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hardware:computer [2021/09/10 21:21]
Jon Daniels [Acquisition] added link to Arxiv paper 		hardware:computer [2024/01/31 01:27]
Jon Daniels [Data Analysis]
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 Having lots of RAM speeds the analysis; ideally the entire dataset can be held in active memory.  Ideally get a computer with CUDA-capable graphics card because some of the data analysis software can take advantage of it to speed the computation (OpenCL is a competing framework for GPU computation).  This is a nascent area and depends on software support; many software developments data analysis are forthcoming so it's hard to say exactly what will be the best hardware in the long run. Having lots of RAM speeds the analysis; ideally the entire dataset can be held in active memory.  Ideally get a computer with CUDA-capable graphics card because some of the data analysis software can take advantage of it to speed the computation (OpenCL is a competing framework for GPU computation).  This is a nascent area and depends on software support; many software developments data analysis are forthcoming so it's hard to say exactly what will be the best hardware in the long run.
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 +Micro-Manager 2.0 has a helpful ability to reslice data into the "normal frame".  The GPU version of the algorithm can operate on datasets up to 1/4 of the GPU memory, e.g. 2 GB datasets can be processed on a GPU with 8 GB of working memory.
  
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