NumSharp GPU acceleration #111

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1. Oceania2018

   added

   enhancementNew feature or requestNew feature or request

   help wantedExtra attention is neededExtra attention is needed

   further discussNeeds discussion before implementation can proceedNeeds discussion before implementation can proceed

   on Nov 16, 2018
2. fdncred commented on Nov 16, 2018

   

   fdncred

   on Nov 16, 2018

   Contributor

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   @Oceania2018, I was going to suggest the same thing. We don't want to be writing cuda .cu files. It's not easy. However, my first suggestion would be to parallelize the code where possible. i.e. on IEnumerables we could use .AsParallel(), on for loops we could use Parallel.For and Parallel.Foreach. BenchmarkDotNet will be helpful here.
3. ZhiZe-ZG commented on Feb 26, 2022

   

   ZhiZe-ZG

   on Feb 26, 2022

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   This function is very important in some occasions where accelerated computing is required but neural networks (and torch) are not suitable.
4. Oceania2018 commented on Feb 26, 2022

   

   Oceania2018

   on Feb 26, 2022

   MemberAuthor

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   We switched to Tensorflow.Numpy.