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Hi, quick follow up: Has anyone built a workstation for microscopy analysis
using AMD hardware? Their price/thread is way better than Intel's (both
customer- and server-grade CPUs) and the only downside seems to be that
they run a little hotter. Is there limited support in the various software
packages? Or is it just a matter of legacy infrastructure? Thanks, Francesco


On Thu, Jan 16, 2020 at 9:50 AM Olaf Selchow <[log in to unmask]> wrote:

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>
> Hello everybody,
>
> Happy to share a bit of my experience on this:
>
> - Re the multiview deconvolution by Preibisch et al - with GPU
> enhancement: It works but my experience also is that it could be a bit
> easier to get the installation done. CUDA support is not depreciated
> though, to my knowledge.
>
> - Typical data set size in what I have done in Light Sheet Microscopy in
> recent years is 500 GB to 10 TB both, in live imaging (long timer series
> with multiple views) and imaging of optically cleared specimen.
>
> - Re the specs of a suitable machine, the following things I would
> consider (depends on how many people will use it, how many microscopes
> connected, installation location of the processing computer)
>
> 1. With Imaris and / or arivis vision4D to be run on the same machine as
> Fiji and maybe the manufacturers software (Zeiss ZEN, Leica LAS X, 3i
> Slidebook, etc) you want to run Windows, I guess.
>
> 2. If you want to be able to use it with multiple users in parallel you
> want/need Windows Server.
>
> 3. I would make sure that the microscopes are connected with direct 10
> Gbit. Over an institutional network, even if 10 Gbit, you might be limited
> in bandwidth and so you can migrate the data only after acquisition from
> the acquisition PC to the Storage. This costs time, blocks the acquisition
> PC and essentially duplicates the required storage capacity you need. I
> always try to bring the microscopes with the processing & analysis
> computers in a 10 Gbit subnet that I can manage with a dedicated
> router/firewall/switch. That prevents interferences with  all the other
> traffic in your institute.
>
> 4. I would not use a NAS. Processing data by loading data into memory /
> software via network (and saving the results / temp data via the same line)
> can take ages. 10 Gbit is far too slow in my view. I’d use a prcessing
> machine with a strong RAID controler and a large RAID array directly
> attached to the processing unit. And I would save the data from the
> microscopes directly to this volume.
>
> 5. if you need super large RAM depends on how many people are supposed to
> work in parallel on this machine, if you want to use VMs, and what software
> you use (arivis needs much less RAM than others. Fiji benefits a lot from
> super large RAM, etc.). But if you choose the right motherboard and OS, you
> can always and easily upgrade RAM later.
>
> 6. CPU: generally, large multicore CPUs speed up things. But some
> software, even today, doesn’t make much use of parallelization. If you buy
> a very expensive dual-44-core CPU for thousands of $/€ you might end up
> with the software not using it. Actual check out the workflows. Some
> vendors might say „yes, our software uses all cores available“ , but in the
> end the processing function you are using most might still be running on a
> single or only 2 cores.
>
> 7. Monitor: I have worked with a number of 32 inch      UHD 4K (3840 x
> 2160, 16:9) and 32 inch WQHD 3.6K (2560 x 1440, 16:9) monitors and never
> had a real problem. But thi smight depend on the GPU you use.
>
> 8. GPUs:
> - if you want to use 3D renderings over remote access (e.g., RDP)
> sessions, I strongly recommend professional GPUs. I know, they are
> expensive. But the drivers on the Geforce or other gaming-grade GPUs can
> give you a hard time when working remotely. I have good experience with
> NVIDIA Quadro RTX boards (they are, in some GPU CUDA processing tasks, 2x
> faster than the previous P-seroes that is otherwise also perfectly fine.)
> For 3D viewing / rendering / analyzing data, make sure the VRAM on the GPU
> is 11 GB / 16 GB or larger.
> - for some software and for VMs, it makes sense to think of the option of
> multiple GPUs. Maybe you just want to make sure you can fit a 2nd or 3rd
> later on. In SVI Huygens you can, for example, assign instances of DCV to
> certain GPUs - so multiple GPUs can speed up your work. You need to buy the
> respective licenses from SVI though.
>
> 9. Storage volume: make sure you have multiple (2 or more) fast volumes
> (RAIDs of HDD or SSDs) to have space for the software where it can save
> temp data - on an independent volume. Multiple simultaneous read/write
> processes can slow down even fast RAID configs. Also keep in mind that SSDs
> are more convenient and faster, but still more expensive and still have a
> higher failure rate. Mak sure you consider hard drives 7 ssds as
> consumables. In a RAID of 15 HDDs, it is perfectly normal that you have 1
> HDD/year in average that fails and needs replacement. SSDs maybe even more
> often.
>
> The Lenovo Think Station P920 is certainly a great hardware. You’ll still
> have to invest a bit of time and money to get it ready to work for your
> applications. Networking i, etc.
>
> I would also point out that there is commercial options that provide you a
> turnkey solution with support that can scale / grow with your needs.
> I have worked with ACQUIFER HIVEs a lot. Check with them or a similar
> provider if your budget allows a high end solution for 60k (+) $/€ and if
> you are looking for a solution provider that saves you from configuring
> network adapters yourself …
> Note: I used to have consultancy projects with ACQUIFER and might have
> more in the future. So I am a bit biased, but mostly because I think they
> have great hardware and services and I worked wit them to position their
> products and improve them. I do not (!) have a direct financial interest in
> them selling a platform unit to you!
>
> I hope this helps.
> best regards,
> Olaf
>
> ———
> Dr. Olaf Selchow
> --
> Microscopy & BioImaging Consulting
> Image Processing & Large Data Handling
> --
> [log in to unmask]
>


-- 
Francesco S. Pasqualini
Visiting Professor University of Pavia
Associate Harvard University

tel: +39 351-521-7788 (IT)
tel: +1 617-401-5243 (USA)