Walk Me Through
This is a "quick start" introduction into using the BA-HPC cluster at the Bibliotheca Alexandrina. This covers the general activities most users will deal with when using the cluster.
In order to properly follow this quick start guide, you should have
- an account on BA-HPC cluster
- an account at our support system
- knowledge on how to use
- a basic familiarity with Unix
If do not have an account, you could follow the above links before proceeding with this quick start.
2. Key generationAfter receiving mail notification about your account has been successfully created on the BA-HPC. Just ons step more is required to establish a connection between your machine and the BA-HPC cluster. All you will need is to create public/private authentication key pair. It is a more secured and easy way to use than creating a password beside the username.
Creating a passphrase while generating generate private/public key pair is a good practice. It encrypts your private key locally on your machine. You may need the creation of passphrase in case if your machine is shared with other colleagues/users. You will be asked about the passphrase everytime you turn on your machine to login. You can skip the passphrase creation if your machine is for personal use. You will not be asked for any passwords to login.
Unix-like systems users
Use the ssh-keygen command to generate a public/private key pair
The following steps will help you to create public/private generate key pair via Bitvise
- Install SSH client.
- Generate public/private key pair from Client Key manager
- Press the Generate button to generate a new keypair
- Export SSH public key in OpenSSH format, 2048-bit length
For more details and screenshots you can check this link
Upload your public key
After generating the public key, you can be directed to BA-HPC web portal via your dashboard to upload your public key.
You will go to your proposal you have created and click the
- For key must be in Openssh format warning, kindly make sure you exported SSH public key in OpenSSH format.
- For Key is invalid warning, try to remove any trailing white spaces at the end of your public key.
- Please do not share your private key with any. We update your account only by taking your public key to add it in your account.
3. Logging into the login node
The cluster have two login nodes, login01 and login02, that are available for users to log into. From any of the login nodes you can submit and monitor your jobs, look at results of the jobs, etc.
For most tasks you will wish to accomplish, you will start by logging into the login node. In order to do that, you need to use the Secure Shell protocol (SSH). This is standardly installed as ssh on Unix systems, and clients are available for Windows and Mac.
Unix-like systems user
On Unix-like systems, you can login to
login02 by executing in the terminal
$ ssh firstname.lastname@example.org
If your private key is not palced under $HOME/.ssh directory on your local machine.
$ ssh -i path/to/private/key email@example.com
If you're using BitVise client, you need to set the host to
login02.c2.hpc.bibalex.org, Initial method to
key, port to
22 and from Client Key, choose the name of the last key you created and uploaded to BA-HPC website, for example in the following screenshot named
Profile 2. Besides, you'll need to use your real username instead of
- Make sure the network your are using allow SSH protocol through port 22.
- Make sure the private key and public key are related to each other and not different ones. In case you have created several keys.
- For Linux user, please make sure the file permission -rw------- is applied to your private key only and your .ssh directory permission is drwx------
- For Windows user, you can check the reason of the error from Bitvise logs.
- For Windows user, kindly make sure you are not using a depricated version of Bitvise.
- For dual-boot user, please make sure to create key pair and login from the same system.
On the login node
- It's not preferred to run software commands on login node, it may result with memory fault or segmentation errors. The best practice is to run commands via Slurm, to distribute the job on the compute nodes, whether CPU or GPU.
- DO NOT RUN computationally intensive processes on the login node. Maximum runtime of any process on login node is 30 minutes.
- On login node, maximum simultaneous processes for each user is 100.
Note that your home directory
$HOMEis limited to 100 MB. Use
datadirectory (linked at $HOME/data) for large data.
4. Uploading and downloading your work
Unix-like systems user
For a user-friendly way, you may use a free SFTP program called
On your local machine, excute the follwoing command to perfoem file transfer:
$ rsync -a ~/directory/file firstname.lastname@example.org:/home/username/data/
After filling login data, You can press and trasnfer file through drag and drop for files/folders upload and download.
5. Setting up your environment modules
The software environment used on BA-HPC cluster can be managed via modules. Modules facilitate the task of updating applications and provide a user-controllable mechanism for accessing software revisions and controlling combination of versions. For the job to executed, you must load any required modules before submitting your job.
Common commands to work with modules:
module avail # lists available modules module list # lists current loaded modules module help module-name # help on specific module module whatis module-name # brief description on a specific module module display module-name # display changes by a given module module load module-name # load a specific module module unload module-name # unloads a specific module module clear # unloads all loaded modules
module loadmultiple versions of the same module at the same time (including same version for different compilers). The module command will report a conflict if you attempt to do so.
If you work with liscenced software. kindly note you are responsible for providing your own licenses for software not in the public domain. It's recommended to build the liscenced software, which support parallelism, under you data directory. The version of the software has to be for use on a Linux cluster.
6. Submitting parallel jobs
To handle the queuing, scheduling, and execution of jobs the BA-HPC cluster use a batch scheduling
system called Slurm (Simple Linux Utility for Resource Management). Normally, you will submit jobs by writing a job script file and
submitting the job to Slurm with the
sbatch command takes a number of options (some of which can be omitted or defaulted). These options define various
requirements of the job, which are used by the scheduler to figure out what is needed to run your job, and to schedule
it to run as soon as possible, subject to the constraints on the system, usage policies, and considering the other
users of the cluster.
The options to
sbatch can be given on the command line, or in most cases inside the job script. When given inside the
job script, the option is placed alone on a line starting with
#SBATCH (you must include a space after the
#SBATCHlines SHOULD come before any non-comment/non-blank line in the script.
Choosing CPU queue
If your application doesn't know anything about the multiple cores, then it has only a single stream of instructions that will only occupy a single processor core. And most of the cores will be unused. Computers in a cluster are commonly called compute node, every compute node has multiple independent processors inside it, called cores. As the programmer, to make use of the cores, you have to run sequences of instructions. There are two ways to do that.
One is called multi-processing, where job allocate cores from more than one node. There are other options for set
--ntasks-per-node=# rather than
The difference between task and cpu is that a task may compromise multiple cpus, though this scenario seems kind of rare on the cluster, they are the same when cpus-per-task=1.
The following example requests 24 tasks, each with a single core.
The other is multi-threading, where all the cores are on the same node. You must set
nodes=1 and then set
--cpus-per-task to the number of OpenMP threads you wish to use.
You can use the script example for OpenMP job.
SLURM is very flexible and allows you to be very specific about your resource requests. However, requesting more cores doesn't usually make the job run faster. This may result with communication overhead between cores. Thinking about your application and doing some testing will be important to determine the best set of resources for your specific job.
Choosing GPU queueOn the BA-HPC cluster, you only specify a partition when you want to run your job on a GPU enabled nodes. To request GPUs for your job on the HPC, you need to add the
#SBATCH --gres=gpu:Noptions to your job script file, where N specifies the number of GPUs that you are requesting. Kindly note that we have at most 2 GPUs per node and the total number of GPU enabled nodes is 16.
Currently, we do not directly charge usage of GPUs. GPU based jobs usage will charged for
the CPUs they consume on the GPU enabled node.Every GPU enabled node have 2 GPUs and 16 CPU cores.
Since all jobs run in exclusive mode, consuming 1 GPU resource will also consume 8 CPU cores. So, you can start with assigning
Choosing Slurm account
You have by default slurm account for cpu and gpu, cpu is recognized by default.
For GPU slurm account, it may be necessary to edit the job script with your project name to set a non-default Slurm bank account in the header block, e.g.:
Setting job time
You will need to add the
#SBATCH --time=00:15:00 option to your job script file to set a limit on the total run time of the job allocation. The time for the job depends on your estimation for how long it may take to finish.
[username@login01 ~]$ squeue JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON) 84353 cpu jobname username PD 0:00 1 (AssocGrpCPUMinutesLimit)
Besides not setting time limit, if you ran out of CPU or GPU hours you may encounter AssocGrpCPUMinutesLimit. Your job state will change from RUNNING to PENDING again. You can monitor your CPU or GPU quota here.
7. Creating and submitting MPI job
The Message Passing Interface (MPI) is a standardized and portable system for communication between the various tasks of parallized jobs in HPC environments. A number of different implementations of MPI libraries are available at our cluster. Although the MPI interface itself is somewhat standardized, the different versions are not binary compatible. It is important that you match the MPI implementation you use and with which your code was compiled. The recommended MPI library on BA-HPC cluster is Intel MPI libraries.
Let's start by compiling a sample MPI program written in C. The program initialize a defined number
of processes that print the 'Hello World' line to a file along with process rank.
The source code for this program can be found at this
To start using MPI environment, load the intel impi module using:
[username@login01 ~]$ module load impi
Let's use our newly loaded module to compile our C program, using MPI C compiler wrapper
[username@login01 ~]$ mpicc hello-mpi.c -o hello-mpi.bin
Now that we got our binary file, let's create a job script to submit it to OGS.
Here's an example of a simple script that will specify the necessary job parameters, we'll call it
#! /bin/bash #SBATCH --job-name=mpi_job #SBATCH --ntasks=24 #SBATCH --cpus-per-task=1 #SBATCH --time=00:15:00 mpirun -np 24 ./hello-mpi.bin
#SBATCH --job-name=mpi_jobspecify the job name.
#SBATCH --ntasks=24restart the job in the case the system has a crash or is rebooted.
#SBATCH --cpus-per-task=1specifies the number of cores per task, we will need only one core per process.
mpirun -np 24 ./hello-mpi.binrun the MPI executable and specifies number of processes.
Now that you have a job script, you need to submit the job to the cluster with the
command. Make sure the Intel MPI libraries are loaded first then use 'sbatch' to submit the job
to the scheduler
[username@login01 ~]$ module list Currently Loaded Modulefiles: 1) GCCcore/5.4.0 2) binutils/2.26-GCCcore-5.4.0 3) icc/2016.3.210-GCC-5.4.0-2.26 4) ifort/2016.3.210-GCC-5.4.0-2.26 5) iccifort/2016.3.210-GCC-5.4.0-2.26 6) impi/184.108.40.206-iccifort-2016.3.210-GCC-5.4.0-2.26 [username@login01 ~]$ sbatch hello-mpi.sh Submitted batch job 156
At this point, your job has been placed in the queue, and will wait its turn for resources to be available. Depending on how heavily used the cluster is at that time, and how many resources you are requesting, your job might start within minutes or it might wait for hours.
Once resources become available, our scheduler will assign resources to your job, including one or more nodes.
The standard output and standard error streams will be directed to a file, by default
in the directory where you started the job, where the
is the job number as described above.
Output from your job can be viewed in the above specified file shortly after it starts running (assuming it has output something). This can be used to check the status of your job, although it is recommended make your code generates a lot of output to redirect it to another file.
For our trivial example from the last section, when the job completes we should see something like
[username@login01]$ cat slurm-156.output Hello world: rank 12 of 24 running on comp085.local Hello world: rank 1 of 24 running on comp085.local Hello world: rank 2 of 24 running on comp085.local Hello world: rank 4 of 24 running on comp085.local Hello world: rank 7 of 24 running on comp085.local Hello world: rank 8 of 24 running on comp085.local Hello world: rank 9 of 24 running on comp085.local Hello world: rank 14 of 24 running on comp085.local Hello world: rank 15 of 24 running on comp085.local Hello world: rank 16 of 24 running on comp085.local Hello world: rank 17 of 24 running on comp085.local Hello world: rank 18 of 24 running on comp085.local Hello world: rank 20 of 24 running on comp085.local Hello world: rank 21 of 24 running on comp085.local Hello world: rank 0 of 24 running on comp085.local Hello world: rank 3 of 24 running on comp085.local Hello world: rank 5 of 24 running on comp085.local Hello world: rank 6 of 24 running on comp085.local Hello world: rank 10 of 24 running on comp085.local Hello world: rank 11 of 24 running on comp085.local Hello world: rank 13 of 24 running on comp085.local Hello world: rank 19 of 24 running on comp085.local Hello world: rank 22 of 24 running on comp085.local Hello world: rank 23 of 24 running on comp085.local
As you can see in the output files above, the MPI program executed and each process was assigned a unique rank, which was printed off along with the hostname.
8. Creating and submitting CUDA job
CUDA is a parallel computing platform and API model created by Nvidia. It allows you to use a CUDA-enabled GPU for general purpose processing – an approach termed GPGPU (General-Purpose computing on Graphics Processing Units). The CUDA platform is a software layer that gives direct access to the GPU's virtual instruction set and parallel computational elements, for the execution of compute kernels.
Again let's start by compiling a sample CUDA program written in C. The program use GPU to add two 2
vectors of integers in parallel. It starts by generating 2 vectors of size
n then pass
these vectors to the GPU memory, after that we make each core simply sums a single element from
each of the two input vectors and writes the result into the output vector. Finally, we print only
m number of elements into the output file. The source code for this program can be
found at this github gist
. To start using the CUDA library, let's load Intel C compiler and CUDA
[username@login01 ~]$ module load icc CUDA
Then let's use the loaded Nvidia CUDA Compiler (NVCC) to compile our source code
[username@login01 ~]$ nvcc vector-add.cu -o vector-add.bin
An example of a simple script that will specify the necessary job parameters for a GPU based job,
we'll call it
#!/bin/bash #SBATCH --job-name=first-cuda-job #SBATCH --account=g.projectname #SBATCH --partition=gpu #SBATCH --gres=gpu:1 #SBATCH --nodes=1 #SBATCH --ntasks=1 #SBATCH --time=00:15:00 ./vector-add.bin 100000 10
There's some additional options we've put in our job script:
#SBATCH --account=g.projectnameSpecify your GPU slurm account.
#SBATCH --partition=gpuSubmit the job to the gpu partition.
#SBATCH --gres=gpu:1Specify the number of GPUs. In this example we only need one GPU card.
#SBATCH --nodes=1Specify the number of required nodes. In this example we only need one node.
#SBATCH --ntasks=1Specify the number of CPU cores/process to be used. In this example we only need one process to initiate our program execution on the GPU. Maximum number of CPU cores/process to be used is 16.
./vector-add.bin 100000 10Generate two vectors of length 100000, and print only the first 10 elements in the output file.
Now that you have a job script, let's submit the job to the cluster. Make sure the CUDA library are loaded first then use 'sbatch' to submit the job to the scheduler
[username@login01 ~]$ module list Currently Loaded Modulefiles: 1) CUDA/8.0.44 [username@login01 ~]$ sbatch cuda-vec_add.sh Submitted batch job 157
After the job completes, we should see something like
[username@login01]$ cat slurm-157.out h_x = 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 h_y = 100000.0 99999.0 99998.0 99997.0 99996.0 99995.0 99994.0 99993.0 99992.0 99991.0 The sum is: 100001.0 100001.0 100001.0 100001.0 100001.0 100001.0 100001.0 100001.0 100001.0 100001.0
9. Creating and submitting Python job
Anaconda is a distribution of the Python and R programming languages for scientific computing that wil provide you with the packages you need by excuting the following
[username@login01 ~]$ module load Anaconda3 [username@login01 ~]$ source activate /share/apps/conda_envs/ba-hpc [username@login01 ~]$ conda list
conda listwill show you the available libraries we have, that most researchers use on the BA-HPC
If the anaconda library you need does not exist, you can mail us at
Working with Python pip
Actions below, are quite helpful when cache files crowdens your home more than 100 MB. So, you will move bin and lib directories to data directory and create symbolic links under .local which refrence the actual moved files. The same idea goes for .cache directory too.
[username@login01 ~]$ cd .local [username@login01 ~]$ mv bin lib ../data/ [username@login01 ~]$ ln -s ../data/bin . [username@login01 ~]$ ln -s ../data/lib . [username@login01 ~]$ cd .. [username@login01 ~]$ cd .cache [username@login01 ~]$ mv pip ../data/pip_cache [username@login01 ~]$ ln -s ../data/pip_cache pip
pipafter activating the ba-hpc env. Besides,
--usermust be specified in all pip commands.
Working with Jupyter
Jupyter comes in handy for data visualization projects, since it is a GUI (web) interface for coding pyhton, you will need run a certain submission script called
server.sh in the same directory that has jupyter notebooks under your data directory.
Besides logging to the BA-HPC at the begiining, you will need SSH-tunelling/Port forwarding connection to run and view your python code and results on the cluster. As running GUI applications is not supported on the cluster.
server.sh script covers the steps you need to follow for handling SSH-tunelling.
For Windows users, you will need to sbatch server.sh script to Slurm to run hour jupyter job. After that on your local machine, Open bitvise and select C2S (Client-to-Server) for ssh tunelling. For further reading.
This example is about serial and parallel Python implementations of the Leibniz formula for approximating the value of pi. Besides, it demonstrates the time differences between serial execution and parallel execution. The source code for this program and the submission scripts can be found at BA-HPC code repository.
You will need to load mpi4py module before submitting your script
[username@login01 ~]$ module load mpi4py
We have a code repository which is an accumulator for scripts intended as executable examples for users to carry out tasks related to the TensorFlow machine learning framework on a High-Performance Computing (HPC) system.
Listing GPU devices
The source code for this program and the submission script can be found at BA-HPC code repository.
Dynamic Recurrent Neural Network
A simple Tensorflow implementation of a Recurrent Neural Network (LSTM) that performs dynamic computation over sequences with variable length on a toy dataset. The source code for this program can be found at this link. The scripts to fetch and run this program can be found at BA-HPC code repository.
Convolutional Neural Network
A tensorflow tutorial demonstrates training a simple Convolutional Neural Network (CNN) to classify CIFAR images, which is a frequently used benchmark for image classification tasks. The source code for this program can be found at this link The scripts to fetch and run this program can be found at BA-HPC code repository.
10. Creating and submitting Gromacs job
In this section, you will see to run a `gmx mdrun` example, which is the "main computational chemistry engine within GROMACS. This version of the example uses GPU resources.You will need to load the To start using the libraries you need, load the module
[username@login01 ~]$ module load Gromacs
11. Creating and submitting Quantum Espresso job
Quantum ESPRESSO is an open-source plane-wave periodic density functional theory code and considered one of the products of (SCM) Software for Chemistry and Materials.
For QE 6.3, you can check an example for input file scf.in.
Before getting into work with qe you will need to load the required modules for running QE 6.3
[username@login01 ~]$ module load intel/2018
To make slurm take action to work on your input file. you will submit the submission script pw.x.sh
[username@login01 ~]$ sbatch pw.x.sh
For QE 6.7, you can easily use recent QE from the environment modules
[username@login01 ~]$ module load QuantumESPRESSO/6.7-intel-2019b
Here's an example of a submission script that specify the necessary job parameters
#!/bin/sh #SBATCH --job-name=qe-job #SBATCH --account=projectname #SBATCH --ntasks=24 #SBATCH --cpus-per-task=1 #SBATCH --time=03:00:00 mpirun -np "$SLURM_NTASKS" pw.x scf.in > opt.out
12. Monitoring job status
The basic command for monitoring your jobs' status is the
command. Because normally you are only interested in your jobs, it is advisable to add the
flags, to speed up the command and only show your jobs. Replace
with your username.
To check your jobs' state in the queue
[username@login01 ~]$ squeue -u $USER
Watch your job in the queue, time is updated every two seconds
[username@login01 ~]$ watch squeue -u $USER
To stop/cancel a job from the queue
[username@login01 ~]$ scancel <jobid>
After the job finishes
To check elapsed time in the same session
[username@login01 ~]$ sacct -Xo jobid,jobname,elapsed,state
To generally check all your jobs' status in any session
[username@login01 ~]$ sacct -S 2019-01-01 -u <username>
13. Monitoring CPU or GPU quota
To check your CPU hours usage
[username@login01 ~]$ cpumins <projectname> cpu : 3620550 of 4800000 mins (60342 of 80000 h) 75%
To check your GPU hours usage
[username@login01 ~]$ gpumins g.<projectname> gres/gpu : 16493 of 60000 mins (274 of 1000 h) 27%
14. Monitoring Lustre quota
You could use the following command to check your account storage:
[username@login01 ~]$ lfs quota -hg <projectname> /lfs01
We should see something like
[username@login01 ~]$ lfs quota -hg <projectname> /lfs01 Disk quotas for grp alex036 (gid 1034): Filesystem used quota limit grace files quota limit grace /lfs01 2.785G 10G 10.1G - 155 100000 105000 -
Here, the output shows the used storage under your data directory and also shows number of files which is 155 in this example.