX2 ARM+Pascal GPU accelerators (ARM processor 6 cores, 256 Core GPU), 8 GB RAM (may have changed since then.
For those interested in details:
It is following the approach outlined in sections 7.1.3 and 7.2 of this paper describing RenderMan:
graphics.pixar.com/library/Render…
The error metric is from section 2.1 here: pdfs.semanticscholar.org/8329/759ae51c9…
It is following the approach outlined in sections 7.1.3 and 7.2 of this paper describing RenderMan:
graphics.pixar.com/library/Render…
The error metric is from section 2.1 here: pdfs.semanticscholar.org/8329/759ae51c9…
In layman's terms: Every other sample is written to a separate buffer. By comparing this extra buffer to the main image buffer, the renderer can estimate convergence.
Pixels receive progressively more samples until a convergence threshold or sample count limit is reached.
Pixels receive progressively more samples until a convergence threshold or sample count limit is reached.
