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hardware:computer [2024/01/31 01:27]
Jon Daniels [Data Analysis] |
hardware:computer [2024/02/14 17:55] (current)
Jon Daniels [Data Analysis] |
| ===== Data Analysis ===== | ===== 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. | Some users do image analysis and processing on a separate workstation, others use the acquisition computer when it's not being used for acquisition. |
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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. | Having lots of RAM can speed any 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 "lab frame" which is helpful especially for stage scanning data. 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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| ===== Specific suggestions ===== | ===== Specific suggestions ===== |
