HPC tutorial

How to connect to a remote session

1. Get a terminal application

Definition: A terminal is an interface in which you can type and execute text based commands.

Download your Terminal if you don't have one already.

• Windows

  • MobaXterm download

  Remarks: In Windows, there is a default command line, called command prompt. However, the names of many commands is different and some commands do not exist. For that reason, use another terminal, such as MobaXterm.

• Mac

  • iTerm2 download
  • Terminal

• Linux

  • Terminal

2. Access with SSH

Background information

• What is SSH?

• What is a SSH key pair?

Step 2a: Connect to UL HPC (Linux / Mac OS / Unix)

Run the following commands in a terminal (substituting studentXX with the name of the key file you received from us):

(laptop)$> ssh -p 8022 -i /path/to/studentXX.key [email protected]

Step 2b: Connect to UL HPC (Windows)

• Open MobaXterm: Start > Program Files > MobaXterm
• Click on Session
  • In SSH Session:
    • Remote host: access-gaia.uni.lu
      • Check the Specify username box
      • Username: studentXX
    • Port: 8022
  • In Advanced SSH Settings
    • Check Use private key box
      • Select the studentXX.key file you received.
  • Click on Save

How to reserve resources on the cluster

Web monitoring interfaces

Each cluster offers a set of web services to monitor the platform usage:

• Monika, the visualization interface of the OAR scheduler, which display the status of the clusters as regards the jobs running on the platform.
• A pie-chart overview of the platform usage
• DrawGantt, the Gantt visualization of jobs scheduled on OAR
• Ganglia, a scalable distributed monitoring system for high-performance computing systems such as clusters and Grids.

Reserving resources with OAR

The basics

• reference documentation

OAR is an open-source batch scheduler which provides simple yet flexible facilities for the exploitation of the UL HPC clusters.

• it permits to schedule jobs for users on the cluster resource
• a OAR resource corresponds to a node or part of it (CPU/core)
• a OAR job is characterized by an execution time (walltime) on a set of resources. There exists two types of jobs:
  • interactive: you get a shell on the first reserved node
  • passive: classical batch job where the script passed as argument to oarsub is executed on the first reserved node

For simplicity we will only cover interactive jobs in this tutorial.

We will now see the basic commands of OAR.

Connect to the frontend of the Gaia cluster (access-gaia.uni.lu). You can request resources in interactive mode with the following command:

(access)$> oarsub -I

Notice that with no parameters, oarsub gave you one resource (one core) for two hours. You were also directly connected to the node you reserved with an interactive shell.

Now exit the reservation:

(node)$> exit      # or CTRL-D

When you run exit, you are disconnected and your reservation is terminated.

Job management

You can check the status of your running jobs using oarstat command:

(access)$> oarstat      # access all jobs
(access)$> oarstat -u   # access all your jobs

Hierarchical filtering of resources

OAR features a very powerful resource filtering/matching engine able to specify resources in a hierarchical way using the / delimiter. The resource property hierarchy is as follows:

enclosure -> nodes -> cpu -> core

• Reserve interactively 2 cores on 3 different nodes belonging to the same enclosure (total: 6 cores) for 3h15:

   (access)$> oarsub -I -l /enclosure=1/nodes=3/core=2,walltime=3:15
  

• Reserve interactively two full nodes belonging to different enclosures for 6 hours:

    (access)$> oarsub -I -l /enclosure=2/nodes=1,walltime=6
  

Software environment

On the ULHPC clusters the software is managed with Environment Modules. This is a software package that allows us to provide a multitude of applications and libraries in multiple versions on the UL HPC platform. The tool itself is used to manage environment variables such as PATH, LD_LIBRARY_PATH and MANPATH, enabling the easy loading and unloading of application/library profiles and their dependencies.

• Introduction to Environment Modules by Wolfgang Baumann
• Modules tutorial @ NERSC
• UL HPC documentation on modules

Test data

Important: This is already done for the studentXX accounts, so you can skip this section and go ahead to run mpiBLAST.

This tutorial relies on several input files for the bioinformatics packages, thus you will need to download them before following the instructions in the next sections:

(access)$> mkdir -p ~/bioinfo-tutorial/tophat ~/bioinfo-tutorial/mpiblast
(access)$> cd ~/bioinfo-tutorial
(access)$> wget --no-check-certificate https://raw.github.com/ULHPC/tutorials/devel/advanced/Bioinformatics/tophat/test_data.tar.gz -O tophat/test_data.tar.gz
(access)$> wget --no-check-certificate https://raw.github.com/ULHPC/tutorials/devel/advanced/Bioinformatics/tophat/test2_path -O tophat/test2_path
(access)$> wget --no-check-certificate https://raw.github.com/ULHPC/tutorials/devel/advanced/Bioinformatics/mpiblast/test.fa -O mpiblast/test.fa

Additionally you need a .ncbirc file containing the paths to the blast databases (too big to download quickly). The file can be downloaded from here and needs to be placed in your $HOME directory (make sure to backup an existing $HOME/.ncbirc before overwriting it with the one in this tutorial).

Run mpiBLAST

Characterization: data intensive, little RAM overhead, native parallelization

Description

mpiBLAST: Open-Source Parallel BLAST

mpiBLAST is a freely available, open-source, parallel implementation of NCBI BLAST. By efficiently utilizing distributed computational resources through database fragmentation, query segmentation, intelligent scheduling, and parallel I/O, mpiBLAST improves NCBI BLAST performance by several orders of magnitude while scaling to hundreds of processors [*].

Example

This example will be run in an interactive session, with batch-mode executions being proposed later on as exercises.

# Connect to Gaia (Linux/OS X):
(yourmachine)$> ssh -p 8022 -i /path/to/studentXX.key [email protected]

# Request 1 full node in an interactive job:
(access-gaia)$> oarsub -I -t moonshot -l nodes=1,walltime=00:30:00

# Load the bioinfo software set
(node)$> module use $RESIF_ROOTINSTALL/bioinfo/modules/all

# Check the mpiBLAST versions installed on the clusters:
(node)$> module avail 2>&1 | grep -i mpiblast

# Load the default mpiBLAST version:
(node)$> module load bio/mpiBLAST

# Check that it has been loaded, along with its dependencies:
(node)$> module list

# The mpiBLAST binaries should now be in your path
(node)$> mpiformatdb --version
(node)$> mpiblast --version

mpiBLAST requires access to NCBI substitution matrices and pre-formatted BLAST databases. For the purposes of this tutorial, a FASTA (NR) database has been formatted and split into 12 fragments, enabling the parallel alignment of a query against the database.

We will run a test using mpiBLAST. Note that mpiBLAST requires running with at least 3 processes, 2 dedicated for scheduling tasks and coordinating file output, with the additional processes performing the search.

# Go to the test directory and execute mpiBLAST with one core for search
(node)$> cd ~/bioinfo-tutorial/mpiblast
(node)$> export I_MPI_FABRICS=shm:tcp
(node)$> mpirun -hostfile $OAR_NODEFILE -np 3 mpiblast -p blastp -d nr -i test.fa -o test.out

# Note the speedup when using 4 cores
(node)$> mpirun -hostfile $OAR_NODEFILE -np 4 mpiblast -p blastp -d nr -i test.fa -o test.out

Exercises

Launch jobs with 8 and 12 cores across two and three nodes and measure the speedup obtained.

Hint: To reserve 8 cores across two nodes, use oarsub -I -l nodes=2/core=4,walltime=00:30:00

TopHat (and Bowtie2)

Characterization: data intensive, RAM intensive

Description

TopHat : A spliced read mapper for RNA-Seq

TopHat is a program that aligns RNA-Seq reads to a genome in order to identify exon-exon splice junctions. It is built on the ultrafast short read mapping program Bowtie [*].

Bowtie2: Fast and sensitive read alignment

Bowtie 2 is an ultrafast and memory-efficient tool for aligning sequencing reads to long reference sequences. It is particularly good at aligning reads of about 50 up to 100s or 1,000s of characters, and particularly good at aligning to relatively long (e.g. mammalian) genomes [*].

Example

This example will show you how to use TopHat in conjunction with Bowtie2.

# Connect to Gaia (Linux/OS X):
(yourmachine)$> ssh -p 8022 -i /path/to/studentXX.key [email protected]

# Request 1 full node in an interactive job:
(gaia-frontend)$> oarsub -I -t moonshot -l nodes=1,walltime=00:30:00

# Load the bioinfo software set
(node)$> module use $RESIF_ROOTINSTALL/bioinfo/modules/all

# Check the Tophat versions installed on the clusters:
(node)$> module avail 2>&1 | grep -i tophat

# Load the default Tophat version:
(node)$> module load bio/TopHat

# Load the corresponding Bowtie2 version:
(node)$> module load bio/Bowtie2/2.3.2-foss-2017a

# Check that both have been loaded:
(node)$> module list

# The Tophat and Bowtie2 binaries should now be in your path
(node)$> tophat --version
(node)$> bowtie2 --version

Now we will make a quick TopHat test, using the provided sample files:

# Go to the test directory, unpack the sample dataset and go to it
(node)$> cd ~/bioinfo-tutorial/tophat
(node)$> tar xzvf test_data.tar.gz
(node)$> cd test_data

# Launch TopHat in serial mode
(node)$> tophat -r 20 test_ref reads_1.fq reads_2.fq

# Launch TopHat in parallel mode
(node)$> tophat -p 4 -r 20 test_ref reads_1.fq reads_2.fq

We can see that for this fast execution, increasing the number of threads does not improve the calculation time due to the relatively high overhead of thread creation. Note that TopHat / Bowtie are not MPI applications and as such can take advantage of at most one compute node.

Next, we will make a longer test, where it will be interesting to monitor the TopHat pipeline (with htop for example) to see the transitions between the serial and parallel stages (left as an exercise).

# Load the file which will export $TOPHATTEST2 in the environment
(node)$> source ~/bioinfo-tutorial/tophat/test2_path

# Launch TopHat in parallel mode
(node)$> tophat2 -p 4 -g 1 -r 200 --mate-std-dev 30 -o ./  $TOPHATTEST2/chr10.hs $TOPHATTEST2/SRR027888.SRR027890_chr10_1.fastq $TOPHATTEST2/SRR027888.SRR027890_chr10_2.fastq

To monitor the run of Tophat, open a new session or terminal window and connect to the Gaia cluster.

# Check the running jobs
(access)$> oarstat -u

# Connect to your running job
(access)$> oarsub -C <jobid>

# Monitor CPU and RAM usage
(node)$> htop

The input data for the first test corresponds to the TopHat test set, while the second test is an example of aligning reads to the chromosome 10 of the human genome as given here.

Proposed exercises

Launch jobs with 1, 2, and 4 cores on one node, using the second test files, and measure the speedup obtained.

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References

• Image 1: by Saleh Salem on DigitalOcean licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
• Image 2: from SSH tutorial by Sébastien Varrette (falkor)
• Text for Section 1 modified from LCSB R3 Linux 101 tutorial
• Text for Section 2 and 3 modified from ULHPC "Getting started" tutorial
• Text for Section 4-6 modified from ULHPC "Bioinformatics" tutorial