Commit 11925d92 authored by David Hrbáč's avatar David Hrbáč
Browse files

Merge branch 'module_matrix' into 'master'

Module matrix

See merge request !78
parents 816c986f beb9a4d3
Pipeline #1912 passed with stages
in 5 minutes and 24 seconds
......@@ -17,7 +17,7 @@ two spaces:
allow_failure: true
script:
- echo "== Files having more than one space betwee two characters =="
- find docs.it4i/ -name '*.md' -exec grep "[[:alpha:]] [[:alpha:]]" -l {} +
- find docs.it4i/ -name '*.md' -exec grep "[[:alpha:]] [[:alpha:]]" -l {} + || true
capitalize:
stage: test
......
......@@ -48,7 +48,7 @@ To check whether your proxy certificate is still valid (by default it's valid 12
To access Anselm cluster, two login nodes running GSI SSH service are available. The service is available from public Internet as well as from the internal PRACE network (accessible only from other PRACE partners).
#### Access from PRACE network:
#### Access From PRACE Network:
It is recommended to use the single DNS name anselm-prace.it4i.cz which is distributed between the two login nodes. If needed, user can login directly to one of the login nodes. The addresses are:
......@@ -68,7 +68,7 @@ When logging from other PRACE system, the prace_service script can be used:
$ gsissh `prace_service -i -s anselm`
```
#### Access from public Internet:
#### Access From Public Internet:
It is recommended to use the single DNS name anselm.it4i.cz which is distributed between the two login nodes. If needed, user can login directly to one of the login nodes. The addresses are:
......@@ -122,7 +122,7 @@ Apart from the standard mechanisms, for PRACE users to transfer data to/from Ans
There's one control server and three backend servers for striping and/or backup in case one of them would fail.
### Access from PRACE network
### Access From PRACE Network
| Login address | Port | Node role |
| ---------------------------- | ---- | --------------------------- |
......@@ -155,7 +155,7 @@ Or by using prace_service script:
$ globus-url-copy gsiftp://`prace_service -i -f anselm`/home/prace/_YOUR_ACCOUNT_ON_ANSELM_/_PATH_TO_YOUR_FILE_ file://_LOCAL_PATH_TO_YOUR_FILE_
```
### Access from public Internet
### Access From Public Internet
| Login address | Port | Node role |
| ---------------------- | ---- | --------------------------- |
......
......@@ -632,7 +632,7 @@ The output should be similar to:
There are two ways how to execute an MPI code on a single coprocessor: 1.) lunch the program using "**mpirun**" from the
coprocessor; or 2.) lunch the task using "**mpiexec.hydra**" from a host.
#### Execution on coprocessor
#### Execution on Coprocessor
Similarly to execution of OpenMP programs in native mode, since the environmental module are not supported on MIC, user has to setup paths to Intel MPI libraries and binaries manually. One time setup can be done by creating a "**.profile**" file in user's home directory. This file sets up the environment on the MIC automatically once user access to the accelerator through the SSH.
......@@ -681,7 +681,7 @@ The output should be similar to:
Hello world from process 0 of 4 on host cn207-mic0
```
#### Execution on host
#### Execution on Host
If the MPI program is launched from host instead of the coprocessor, the environmental variables are not set using the ".profile" file. Therefore user has to specify library paths from the command line when calling "mpiexec".
......@@ -726,7 +726,7 @@ A simple test to see if the file is present is to execute:
/bin/pmi_proxy
```
#### Execution on host - MPI processes distributed over multiple accelerators on multiple nodes**
#### Execution on Host - MPI Processes Distributed Over Multiple Accelerators on Multiple Nodes**
To get access to multiple nodes with MIC accelerator, user has to use PBS to allocate the resources. To start interactive session, that allocates 2 compute nodes = 2 MIC accelerators run qsub command with following parameters:
......@@ -886,7 +886,7 @@ A possible output of the MPI "hello-world" example executed on two hosts and two
!!! note
At this point the MPI communication between MIC accelerators on different nodes uses 1Gb Ethernet only.
### Using the PBS automatically generated node-files
### Using the PBS Automatically Generated Node-Files
PBS also generates a set of node-files that can be used instead of manually creating a new one every time. Three node-files are genereated:
......
......@@ -69,7 +69,7 @@ Names of applications (APP):
To get the FEATUREs of a license take a look into the corresponding state file ([see above](isv_licenses/#Licence)), or use:
### Application and List of provided features
### Application and List of Provided Features
* **ansys** $ grep -v "#" /apps/user/licenses/ansys_features_state.txt | cut -f1 -d' '
* **comsol** $ grep -v "#" /apps/user/licenses/comsol_features_state.txt | cut -f1 -d' '
......@@ -92,7 +92,8 @@ Example of PBS Pro resource name, based on APP and FEATURE name:
| matlab-edu | MATLAB_Distrib_Comp_Engine | feature_matlab-edu_MATLAB_Distrib_Comp_Engine |
| matlab-edu | Image_Acquisition_Toolbox | feature_matlab-edu_Image_Acquisition_Toolbox\\ |
#### Be aware, that the resource names in PBS Pro are CASE SENSITIVE!
!!! Warnig
Resource names in PBS Pro are case sensitive.
### Example of qsub Statement
......
......@@ -279,7 +279,7 @@ Optimized network setup with sharing and port forwarding
### Advanced Networking
#### Internet access
#### Internet Access
Sometime your virtual machine needs access to internet (install software, updates, software activation, etc). We suggest solution using Virtual Distributed Ethernet (VDE) enabled QEMU with SLIRP running on login node tunneled to compute node. Be aware, this setup has very low performance, the worst performance of all described solutions.
......@@ -321,7 +321,7 @@ Optimized setup
$ qemu-system-x86_64 ... -device virtio-net-pci,netdev=net0 -netdev vde,id=net0,sock=/tmp/sw0
```
#### TAP interconnect
#### TAP Interconnect
Both user and vde network back-end have low performance. For fast interconnect (10 Gbit/s and more) of compute node (host) and virtual machine (guest) we suggest using Linux kernel TAP device.
......
......@@ -198,7 +198,7 @@ To run the code use interactive PBS session to get access to one of the GPU acce
The NVIDIA CUDA Basic Linear Algebra Subroutines (cuBLAS) library is a GPU-accelerated version of the complete standard BLAS library with 152 standard BLAS routines. Basic description of the library together with basic performance comparison with MKL can be found [here](https://developer.nvidia.com/cublas "Nvidia cuBLAS").
#### cuBLAS example: SAXPY
#### cuBLAS Example: SAXPY
SAXPY function multiplies the vector x by the scalar alpha and adds it to the vector y overwriting the latest vector with the result. The description of the cuBLAS function can be found in [NVIDIA CUDA documentation](http://docs.nvidia.com/cuda/cublas/index.html#cublas-lt-t-gt-axpy "Nvidia CUDA documentation "). Code can be pasted in the file and compiled without any modification.
......
......@@ -119,7 +119,7 @@ Output:VCF
Variant Call Format (VCF)
VCF (3) is a standardized format for storing the most prevalent types of sequence variation, including SNPs, indels and larger structural variants, together with rich annotations. The format was developed with the primary intention to represent human genetic variation, but its use is not restricted >to diploid genomes and can be used in different contexts as well. Its flexibility and user extensibility allows representation of a wide variety of genomic variation with respect to a single reference sequence.
VCF (3) is a standardized format for storing the most prevalent types of sequence variation, including SNPs, indels and larger structural variants, together with rich annotations. The format was developed with the primary intention to represent human genetic variation, but its use is not restricted to diploid genomes and can be used in different contexts as well. Its flexibility and user extensibility allows representation of a wide variety of genomic variation with respect to a single reference sequence.
A VCF file consists of a header section and a data section. The header contains an arbitrary number of metainformation lines, each starting with characters ‘##’, and a TAB delimited field definition line, starting with a single ‘#’ character. The meta-information header lines provide a standardized description of tags and annotations used in the data section. The use of meta-information allows the information stored within a VCF file to be tailored to the dataset in question. It can be also used to provide information about the means of file creation, date of creation, version of the reference sequence, software used and any other information relevant to the history of the file. The field definition line names eight mandatory columns, corresponding to data columns representing the chromosome (CHROM), a 1-based position of the start of the variant (POS), unique identifiers of the variant (ID), the reference allele (REF), a comma separated list of alternate non-reference alleles (ALT), a phred-scaled quality score (QUAL), site filtering information (FILTER) and a semicolon separated list of additional, user extensible annotation (INFO). In addition, if samples are present in the file, the mandatory header columns are followed by a FORMAT column and an arbitrary number of sample IDs that define the samples included in the VCF file. The FORMAT column is used to define the information contained within each subsequent genotype column, which consists of a colon separated list of fields. For example, the FORMAT field GT:GQ:DP in the fourth data entry of Figure 1a indicates that the subsequent entries contain information regarding the genotype, genotype quality and read depth for each sample. All data lines are TAB delimited and the number of fields in each data line must match the number of fields in the header line. It is strongly recommended that all annotation tags used are declared in the VCF header section.
......@@ -131,15 +131,15 @@ two bases by another base (SAMPLE2); the second line shows a SNP and an insertio
### Annotating
Component: HPG-Variant
Component: HPG-Variant
The functional consequences of every variant found are then annotated using the HPG-Variant software, which extracts from CellBase, the Knowledge database, all the information relevant on the predicted pathologic effect of the variants.
VARIANT (VARIant Analysis Tool) (4) reports information on the variants found that include consequence type and annotations taken from different databases and repositories (SNPs and variants from dbSNP and 1000 genomes, and disease-related variants from the Genome-Wide Association Study (GWAS) catalog, Online Mendelian Inheritance in Man (OMIM), Catalog of Somatic Mutations in Cancer (COSMIC) mutations, etc. VARIANT also produces a rich variety of annotations that include information on the regulatory (transcription factor or miRNAbinding sites, etc.) or structural roles, or on the selective pressures on the sites affected by the variation. This information allows extending the conventional reports beyond the coding regions and expands the knowledge on the contribution of non-coding or synonymous variants to the phenotype studied.
Input: VCF
Input: VCF
Output: The output of this step is the Variant Calling Format (VCF) file, which contains changes with respect to the reference genome with the corresponding QC and functional annotations.
Output: The output of this step is the Variant Calling Format (VCF) file, which contains changes with respect to the reference genome with the corresponding QC and functional annotations.
#### CellBase
......@@ -225,7 +225,7 @@ second one.
--prefix. Prefix for PBS Job names.
-s, --start & -e, --end. Initial and final stage. If we want to launch the pipeline in a specific stage we must use -s. If we want to end the pipeline in a specific stage we must use -e.
-s, --start & -e, --end. Initial and final stage. If we want to launch the pipeline in a specific stage we must use -s. If we want to end the pipeline in a specific stage we must use -e.
--log. Using log argument NGSpipeline will prompt all the logs to this file.
......@@ -338,13 +338,13 @@ The output folder contains all the subfolders with the intermediate data. This f
![TEAM upload panel. Once the file has been uploaded, a panel must be chosen from the Panel list. Then, pressing the Run button the diagnostic process starts.]\((../../../img/fig7.png)
Figure 7. _TEAM upload panel._ _Once the file has been uploaded, a panel must be chosen from the Panel_ list. Then, pressing the Run button the diagnostic process starts.
Figure 7. _TEAM upload panel._ _Once the file has been uploaded, a panel must be chosen from the Panel_ list. Then, pressing the Run button the diagnostic process starts.
Once the file has been uploaded, a panel must be chosen from the Panel list. Then, pressing the Run button the diagnostic process starts. TEAM searches first for known diagnostic mutation(s) taken from four databases: HGMD-public (20), [HUMSAVAR](http://www.uniprot.org/docs/humsavar), ClinVar (29) and COSMIC (23).
![The panel manager. The elements used to define a panel are (A) disease terms, (B) diagnostic mutations and (C) genes. Arrows represent actions that can be taken in the panel manager. Panels can be defined by using the known mutations and genes of a particular disease. This can be done by dragging them to the Primary Diagnostic box (action D). This action, in addition to defining the diseases in the Primary Diagnostic box, automatically adds the corresponding genes to the Genes box. The panels can be customized by adding new genes (action F) or removing undesired genes (action G). New disease mutations can be added independently or associated to an already existing disease term (action E). Disease terms can be removed by simply dragging themback (action H).](../../../img/fig7x.png)
Figure 7. The panel manager. The elements used to define a panel are ( A ) disease terms, ( B ) diagnostic mutations and ( C ) genes. Arrows represent actions that can be taken in the panel manager. Panels can be defined by using the known mutations and genes of a particular disease. This can be done by dragging them to the Primary Diagnostic box (action D ). This action, in addition to defining the diseases in the Primary Diagnostic box, automatically adds the corresponding genes to the Genes box. The panels can be customized by adding new genes (action F ) or removing undesired genes (action G). New disease mutations can be added independently or associated to an already existing disease term (action E ). Disease terms can be removed by simply dragging them back (action H ).
Figure 7. The panel manager. The elements used to define a panel are ( A ) disease terms, ( B ) diagnostic mutations and ( C ) genes. Arrows represent actions that can be taken in the panel manager. Panels can be defined by using the known mutations and genes of a particular disease. This can be done by dragging them to the Primary Diagnostic box (action D ). This action, in addition to defining the diseases in the Primary Diagnostic box, automatically adds the corresponding genes to the Genes box. The panels can be customized by adding new genes (action F ) or removing undesired genes (action G). New disease mutations can be added independently or associated to an already existing disease term (action E ). Disease terms can be removed by simply dragging them back (action H ).
For variant discovering/filtering we should upload the VCF file into BierApp by using the following form:
......@@ -366,7 +366,7 @@ Each prioritization (‘job’) has three associated screens that facilitate the
1. Medina I, De Maria A, Bleda M, Salavert F, Alonso R, Gonzalez CY, Dopazo J: VARIANT: Command Line, Web service and Web interface for fast and accurate functional characterization of variants found by Next-Generation Sequencing. Nucleic Acids Res 2012, 40:W54-58.
1. Bleda M, Tarraga J, de Maria A, Salavert F, Garcia-Alonso L, Celma M, Martin A, Dopazo J, Medina I: CellBase, a comprehensive collection of RESTful web services for retrieving relevant biological information from heterogeneous sources. Nucleic Acids Res 2012, 40:W609-614.
1. Flicek,P., Amode,M.R., Barrell,D., Beal,K., Brent,S., Carvalho-Silva,D., Clapham,P., Coates,G., Fairley,S., Fitzgerald,S. et al. (2012) Ensembl 2012. Nucleic Acids Res., 40, D84–D90.
1. UniProt Consortium. (2012) Reorganizing the protein space at the Universal Protein Resource (UniProt). Nucleic Acids Res., 40, D71–D75.
1. UniProt Consortium. (2012) Reorganizing the protein space at the Universal Protein Resource (UniProt). Nucleic Acids Res., 40, D71–D75.
1. Kozomara,A. and Griffiths-Jones,S. (2011) miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res., 39, D152–D157.
1. Xiao,F., Zuo,Z., Cai,G., Kang,S., Gao,X. and Li,T. (2009) miRecords: an integrated resource for microRNA-target interactions. Nucleic Acids Res., 37, D105–D110.
1. Hsu,S.D., Lin,F.M., Wu,W.Y., Liang,C., Huang,W.C., Chan,W.L., Tsai,W.T., Chen,G.Z., Lee,C.J., Chiu,C.M. et al. (2011) miRTarBase: a database curates experimentally validated microRNA-target interactions. Nucleic Acids Res., 39, D163–D169.
......@@ -375,15 +375,15 @@ Each prioritization (‘job’) has three associated screens that facilitate the
1. Smith,B., Ashburner,M., Rosse,C., Bard,J., Bug,W., Ceusters,W., Goldberg,L.J., Eilbeck,K., Ireland,A., Mungall,C.J. et al. (2007) The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nat. Biotechnol., 25, 1251–1255.
1. Hunter,S., Jones,P., Mitchell,A., Apweiler,R., Attwood,T.K.,Bateman,A., Bernard,T., Binns,D., Bork,P., Burge,S. et al. (2012) InterPro in 2011: new developments in the family and domain prediction database. Nucleic Acids Res.,40, D306–D312.
1. Sherry,S.T., Ward,M.H., Kholodov,M., Baker,J., Phan,L., Smigielski,E.M. and Sirotkin,K. (2001) dbSNP: the NCBI database of genetic variation. Nucleic Acids Res., 29, 308–311.
1. Altshuler,D.M., Gibbs,R.A., Peltonen,L., Dermitzakis,E., Schaffner,S.F., Yu,F., Bonnen,P.E., de Bakker,P.I., Deloukas,P., Gabriel,S.B. et al. (2010) Integrating common and rare genetic variation in diverse human populations. Nature, 467, 52–58.
1. Altshuler,D.M., Gibbs,R.A., Peltonen,L., Dermitzakis,E., Schaffner,S.F., Yu,F., Bonnen,P.E., de Bakker,P.I., Deloukas,P., Gabriel,S.B. et al. (2010) Integrating common and rare genetic variation in diverse human populations. Nature, 467, 52–58.
1. 1000 Genomes Project Consortium. (2010) A map of human genome variation from population-scale sequencing. Nature, 467, 1061–1073.
1. Hindorff,L.A., Sethupathy,P., Junkins,H.A., Ramos,E.M., Mehta,J.P., Collins,F.S. and Manolio,T.A. (2009) Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl Acad. Sci. USA, 106, 9362–9367.
1. Hindorff,L.A., Sethupathy,P., Junkins,H.A., Ramos,E.M., Mehta,J.P., Collins,F.S. and Manolio,T.A. (2009) Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl Acad. Sci. USA, 106, 9362–9367.
1. Stenson,P.D., Ball,E.V., Mort,M., Phillips,A.D., Shiel,J.A., Thomas,N.S., Abeysinghe,S., Krawczak,M. and Cooper,D.N. (2003) Human gene mutation database (HGMD): 2003 update. Hum. Mutat., 21, 577–581.
1. Johnson,A.D. and O’Donnell,C.J. (2009) An open access database of genome-wide association results. BMC Med. Genet, 10, 6.
1. McKusick,V. (1998) A Catalog of Human Genes and Genetic Disorders, 12th edn. John Hopkins University Press,Baltimore, MD.
1. Forbes,S.A., Bindal,N., Bamford,S., Cole,C., Kok,C.Y., Beare,D., Jia,M., Shepherd,R., Leung,K., Menzies,A. et al. (2011) COSMIC: mining complete cancer genomes in the catalogue of somatic mutations in cancer. Nucleic Acids Res., 39, D945–D950.
1. Kerrien,S., Aranda,B., Breuza,L., Bridge,A., Broackes-Carter,F., Chen,C., Duesbury,M., Dumousseau,M., Feuermann,M., Hinz,U. et al. (2012) The Intact molecular interaction database in 2012. Nucleic Acids Res., 40, D841–D846.
1. Croft,D., O’Kelly,G., Wu,G., Haw,R., Gillespie,M., Matthews,L., Caudy,M., Garapati,P., Gopinath,G., Jassal,B. et al. (2011) Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res., 39, D691–D697.
1. Croft,D., O’Kelly,G., Wu,G., Haw,R., Gillespie,M., Matthews,L., Caudy,M., Garapati,P., Gopinath,G., Jassal,B. et al. (2011) Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res., 39, D691–D697.
1. Demir,E., Cary,M.P., Paley,S., Fukuda,K., Lemer,C., Vastrik,I.,Wu,G., D’Eustachio,P., Schaefer,C., Luciano,J. et al. (2010) The BioPAX community standard for pathway data sharing. Nature Biotechnol., 28, 935–942.
1. Alemán Z, García-García F, Medina I, Dopazo J (2014): A web tool for the design and management of panels of genes for targeted enrichment and massive sequencing for clinical applications. Nucleic Acids Res 42: W83-7.
1. [Alemán A](http://www.ncbi.nlm.nih.gov/pubmed?term=Alem%C3%A1n%20A%5BAuthor%5D&cauthor=true&cauthor_uid=24803668)>, [Garcia-Garcia F](http://www.ncbi.nlm.nih.gov/pubmed?term=Garcia-Garcia%20F%5BAuthor%5D&cauthor=true&cauthor_uid=24803668)>, [Salavert F](http://www.ncbi.nlm.nih.gov/pubmed?term=Salavert%20F%5BAuthor%5D&cauthor=true&cauthor_uid=24803668)>, [Medina I](http://www.ncbi.nlm.nih.gov/pubmed?term=Medina%20I%5BAuthor%5D&cauthor=true&cauthor_uid=24803668)>, [Dopazo J](http://www.ncbi.nlm.nih.gov/pubmed?term=Dopazo%20J%5BAuthor%5D&cauthor=true&cauthor_uid=24803668)> (2014). A web-based interactive framework to assist in the prioritization of disease candidate genes in whole-exome sequencing studies. [Nucleic Acids Res.](http://www.ncbi.nlm.nih.gov/pubmed/?term=BiERapp "Nucleic acids research.")>42 :W88-93.
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......@@ -4,7 +4,7 @@ Welcome to Salomon supercomputer cluster. The Salomon cluster consists of 1008 c
The cluster runs [CentOS Linux](http://www.bull.com/bullx-logiciels/systeme-exploitation.html) operating system, which is compatible with the RedHat [Linux family.](http://upload.wikimedia.org/wikipedia/commons/1/1b/Linux_Distribution_Timeline.svg)
## Water-cooled Compute Nodes With MIC Accelerator
## Water-Cooled Compute Nodes With MIC Accelerator
![](../img/salomon)
......
......@@ -48,7 +48,7 @@ To check whether your proxy certificate is still valid (by default it's valid 12
To access Salomon cluster, two login nodes running GSI SSH service are available. The service is available from public Internet as well as from the internal PRACE network (accessible only from other PRACE partners).
#### Access from PRACE network:
#### Access From PRACE Network:
It is recommended to use the single DNS name salomon-prace.it4i.cz which is distributed between the two login nodes. If needed, user can login directly to one of the login nodes. The addresses are:
......@@ -70,7 +70,7 @@ When logging from other PRACE system, the prace_service script can be used:
$ gsissh `prace_service -i -s salomon`
```
#### Access from public Internet:
#### Access From Public Internet:
It is recommended to use the single DNS name salomon.it4i.cz which is distributed between the two login nodes. If needed, user can login directly to one of the login nodes. The addresses are:
......@@ -127,7 +127,7 @@ Apart from the standard mechanisms, for PRACE users to transfer data to/from Sal
There's one control server and three backend servers for striping and/or backup in case one of them would fail.
### Access from PRACE network
### Access From PRACE Network
| Login address | Port | Node role |
| ----------------------------- | ---- | --------------------------- |
......@@ -160,7 +160,7 @@ Or by using prace_service script:
$ globus-url-copy gsiftp://`prace_service -i -f salomon`/home/prace/_YOUR_ACCOUNT_ON_SALOMON_/_PATH_TO_YOUR_FILE_ file://_LOCAL_PATH_TO_YOUR_FILE_
```
### Access from public Internet
### Access From Public Internet
| Login address | Port | Node role |
| ----------------------- | ---- | --------------------------- |
......
......@@ -631,7 +631,7 @@ The output should be similar to:
There are two ways how to execute an MPI code on a single coprocessor: 1.) lunch the program using "**mpirun**" from the
coprocessor; or 2.) lunch the task using "**mpiexec.hydra**" from a host.
#### Execution on coprocessor
#### Execution on Coprocessor
Similarly to execution of OpenMP programs in native mode, since the environmental module are not supported on MIC, user has to setup paths to Intel MPI libraries and binaries manually. One time setup can be done by creating a "**.profile**" file in user's home directory. This file sets up the environment on the MIC automatically once user access to the accelerator through the SSH.
......@@ -680,7 +680,7 @@ The output should be similar to:
Hello world from process 0 of 4 on host cn207-mic0
```
#### Execution on host
#### Execution on Host
If the MPI program is launched from host instead of the coprocessor, the environmental variables are not set using the ".profile" file. Therefore user has to specify library paths from the command line when calling "mpiexec".
......@@ -725,7 +725,7 @@ A simple test to see if the file is present is to execute:
/bin/pmi_proxy
```
#### Execution on host - MPI processes distributed over multiple accelerators on multiple nodes
#### Execution on Host - MPI Processes Distributed Over Multiple Accelerators on Multiple Nodes
To get access to multiple nodes with MIC accelerator, user has to use PBS to allocate the resources. To start interactive session, that allocates 2 compute nodes = 2 MIC accelerators run qsub command with following parameters:
......@@ -885,7 +885,7 @@ A possible output of the MPI "hello-world" example executed on two hosts and two
!!! note
At this point the MPI communication between MIC accelerators on different nodes uses 1Gb Ethernet only.
#### Using the PBS automatically generated node-files
#### Using the PBS Automatically Generated Node-Files
PBS also generates a set of node-files that can be used instead of manually creating a new one every time. Three node-files are genereated:
......
......@@ -236,7 +236,7 @@ For example, once you have loaded one or more modules that were installed with t
In addition, only **one single version** of each software package can be loaded at a particular time. For example, once you have the Python/3.5.2-intel-2017.00 module loaded, you can not load a different version of Python in the same session/job script, neither directly, nor indirectly as a dependency of another module you want to load.
### Unloading modules: ml -modname(s
### Unloading Modules
To revert the changes to the environment that were made by a particular module, you can use ml -<modname>.
For example:
......
......@@ -2,3 +2,6 @@
curl -s https://code.it4i.cz/hrb33/modules-anselm/raw/master/anselm.md -o docs.it4i/modules-anselm.md
curl -s https://code.it4i.cz/hrb33/modules-salomon/raw/master/salomon.md -o docs.it4i/modules-salomon.md
curl -s https://code.it4i.cz/hrb33/modules-salomon/raw/master/salomon-uv.md -o docs.it4i/modules-salomon-uv.md
curl -s https://code.it4i.cz/hrb33/modules-anselm/raw/master/anselm.csv -o scripts/modules-anselm.csv
curl -s https://code.it4i.cz/hrb33/modules-salomon/raw/master/salomon.csv -o scripts/modules-salomon.csv
curl -s https://code.it4i.cz/hrb33/modules-salomon/raw/master/salomon-uv.csv -o scripts/modules-salomon-uv.csv
......@@ -12,9 +12,9 @@ def get_data(filename):
return list(reader) # only return the reader when you have finished.
your_list = []
your_list += get_data('modules-anselm.csv')
your_list += get_data('modules-salomon.csv')
your_list += get_data('modules-salomon-uv.csv')
your_list += get_data('./scripts/modules-anselm.csv')
your_list += get_data('./scripts/modules-salomon.csv')
your_list += get_data('./scripts/modules-salomon-uv.csv')
#print your_list
#a=[["python/2.8.1",1],["python/2.9.1",2],["python/2.8.1",4],["python/3.0.1",4]]
......@@ -37,6 +37,11 @@ c=[
"USA",
]
print '!!! Hint "Cluster Acronyms"'
print ' \* A - Anselm'
print ' \* S - Salomon'
print ' \* U - uv1 at Salomon'
print "| Module | Versions | Clusters |"
print "| ------ | -------- | -------- |"
......
......@@ -28,7 +28,7 @@ def main(location):
return_value = 0
prev_line = lines.pop(0)
disabled = 0
print(`location`)
echo_filename = False
for line in lines:
if line.startswith("``") and disabled == 0:
disabled = 1
......@@ -37,19 +37,23 @@ def main(location):
disabled = 0
if line.startswith('#') and disabled == 0:
if line != titlecase(line[:(line.find("]"))], callback=abbreviations)+line[(line.find("]")):]:
print()
if return_value == 0 and echo_filename == False:
print("%s" % location)
echo_filename = True
print("-"+line,end="")
print("+"+titlecase(line[:(line.find("]"))], callback=abbreviations)+line[(line.find("]")):],end="")
print("+"+titlecase(line[:(line.find("]"))], callback=abbreviations)+line[(line.find("]")):],end="")
print()
return_value = 1
if (line.startswith('---') or line.startswith('===')) and disabled == 0:
if prev_line != titlecase(prev_line[:(prev_line.find("]"))], callback=abbreviations)+prev_line[(prev_line.find("]")):]:
print()
if return_value == 0 and echo_filename == False:
print("%s" % location)
echo_filename = True
print("-"+prev_line,end="")
print("+"+titlecase(prev_line[:(prev_line.find("]"))], callback=abbreviations)+prev_line[(prev_line.find("]")):],end="")
print("+"+titlecase(prev_line[:(prev_line.find("]"))], callback=abbreviations)+prev_line[(prev_line.find("]")):],end="")
print()
return_value = 1
prev_line = line
if return_value == 0:
print("==Ok==")
exit(return_value)
if __name__ == "__main__":
try:
......
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