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hardware:computer [2021/02/13 21:28]
Jon Daniels [Acquisition] 		hardware:computer [2024/01/31 01:27]
Jon Daniels [Data Analysis]
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 100 MB/sec is typical for a magnetic hard drive.  300 MB/sec is typical for a single SSD.  If the data rate is too high for a single SSD, use SSDs in RAID0 configuration (e.g. 4 SSDs in RAID0 can achieve >1 GB/s).  Lately M.2 drives with PCIe interface with comparable speeds to a RAID0 with SSDs have become available and might be a good option.  To benchmark your PC's hard drive write speed you can use [[http://crystalmark.info/?lang=en | Crystal Disk Mark]].  I'm pretty sure the relevant score to diSPIM acquisition is the "Seq" "Write" score (Sequential (Block Size=1MiB) Read/Write with single Thread), at least for Micro-manager software with typical acquisition settings. 100 MB/sec is typical for a magnetic hard drive.  300 MB/sec is typical for a single SSD.  If the data rate is too high for a single SSD, use SSDs in RAID0 configuration (e.g. 4 SSDs in RAID0 can achieve >1 GB/s).  Lately M.2 drives with PCIe interface with comparable speeds to a RAID0 with SSDs have become available and might be a good option.  To benchmark your PC's hard drive write speed you can use [[http://crystalmark.info/?lang=en | Crystal Disk Mark]].  I'm pretty sure the relevant score to diSPIM acquisition is the "Seq" "Write" score (Sequential (Block Size=1MiB) Read/Write with single Thread), at least for Micro-manager software with typical acquisition settings.
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 +Light sheet can generate lots of data very quickly, and it is important to have a plan to deal with the deluge.  This often involves support from the institution's IT department.  A helpful discussion of the challenges and options is the article [[https://arxiv.org/abs/2108.07631v1|Biologists need modern data infrastructure on campus]].
  
  
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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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