Getting started with Mahti
This is a quick start guide for Mahti users. It is assumed that you have previously used CSC supercomputing resources like Puhti, Sisu or Taito. If not, you can start by looking at overview of CSC supercomputers.
Go to MyCSC to apply for access to Mahti or
view your projects and their project numbers if you already have
access. You can also use the command
Connecting to Mahti
Connect using a normal ssh-client:
yourcscusername is the username you get from CSC.
Default modules, which are loaded automatically, are
Compilers and MPI
Currently, Mahti has GNU compiler suites (versions 11.2.0, 9.4.0 and 8.5.0),
as well as AMD compiler suites. All compiler suites can be used with the
mpicxx (C++), or
mpif90 (Fortran) wrappers. We recommend
to start with the GNU compiler suite, but for some applications the other
suites may provide better performance.
In Mahti, many applications benefit from hybrid MPI/OpenMP parallelization, so it is recommended to build a hybrid version if it is supported by your application.
You need to have the MPI module loaded when submitting your jobs.
High performance libraries
Mahti has several high performance libraries installed, see more information about libraries.
More information about specific applications can be found here. Note, the pre-installed selection is not as large as on Puhti.
In Mahti, many applications benefit from hybrid MPI/OpenMP parallelization, however, the optimum ratio of MPI tasks and OpenMP threads depends a lot on the particular application as well as on particular input dataset. Mahti supports also simultaneous multithreading (SMT), i.e. two threads can be run on the same physical CPU core. Benefits of multithreading depend also on the application, in some cases it improves performance while in some cases performance becomes worse. Binding of threads to CPU cores can also have an impact on performance.
More information about controlling hybrid applications can be found here.
The project based shared storage can be found under
/scratch/<project>. Note that this folder is shared by all
users in a project. This folder is not meant for long term data
storage and files that have not been used for 180 days will be
automatically removed. Note that this policy is currently implemented
only on Puhti.
The default quota for this folder is 1 TB. There is also a persistent
project based storage with a default quota of 50 GB. It is located
/projappl/<project>. Each user can store up to 10 GB of data in
their home directory (
The disk areas for different supercomputers are separate, i.e. home, projappl and scratch in Puhti cannot be directly accessed from Mahti.
You can check your current disk usage with
csc-workspaces. More detailed information about storage can be found
here and a guideline on managing data on the
/scratch disk here. See also the LUE tool
for efficiently querying how much data/files you have in a directory.
Moving data between Mahti and Puhti
Data can be moved between supercomputers via Allas by first uploading the data from one supercomputer and then downloading it to the other. This is the recommended approach if the data should also be preserved for a longer time.
Data can also be moved directly between the supercomputers with the
command. For example, in order to copy
my_results (which can be either a
file or a directory) from Puhti to the directory
in Mahti, one can issue in Puhti the command:
rsync -azP my_results <username>@mahti.csc.fi:/scratch/project_2002291
See Using rsync for more detailed instructions
How Mahti and Puhti differ?
If you are new to supercomputers, or the details below are unfamiliar, you likely should start with Puhti and some introductory tutorials first. In a nutshell, Mahti is meant for large parallel jobs, and Puhti for a wide variety of small to medium sized jobs including special resources.
|Resources are granted||By full nodes||By finer detail (cores/memory/...)|
|Minimum job size||128 cores (1 node)||1 core (1/40 node)|
|Maxmimum job size (cores)||200 nodes* (25600)||26 nodes (1040)|
|Memory per node (average per core)||245 GB (2 GB)||192 - 1500 GB (4 - 37 GB)|
|GPUs||NVIDIA A100||NVIDIA V100|
|Fast local disk||(only on GPU nodes)||yes (NMVe)|
*and even more via Grand Challenge calls.