Merge branch 'master' into dev

docs/access/allocations.md

@@ -16,7 +16,7 @@ Having an account only validates that you are eligible to use RC resources.  An
 
 To apply for an allocation, you must do the following:
 
-1.  Fill out the [template](https://www.colorado.edu/rc/userservices/allocations) at the linked page in your preferred format.  If you need assistance, please always feel free to email rc-help@colorado.edu.  
+1.  Contact rc-help@colorado.edu for an allocation proposal template. Complete this template. If you need assistance, please [let us know](rc-help@colorado.edu) and we would be happy to assist.  
 2.  Login to [RCAMP](https://rcamp.rc.colorado.edu/)
 3.  Fill out some basic information about your request and create a project.  This project is a space in which you can link multiple allocations, manage PIs, etc.
 4.  After creating a project, upload the template to complete your allocation request.

docs/access/blanca.md

@@ -34,43 +34,23 @@ Since not all Blanca nodes are identical, you can include node features in your
 
 To determine which nodes exist on the system, type `scontrol show nodes` to get a list.
 
-### Node Features Tables
-
-#### Blanca Core
-
-Node Name     | High-Priority QoS | General Hardware Attributes | Features  
---------------|---------------|-----------------------------|---------  
-bnode010[1-5] | blanca-ics    | 32 cores, 2.6 GHz,<br> 256 GB RAM,<br> 1 TB local disk | sandybridge,<br> avx,<br> rhel7
-bnode010[6-7] | blanca-igg | 24 cores, 2.5 GHz,<br> 128 GB RAM,<br> 1 TB local disk | haswell,<br> avx2,<br> rhel7
-bnode01[08-11] | blanca-ibgc1 | 48 cores, 2.5 GHz,<br> 256 GB RAM,<br> 1 TB local disk | haswell,<br> avx2,<br> rhel7
-bnode01[12-14] | blanca-mrg | 24 cores, 2.5 GHz,<br> 128 GB RAM,<br> 1 TB local disk | haswell,<br> avx2,<br> rhel7
-bnode01[15-16] | blanca-el | 28 cores w/ 2x hyperthreading/core, 2.4 GHz,<br> 128 GB RAM,<br> 1 TB local disk | broadwell,<br> avx2,<br> rhel7
-bnode02[01-36] | blanca-ccn | 16 cores, 3.3 GHz,<br> 64 GB RAM,<br> 1 TB local disk |ivybridge,<br> Quadro,<br> k2000,<br> avx,<br> fdr,<br> rhel7
-bnode0301 | blanca-ics | 32 cores, 2.4 GHz,<br> 256 GB RAM,<br> 1 TB local disk | broadwell,<br> avx2,<br> rhel7
-bnode030[2-9] | blanca-sha | 28 cores, 2.4 GHz,<br> 128 GB RAM,<br> 1 TB local disk | broadwell,<br> avx2,<br> rhel7
-bnode0310 | blanca-ics | 32 cores, 2.4 GHz,<br> 256 GB RAM,<br> 1 TB local disk | broadwell,<br> avx2,<br> rhel7
-bnode0311 | blanca-ceae | 28 cores, 2.4 GHz,<br> 128 GB RAM,<br> 1 TB local disk | broadwell,<br> avx2,<br> rhel7
-bgpu-dhl1 | blanca-dhl | 56 cores, 2.4 GHz,<br> 128 GB RAM,<br> 1 TB local disk | broadwell,<br> avx2,<br> rhel7,<br> Tesla,<br> P100
-bnode03[12-15] | blanca-pccs | 28 cores, 2.4 GHz,<br> 128 GB RAM,<br> 1 TB local disk | broadwell,<br> avx2,<br> rhel7
-bnode0316,<br> bnode0401 | blanca-csdms | 28 cores w/ 2x hyperthreading/core, 2.4 GHz,<br> 128 GB RAM,<br> 1 TB local disk | broadwell,<br> avx2,<br> rhel7, <br> 2x hyperthreading/core
-bnode04[02-03] | blanca-sol | 28 cores w/ 2x hyperthreading/core, 2.4 GHz,<br> 128 GB RAM,<br> 1 TB local disk | broadwell,<br> avx2,<br> rhel7, <br> 2x hyperthreading/core
-bnode05[01-02] | blanca-appm | 32 cores, 2.10 GHz,<br> 191.904 GB RAM,<br> 1 TB local disk | skylake, <br> avx2,<br> rhel7,<br> 2x hyperthreading/core
-himem04 | blanca-ibg | 80 cores, 2.1 GHz,<br> 1024 GB RAM,<br> 10 TB local disk | westmere-ex,<br> localraid,<br> rhel7
-bnode0404 | blanca-rittger | 32 cores, 2.10 GHz,<br> 191.904 GB RAM,<br> 1 TB local disk | skylake,<br> avx2,<br> rhel7,<br> 2x hyperthreading/core
-bnode04[05-08] | blanca-ics | 28 cores, 2.4 GHz,<br> 250.000 GB RAM,<br>1 TB local disk | broadwell,<br> avx2,<br> rhel7
-bnode04[12-14] | blanca-ibg | 32 cores, 2.10 GHz,<br> 1000.00 GB RAM,<br> 10 TB local disk | skylake,<br> avx2,<br> rhel7,<br> 2x hyperthreading/core
-bnode05[03-04] | blanca-csdms | 32 cores, 2.10 GHz,<br> 191.904 GB RAM,<br> 1 TB local disk | skylake,<br> avx2,<br> rhel7,<br> 2x hyperthreading/core
-bnode05[05-06] | blanca-geol | 32 cores, 2.10 GHz,<br> 191.904 GB RAM,<br> 1 TB local disk | skylake,<br> avx2,<br> rhel7,<br> 2x hyperthreading/core
-bnode05[07] | blanca-rittger | 32 cores, 2.10 GHz,<br> 191.840 GB RAM,<br> 1 TB local disk | skylake,<br> avx2,<br> rhel7,<br> 2x hyperthreading/core
-bnode05[08-09] | blanca-appm | 40 cores, 2.10 Ghz,<br> 191.668 GB RAM,<br> 1 TB local disk | cascade,<br> avx2,<br> rhel7,<br> 2x hyperthreading/core
-bgpu-mktg1 | blanca-mktg | 32 cores, 2.10 GHz,<br> 772.476 GB RAM,<br> 1.8 TB local disk,<br> 1 NVIDIA P100 GPU | skylake,<br> avx2,<br> rhel7,<br> Tesla,<br> P100
-bhpc-c7-u7-[1-18]  | blanca-nso    | 36 cores, 2.70 Ghz,<br> 185 GB RAM,<br> 480 GB local disk | skylake,<br> avx2,<br> rhel7,<br> edr  
-bhpc-c7-u7-[19-23] | blanca-topopt | 64 cores, 2.10 Ghz,<br> 185 GB RAM,<br> 480 GB local disk | skylake,<br> avx2,<br> rhel7,<br> edr  
-bhpc-c7-u7-24      | blanca-curc   | 32 cores, 2.10 Ghz,<br> 185 GB RAM,<br> 480 GB local disk | skylake,<br> avx2,<br> rhel7,<br> edr  
-bhpc-c7-u19-[1-18] | blanca-nso    | 36 cores, 2.70 Ghz,<br> 185 GB RAM,<br> 480 GB local disk | skylake,<br> avx2,<br> rhel7,<br> edr  
-hhpc-c7-u19-[19-20] | blanca-curc  | 32 cores, 2.10 Ghz,<br> 185 GB RAM,<br> 480 GB local disk | skylake,<br> avx2,<br> rhel7,<br> edr  
-
-### Description of features
+### Node Features
+
+Blanca is a pervasively heterogeneous cluster. A variety of feature tags are applied to nodes deployed in Blanca to allow jobs to target specific CPU, GPU, network, and storage requirements.
+
+Use the `sinfo` command to determine the features that are available on any node in the cluster.
+
+```bash
+sinfo --format="%N | %f"
+```
+
+The `sinfo` query may be further specified to look at the features available within a specific partition.
+
+```bash
+sinfo --format="%N | %f" --partition="blanca-curc"
+```
+
+#### Description of features
 
 - **westmere-ex**: Intel processor generation  
 - **sandybridge**: Intel processor generation  

docs/access/duo-2-factor-authentication.md

@@ -15,12 +15,15 @@ Research Computing utilizes a 2-factor authentication utility called **Duo** tha
 3. Login to RC Resources via ssh [as described below.](#logging-in-with-duo)
 
 #### Common Issues
+A few common issues users will come across when using Duo include:
 
-* Duo Invite email may be sent to your Spam folder.
-* Do not request a phone call if you want to use the Push app for authentication.
-* Duo accounts are purged if unused for 6-9 months.
+* A Duo Invite email may be sent to your Spam folder.
+* Requesting a phone call if you want to use the Push app for authentication.
+* A Duo account purged after remaining unused for 6-9 months.
+* Having a new device and want to move Duo onto it.
+    * (Check out our FAQ for detailed instructions on accomplishing this.)[https://curc.readthedocs.io/en/latest/faq.html#i-have-a-new-phone-how-do-i-move-my-duo-onto-it]
 
-Please contact us at rc-help@colorado.edu if you encounter any issues with Duo setup.
+Please contact us at rc-help@colorado.edu if you encounter these or any other issues regarding Duo.
 
 ### Logging in with Duo
 

docs/additional-resources/other.md

@@ -2,87 +2,54 @@
 
 ### Expertise
 
-Research Computing at CU Boulder consists of a small group of
-computational scientists, high-performance computing specialists, and
-system administrators with the mission to provide leadership in
-developing, deploying, and operating an integrated
-cyberinfrastructure.  This cyberinfrastructure consists of
-high-performance computing, storage, and high speed networking that
-supports and encourages research, collaboration and discovery. The
-groups also contribute to the educational mission of the university by
-providing workshops and training on cyberinfrastructure related topics
-as well as 1:1 consulting.
+Research Computing at CU Boulder consists of a small group of computational scientists, high-performance computing specialists, and system administrators with the mission to provide leadership in developing, deploying, and operating an integrated cyberinfrastructure.  This cyberinfrastructure consists of high-performance computing, storage, and high speed networking that supports research, collaboration and discovery. Research Computing contributes to the educational mission of the university by providing training workshops and consultation services for cyberinfrastructure related topics.
 
 ### Compute
 
-RC operates the joint CSU-CU-Boulder Summit supercomputer, funded by
-NSF under Grant No. AC- 1532236. The system has peak performance of
-over 400 TFLOPS. The 380 general compute nodes have two Intel Haswell
-CPUs with 12 cores each, 128 GB of RAM and a local SSD. Additionally,
-the system has 10 GPU nodes containing two NVIDIA K80 GPUs, 5
-high-memory nodes with about 1 TB of main memory and in a second
-deployment in December 2016 20 Xeon Phi (“Knight’s Landing”) nodes
-with 72 real cores supporting 288 threads for development and
-benchmarking.  All nodes are connected through a high-performance
-network based on Intel Omni-Path with a bandwidth of 100 Gb/s and a
-latency of 0.4 microseconds. 1 PB of high-performance storage is
-provided using the IBM GPFS file system. This system is available to
-CU-Boulder researchers and collaborators, as well as 10% of cycles are
-provided to members of the Rocky Mountain Advanced Computing
-Consortium.
-
-The RC Condo Computing service offers researchers the opportunity to
-purchase and own compute nodes that will be operated as part of a
-cluster, named “Blanca.” The aggregate cluster is made available to
-all condo partners while maintaining priority for the owner of each
-node.
+* Research Computing operates the joint RMACC Summit supercomputer, funded by NSF under Grant No. AC- 1532236. The system has peak performance of over 400 TFLOPS. The 472 general compute nodes each have 24 cores aboard Intel Haswell CPUs, 128 GB of RAM and a local SSD. Additionally, the system has 11 GPU nodes with two NVIDIA K80 GPUs each, 5 high-memory nodes with ~2TiB of main memory, and 20 Xeon Phi (“Knight’s Landing”) nodes each with 68 real cores supporting 272 threads.  All nodes are connected through a high-performance network based on Intel Omni-Path with a bandwidth of 100 Gb/s and a latency of 0.4 microseconds. A 1.2 PB high-performance IBM GPFS file system is provided. This system is available to CU-Boulder and Colorado State University researchers and collaborators, and 10% of cycles are provided to members of the Rocky Mountain Advanced Computing Consortium.
 
-### Networking
+* The Research Computing Condo Computing service offers researchers the opportunity to purchase and own compute nodes that are operated as part of a cluster, named “Blanca.” The aggregate cluster is made available to all condo partners while maintaining priority for the owner of each node. 
+
+* Research Computing provides a 3d-accelerated virtual desktop environment for real-time visualization and rendering using EnginFrame and DCV. This environment is powered by two visualization nodes, each equipped with 2x AMD EPYC 7402 24-core processors, 256GiB memory, and 2x Nvidia Quadro RTX 8000 GPU accelerators. Each accelerator is itself equipped with 48 GiB of high-speed GDDR6 memory.
 
-The current CU Boulder network is a 40 Gbps fiber core with Cat 5 or
-higher wiring throughout campus. RC has created an 80 Gbps Science-DMZ
-to connect the Summit supercomputer to storage and to bring individual
-dedicated 10 Gbps circuits to various locations as needed. CU Boulder
-participates in I2 (the Internet 2 higher education, government, and
-vendor research computing consortium) and is an active member of the
-Front-Range gigapop and other networks. RC has started to provide
-campus researchers with a leading-edge network that meets their needs
-and facilitates collaboration, high performance data exchange, access
-to co-location facilities, remote mounts to storage, and real-time
-communications.
+### Networking
 
+The current CU Boulder network is a 40 Gbps fiber core with Cat 5 or higher wiring throughout campus. Research Computing has created an 80 Gbps Science-DMZ to connect the RMACC Summit supercomputer to storage and to bring individual dedicated 10 Gbps circuits to various locations as needed. CU Boulder participates in I2 (the Internet 2 higher education, government, and vendor research computing consortium) and is an active member of the Front-Range gigapop and other networks. Research Computing has begun to provide campus researchers with a leading-edge network that meets their needs and facilitates collaboration, high performance data exchange, access to co-location facilities, remote mounts to storage, and real-time communications.
 
 ### File Transfer
 
-For moving large amounts of data Research Computing has several nodes
-dedicated to GridFTP file transfer. RC’s GridFTP servers support both
-the Globus Connect web environment and basic GridFTP via the command
-line.
-
-OIT also offers a file transfer service with a web interface, which
-provides a good way to transfer files to collaborators. Files are
-uploaded to a server and a link to download the file can be emailed to
-an on or off-campus user.
+For moving large volumes of data Research Computing has several nodes dedicated to GridFTP file transfer.
 
+The CU Office of Information Technology also offers a file transfer service with a web interface, which provides an ideal way to transfer large files to collaborators. Files are uploaded to a server and a link to download the file is emailed to an on- or off-campus user.
 
 ### Storage  
 
-Each researcher using the computational resources at CU Boulder has a
-home directory with 2GB and a project space consisting of 250 GB of
-storage. Additional storage is provided as part of a storage
-condominium at a cost of $65 per TB for single copy storage. Tape and
-HSM are additional storage options that are available for archive
-data.
-
-### PetaLibrary
-
-The two main categories of service offered to customers of the
-PetaLibrary are Active storage for data that needs to be accessed
-frequently and Archive storage for data that is accessed
-infrequently. Active data is always stored on disk and is accessible
-to researchers on compute resources managed by RC. Archive storage
-consists of a two-level hierarchical storage management (HSM)
-solution, with disk storage for data that is more likely to be
-accessed and tape for data that is less likely to be accessed
-frequently. The cost for the research is $65/TB/year for disk and
-$35/TB/year for archival storage.
+Each Research Computing user has a 2 GB home directory and a 250 GB projects directory, each of which are backed up regularly. Each RMACC Summit user has a 10 TB scratch directory.
+
+### PetaLibrary Storage Services
+
+The PetaLibrary is a CU Research Computing service supporting the storage, archival, and sharing of research data. It is available at a subsidized cost to any researcher affiliated with the University of Colorado Boulder. The two main categories of service offered to customers of the PetaLibrary are Active storage for data requiring frequent access, and Archive storage for data that is accessed infrequently. Active data is stored on spinning disk and is accessible to researchers on compute resources managed by Research Computing. Archive storage consists of a two-level hierarchical storage management (HSM) solution, with disk storage for data that is more likely to be accessed and tape storage for data that is less likely to be accessed frequently. The cost for CU researchers is $45/TB/year for Active and $20/TB/year for Archive.
+
+Through a collaboration with the CU Libraries, the PetaLibrary can also host the publication and long-term archival of large datasets. The datasets are assigned unique digital object identifiers (DOIs) that are searchable and accessible via the “CU Scholar” institutional repository.
+
+### JupyterHub
+
+JupyterHub is a multi-user server for Jupyter notebooks. It provides a web service enabling users to create and share documents that contain live code, equations, visualizations and explanatory text. The CU Research Computing JuypterHub deploys into the RMACC Summit supercomputer and Blanca condo cluster and includes support for parallel computation.
+
+### EnginFrame
+
+NICE EnginFrame provides a 3D-accelerated remote desktop environment on Nvidia GPU-equipped compute nodes. Coupled with the proprietary Desktop Cloud Visualization (DCV) VNC server, the Research Computing EnginFrame supports the use of common visualization applications in a typical desktop environment using only a modern web browser.
+
+### Center for Research Data and Digital Scholarship (CRDDS)
+
+The Center for Research Data & Digital Scholarship (CRDDS) is a collaboration between Research Computing and University Libraries, offering a full range of data services for both university and community members. The aim of CRDDS is to provide support to community members on areas related to data intensive research.  CRDDS fulfills this mission by providing education and support on such issues as data discovery, reuse, access, publication, storage, visualization, curation, cleaning, and preservation, as well as digital scholarship. CRDDS is located in Norlin Library on Main Campus at CU Boulder. 
+
+CRDDS offers many opportunities to students working with data. The expert staff work hand-in-hand with researchers via weekly office hours, one-on-one consultations, and group trainings in programming, data visualization and more. CRDDS serves as a resource for data management, manipulation and publication for trainees working through undergraduate and graduate coursework.
+
+Examples of workshops/trainings CRDDS has offered include:
+* High performance computing
+* Programming in R
+* Programming in Python
+* Containerization
+* Data mining 
+