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Minimum Requirements and current NRAMM setup » History » Version 49

Sargis Dallakyan, 07/05/2017 07:28 PM

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h1. Minimum Requirements and current NRAMM setup
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h1. Hardware
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h2. Electron Microscope/Camera with their Controling Computers (Windows)
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Microscope need to have the capacity for external control and network connected (See [[Network Configuration]] section for details on that.  Here are known examples of Leginon implementation:
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* FEI: [[FEI TecnaiTitan installation specifics|Tecnai, Polara, Titan Krios]] (Film recording available with  [[Windows_Camera_Package_Requirement#Film_(Only_for_FEI_Tecnai/Titan)|adaexp.exe]]
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* JEOL: [[JEOL installation specifics|1230, 1400, JEM3100FSC,  3200]]
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h2. Digital Camera
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Gatan ([[Gatan_on_Windows-32|CCD]] and [[Gatan_K2_installation_and_setup|K2 Summit]]),  [[Tietz_camera_installation_and_setup|Tietz]], FEI ([[Eagle_camera_installation_and_setup|Eagle]], [[Ceta camera support|Ceta]] and [[Falcon camera support|Falcon]]), Direct Electron [[Adding_DE-12_to_Leginon|DE-12, DE-20]]
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*Note: Falcon integration does not include frame processing pipeline*
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h2. A Second Computer Running Linux (CentOS at NRAMM)
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At NRAMM, we separate the three activities into different computers that serve about 15 people with three microscopes that could be running at the same time.  All scopes share the same database, web server, and file server.  Each microscope has its own processing computer.
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*We don't recommend using a computer with Windows PC as the second computer as an option*.
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One group is able to use an unusually powerful WIndows PC (The one come with Gatan K2 Summit) as the processing server.  However, it is suspected as the reason for the acquisition of frame saving super-resolution mode to fail.
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h3. CPU
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Minimal 2 GHz;
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* Python instance of Leginon runs only on one core on the processing server.  If you have multiple core, the rest will only be useful if you want to do other things on it such as using it as the database server, webserver, and basic Appion processing.
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NRAMM:
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* Processing server: Single quad core (Intel Xeon E5-1607 v2  @ 3.00GHz).  One computer per microscope.
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* Database server: 6 Xeon E5-2620 v2 @ 2.10 GHz
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* Web server: 8 Intel Xeon X5667 3.00GHz
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h3. RAM
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The whole system with its image processing, database query and web serving, needs significant memory.  Realistically, you will need minimal of 4GB memory for all processing+database+web server activities for one microscope operation with 4k camera that serves two persons (one operates the scope, one just look at the images from the web viewers) at the same time.  We know of at least one successful daily usage at this configuration.  For 2k camera, an all-in-one computer with 3GB memory has also been used successfully.  If you are buying a new computer, get at least 6GB memory would be a good idea. 
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At NRAMM, to serve about 15 people viewing images and with three microscopes that could be running at the same time:
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* Processing server: 4GB physical memory and 2 GB swap for years, and now at 8GB and 8GB, respectively.  One computer per microscope
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* Database server: 64GB memory and 18GB swap.
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* Web server: 12GB memory and 12GB swap.
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h3. Display
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Pretty much everything today would work for data acquisition. GPU server for frame alignment of DD camera is separated.  See below.
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h3. File server
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10 GB for the softwares and maybe a few hours worth of data collection. Much larger
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for routine use. NRAMM 45Tb on raid and growing although some are archived.
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h3. Network connection speed
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100 Mbps might be possible; NRAMM 1 Gbps
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h2. Additional need for frame-saving direct detection camera
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h3. File server
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Frame-saving camera such as DE-12 and K2 Summit are capable of saving movies of an exposure in addition to returning an integrated image back to Leginon.  As a result, if the function is used, the disk space required is multiple of that of the image.  Leginon saves the frames as non-gain corrected 16-bit integer, rather than dark/gain-corrected 32-float mrc.  Therefore, the additional storage requirement is approximately *number_of_frames/2* times larger.  Typical number_of_frames used in DE is 10-50 frames and in K2 Summit 20-30 frames.  These frames should be off-loaded from the camera computer or saved to network drive as soon as possible so not to over-load the camera computer.
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In addition, to use the information in the frame movies, these raw frames must be [[appion:GainDark_correction_of_the_raw_frame_with_or_without_drift_correction|gain/dark corrected]] and saved as 32-bit float mrc stack.  For K2 Summit Counted/Super-resolution mode, the alignment of the frame is also esssential.  This means that at some point, the data related to one image will be
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<pre>
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number_of_frames * (0.5 + 1 + 1)
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</pre>
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times more than non-frame saving ones.  Factoring in that hundreds of such image may be acquired within a 24 hr session, it is therefore important to take this into account in allocating the data storage system for long term.
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NRAMM's current setup:
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# 10 Gbs network between frame saving camera and the file server.
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# Raw frames are transferred off camera with rsync using [[DDD raw frame file transfer|rawtransfer.py]] which also removed the finished frame stack on the camera to make room for more to come.
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# These raw frames are needed if default frame processing does not give optimal results.  We keep these on network drive for 30 days and make it user's responsibility to archive this on external drives. bzip or bzip2 are commonly used by the users to compress the files.  Since the raw frames are integers, it can be easily compressed to 4-10 times smaller.
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# In the [[appion:GainDark_correction_of_the_raw_frame_with_or_without_drift_correction|frame processing]] Appion script, if frame alignment is performed (usually finished within a day or two after data acquisition), the un-aligned frame stack is removed since majority of the problem require re-correction.
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# The frame-aligned stack is integrated and saved in the Leginon database as "-a" images. 95% of the users do not require frame-aligned stack after this point.
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h3. DD Frame-alignment server
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A good gpu is needed for frame alignment using the program described in Li et. al. (2013) Nat. Method vol. 10 pp584-590 and variants of it.
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If real-time speed is desired, parallel processing on multiple hosts may be needed.
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For MotionCor2
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minimum: A CUDA 8.0 capable standard linux computer that you don't need to access its monitor (and hence using gpu for display) during the alignment computation.  
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NRAMM's current best setup (This is a combination of dedicated resource plus shared resource when super-resolution movies are acquired.
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* Dedicated gpu: Nvidia
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* Shared resource:
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* up to 4 node gpu cluster each with 128 GB RAM and Intel(R) Xeon(R) CPU E5-2650 0 @ 2.00GHz & NvidiaTesla M2090 GPU.  (These have 16 cpu but only one is used per job)
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h5. Buffer Server
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Our current buffer server specs: 2U Dual 2.1GHz Intel E5-2620 v4 with: 128GB memory (8x 16GB), 9x 8TB 7.2K SATA drives, 1x 120GB SSD drive, 2x NVIDIA PNY GeForce GTX 1080, 1x Dual 10GE SFP+ cards (2 ports). Our buffer server is connected to Camera computer with a direct fiber link and we have SFP+ optical module for 10GBASE-LR installed on buffer server. We also have the following card installed on buffer server to connect it to DDN/GPFS file system using Infiniband switches: ConnectX-3 VPI adapter card, dual-port QSFP, FDR IB (56Gb/s) / 40GigE, PCIe 3.0 x8 8GT/s. 
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h1. Software
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h2. Leginon system components developed at Leginon home
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Leginon Home:  "http://www.leginon.org/":http://www.leginon.org/
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h2. Supporting packages and programs available through internet or your Linux distribution
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There are minimum of ten packages or single programs, some of them are included in your
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Linux distribution.
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h2. Leginon supporting programs available upon request
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*adaexp.exe* that is required if film exposure is to be made through Leginon on FEI Tecnai
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machines is available by request. Please contact Max Otten: (mto at nl.feico.com)
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and let him know what version of the Tecnai user interface you are using.
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______
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[[Graphical User Interface|< Graphical User Interface]] | [[Getting Started|Getting Started >]]
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