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Sections Production environment, Programming environment are specific for the two partitions, Booster and Data Centric General Purpose (DCGP):
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hostname: login.leonardo.cineca.it
login01-ext.leonardo.cineca.it
login02-ext.leonardo.cineca.it
login05-ext.leonardo.cineca.it
login07-ext.leonardo.cineca.it
early availability: March, 2023 (Booster)
start of pre-production:June, 2023 (Booster)
last quarter 2023 (Data Centric January 2024 (DCGP)
start of production: August 2023 (Booster)
February 2024 (DCGP)
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This HPC system is the new pre-exascale Tier-0 EuroHPC Joint Undertaking supercomputer hosted by CINECA and currently built in the Bologna Technopole, Italy. It is supplied by EVIDEN ATOS, and it is based on two new specifically-designed compute blades, which are available through two distinct SLURM partitions on the cluster:
- X2135 GPU blade based on NVIDIA Ampere A100-64 accelerators - LEONARDO Booster partition
- X2140 CPU-only blade based on Intel Sapphire Rapids processors - LEONARDO Data Centric General Purpose (DCGP) partition
The overall system architecture also uses NVIDIA Mellanox InfiniBand High Data Rate (HDR) a BullSequana XH2135 supercomputer nodes, each with four NVIDIA Tensor Core GPUs and a single Intel CPU. It also uses NVIDIA Mellanox HDR 200Gb/s InfiniBand connectivity, with smart in-network computing acceleration engines that enable extremely low latency and high data throughput to provide the highest AI and HPC application performance and scalability.
System Architecture
Architecture: Atos BullSequana XH21355 "Da Vinci" blade - Booster - Atos BullSequana X2610 compute blade - Data-centric (will be available in the last quarter of the 2023)
Internal Network: Nvidia Mellanox HDR DragonFly+ 200 Gb/s
Storage: 106 PB (raw) Large capacity storage, 620 GB/s
High Performance Storage 5.4 PB, 1.4 TB/s Based on 31 x DDN Exascaler ES400NVX2
The system also includes a Capacity Tier and a Fast Tier storage, based on DDN Exascaler.
The Operating System is RedHat Enterprise Linux 8.6.
System Architecture
Login nodes: 4 nodesLogin nodes: in β production 1 (16 later): login14 accessible via IP 131.175.43.130, icelake no-gpu
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Peak performance details
| Node Performance | ||
Theoretical | CPU (nominal/peak freq.) | 1680 Gflops |
| GPU | 75000 Gflops | |
| Total | 76680 GFlops | |
| Memory Bandwidth (nominal/peak freq.) | 24.4 GB/s | |
Access
All the login nodes have an identical environment and can be reached with SSH (Secure Shell) protocol using the "collective" hostname:
>$ login.leonardo.cineca.it
which establishes a connection to one of the available login nodes. To connect to LEONARDO you can also indicate explicitly the login nodes:
$ login01-ext.leonardo.cineca.it
$ login02-ext.leonardo.cineca.it
$ login05-ext.leonardo.cineca.it
$ login07-ext.leonardo.cineca.it
The mandatory access to Leonardo LEONARDO is the two-factor authentication (2FA). Please refer to this link of the User Guide to activate and connect via 2FA. For information about data transfer from other computers please follow the instructions and caveats on the dedicated section section Data storage or the document Data Management.
Accounting
The accounting is still unavailable in this pre-production phase and will soon be implemented.(consumed budget) is active from the start of the production phase. For accounting information please consult our dedicated section.
The account_no name (or project) is important for batch executions. You need to indicate an account_no name to be accounted for in the scheduler, using the flag "-A"
#SBATCH -A <account_no>name>
With the "saldo -b" command you can list all the account_no name associated with your username.
>$ saldo -b (reports projects defined on LEONARDO Booster)
$ saldo --dcgp -b (reports projects defined on LEONARDO DCGP)
Please note that the accounting is in terms of consumed core hours, but it strongly depends also on the requested memory and local storage, and number of GPUs, please refer to the dedicated section.
Budget Linearization policy
On LEONARDO, as on the other HPC clusters in CinecaCINECA, a a linearization policy for the usage of project budgets has been defined and implemented. The The goal is to improve the response time, giving users the opportunity of using the cpu hours assigned to their project in relation to their actual size (total amount of core-hours).
Disks and Filesystems
The storage organization conforms to the CINECA infrastructure (see Section Data Storage and Filesystems).
In addition to the home directory directory $HOME, for for each user is defined a scratch area area $SCRATCH (or $CINECA_SCRATCH), a large disk for the storage of run time data and files.
An new user specific area $PUBLIC is defined on LEONARDO, useful for example to share installations with other users (it is indeed the default directory for SPACK sub-directories, see more details in the dedicated page).
A $WORK area is defined for each active project on the system, reserved for to all the collaborators of the project. In this pre-production phase the $WORK area is not yet available. Until the $WORK areas will be configured and put in place the automatic cleaning of the scratch area will NOT be active.A corresponding $FAST area is defined for each active project on the scratch filesystem, on its subset of "fast" NVMe SSD flash drives. As for $WORK, the $FAST area is reserved to all the collaborators of the project. An extension of the default $WORK quota (1 TB) can be granted if justified and essential for the course of the project's activity, while the use of the $FAST is limited to 1 TB of space per project.
Total Dimension (TB) | Quota (GB) | Notes | |||
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| $HOME | 0.46 PiB | 70GB 50GB per user |
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| $CINECA_SCRATCH | 41.4 40 PiB | no quota |
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| $PUBLIC | 0.46 PiB | 50GB per user |
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| $WORK | not yet available (10PB) | 30 PB | 1TB per project |
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| $FAST | 3.5PB | 1TB per project |
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- The work filesystem: the $WORK areas are not available yet. Until they will be configured and put in place the automatic cleaning of the scratch area will NOT be activeis NOT active yet, but it will soon be enforced.
It is also available a temporary storage area local to nodes on login and compute nodes (on the latter it is generated when the job starts and removed when it ends) and accessible via environment variable $TMPDIR. This area is:
- on the local SSD disks on login nodes (14 TB of capacity), mounted as /scratch_local (TMPDIR=/scratch_local). This is a shared area with no quota, remove all the files once they are not requested anymore. A cleaning procedure will be enforced in case of improper use of the area.
- on the local SSD disks on the serial node (lrd_all_serial, 14TB of capacity), managed via the slurm job_container/tmpfs plugin. This plugin provides a job-specific, private temporary file system space, with private instances of /tmp and /dev/shm in the job's user space (TMPDIR=/tmp, visible via the command "df -h"), removed at the end of the serial job. You can request the resource via sbatch directive or srun option "--gres=tmpfs:XX" (for instance: --gres=tmpfs:200GB), with a maximum of 1 TB for the serial jobs. If not explicitly requested, the /tmp has the default dimension of 10 GB.
- on the local SSD disks on DCGP nodes (3 TB of capacity). As for the serial node, the local /tmp and /dev/shm areas are managed via plugin, which at the start of the jobs mounts private instances of /tmp and /dev/shm in the job's user space (TMPDIR=/tmp, visible via the command "df -h /tmp"), and unmounts them at the end of the job (all data will be lost). You can request the resource via sbatch directive or srun option "--gres=tmpfs:XX", with a maximum of all the available 3 TB for DCGP nodes. As for the serial node, if not explicitly requested, the /tmp has the default dimension of 10 GB. Please note: for the DCGP jobs the requested amount of gres/tmpfs resource contributes to the consumed budget, changing the number of accounted equivalent core hours, see the dedicated section on the Accounting
- on RAM on the diskless booster nodes (with a fixed size of 10 GB, no increase is allowed, and the gres/tmpfs resource is disabled).
For a general discussion on the TMPDIR area, For more details please see the dedicated section of UG2.5: Data storage and FileSystems. On LEONARDO the $TMPDIR local area has 1 TB of available space.
Since all the filesystems are based on Lustre, the usual unix command "quota" is not working. Use the local command cindata to query for disk usage and quota ("cindata -h" for help) that will be available soon.
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$ cindataor the tool "cinQuota" available in the module cintools
$ cinQuotaFor more details about both these commands, please consult the section dedicated to how to monitor the occupancy.
Software environment
Module environment
The software modules are collected in different profiles and organized by functional categories (compilers, libraries, tools, applications, ...). The profiles are of two types: “programming” type (base and advanced) for compilation, debugging and profiling activities, and “domain” type (chem-phys, lifesc, ...) for the production activity. They can be loaded together.
"Base" profile is the default. It is automatically loaded after login and it contains basic modules for the programming activities (ibm, gnu, pgi, cuda compilers, math libraries, profiling and debugging tools, ...).
If you want to use a module placed under other profiles, for example an application module, you will have to previously load preventively the corresponding profile:
>module$ module load profile/<profile name>
>module$ module load <module name>
Almost all the softwares on Leonardo LEONARDO were installed with Spack manager, which loads automatically the possible dependencies, so so "autoload comand " command is unnecessary. In other cases could be useful to load dependencies with> module load autoload <module name>
For listing all profiles you have loaded you can use the following command:
>module$ module list
In order to detect all profiles, categories and modules available on LEONARDO, the command “modmap” will be soon is available as for the other clusters. With modmap you can see if the desired module is available and which profile you have to load to use it.
>modmap$ modmap -m <module_name>
Note: on LEONARDO you can find modules compiled to support GPUs and modules suitable only for CPUs. You can check the compiler in the full name of the module, where the version is specified (e.g. gromacs/2022.3--intel-oneapi-mpi--2021.10.0--oneapi–2023.2.0). Remind that modules compiled with gcc, nvhpc, cuda should be used only on the Booster partition, while modules compiled with intel oneapi are suitable for running on the DGCP partition. Please refer to the specific sections of the two partitons for more details on the available compilers: Booster Programming environment and DCGP Programming environment.
Spack environment
In case you don't find a software you are interested in, you can install it by yourself.
In this case, on Leonardo we also LEONARDO we offer the possibility to use the “spack” environment by loading the corresponding module. Please refer to the dedicated section in UG2.6: Production Environment
Please note that we are still optimizing Leonardo LEONARDO software stack, and more installations may be added/replaced. Always check with "module av" (the hash in the module name can change).
GPU and intra/inter connection environment
It will be described soon.
Production environment
Since LEONARDO is a general purpose syste and is used by several users at the same time, long production jobs must be submitted using a queuing system (scheduler). The scheduler guarantees that the access to the resources is as fair as possible
The production environment on LEONARDO, is based on the slurm scheduler, already in place on the cluster but still not complete and in a pre-production configuration.
Leonardo is based on a policy of node sharing among different jobs, i.e. a job can ask for resources and these can also be a part of a node, for example few cores and 1GPU. This means that, at a given time, one physical node can be allocated to multiple jobs of different users. Nevertheless, exclusivity at the level of the single core is guaranteed by low-level mechanisms.
Roughly speaking, there are two different modes to use an HPC system: Interactive and Batch. For a general discussion see the section Production Environment.
Interactive
A serial program can be executed in the standard UNIX way:
> ./programThis is allowed only for very short runs on the login nodes. Soon we will impose 10 minutes cpu-time limit for the interactive processes. Please do not execute parallel applications on the login nodes!
Batch
As usual on HPC systems, the large production runs are executed in batch mode. This means that the user writes a list of commands into a file (for example script.x) and then submits it to a scheduler (SLURM for Leonardo) that will search for the required resources in the system. As soon as the resources are available script.x is executed and the results and sent back to the user.
This is an example of script file:
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Remind that, on LEONARDO (at variance with other CINECA clusters), the default area where Spack directories are created (/cache, /install, /modules, /user_cache) is the $PUBLIC one (described in section Disks and Filesystems).
- Please refer to the general online guide to slurm and on task/thread bindings, and please pay attention to the setting of the SRUN_CPUS_PER_TASK for hybrid applications dispatched with "srun".
You can write your script file (for example script.x) using any editor, then you submit it using the command:
> sbatch script.xThe script file must contain both directives to SLURM and commands to be executed, as better described in the section Batch Scheduler SLURM.
Using SLURM directives you indicate the account_number (-A: which project pays for this work), where to run the job (-p: partition), what is the maximum duration of the run (--time: time limit). Moreover you indicate the resources needed, in terms of cores, GPUs (later) and memory.
One of the commands will be probably the launch of a parallel MPI application. In this case the right command is srun, as an alternative to the usual mpirun command. In this way you will get full support for process tracking, accounting, task affinity, suspend/resume and other features.
Please note: the "mail" directives are not effective yet.
SLURM partitions
A list of partitions defined on the cluster, with access rights and resources definition, can be displayed with the command sinfo:
> sinfo -o "%10D %20F %P"
The command returns a more readable output which shows, for each partition, the total number of nodes and the number of nodes by state in the format "Allocated/Idle/Other/Total".
In the following table you can find the main features and limits imposed on the partitions of Leonardo.
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SLURM
partition
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max running jobs per user/
max n. of cores/nodes/GPUs per user
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lrd_all_serial
(default)
not yet available
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min = 33 nodes
max =256 nodes *
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max = 3 nodes
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- *For the "boost_usr_prod" partition you can use at most 32 nodes (MaxTime=24:00:00). Please request the boost_qos_bprod QOS to go up to 512 nodes (MaxTime=10:00:00) This limit will be in place until May 25, when it will be reduced to 256 nodes with MaxTime=24:00:00 (production environment) before May 25.
- For EUROFusion users and their dedicated queues please refer to the dedicated document.
Graphic session
It will be available soon.
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Leonardo compute nodes host four A100 GPUs per node (CUDA compute capability 8.0). The most recent versions of nVIDIA CUDA toolkit and of the nVIDIA nvhpc compilers (ex PGI, supporting CUDA Fortran) is available in the module environment.
Compilers
You can check the complete list of available compilers on MARCONI with the command:
> module availableand checking the "compilers" section. The available compilers are:
- Gnu Compilers Collection (GCC)
- NVIDIA nvhpc (ex PGI)
- CUDA
NVIDIA nvhpc (ex PORTLAND PGI + NVIDIA CUDA)
As of August 5, 2020, the "PGI Compilers and Tools" technology is a part of the NVIDIA HPC SDK product, available as a free download from NVIDIA.
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You can proceed with the sections related to Production environment and Programming environment in the specific pages for the two partitions:
For legacy reasons, the nVIDIA nvhpc suite also offers the PGI C, C++, and Fortran compilers with their original names:
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To enable CUDA C++ or CUDA Fortran, and link with the CUDA runtime libraries, use the -cuda option (-Mcuda is deprecated). Use the -gpu option to tailor the compilation of target accelerator regions.
The OpenACC parallelization is enabled by the -acc flag. GPU targeting and code generation can be controlled by adding the -gpu flag to the compiler command line.
The OpenMP parallelization is enabled by the -mp compiler option. The GPU offload via OpenMP is enabled by the -mp=gpu option.
GNU compiler collection
The gnu compilers are always available. GCC version 8.5.0 is available without the need to load any gcc module. In the module environment you can find more recent version though.
The name of the GNU compilers are:
- g77: Fortran77 compiler
- gfortran: Fortran95 compiler
- gcc: C compiler
- g++: C++ compiler
The documentation can be obtained with the man command after loading the gnu module:
> man gfortan
> man gcc
CUDA
Compute Unified Device Architecture is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs). With CUDA, developers are able to dramatically speed up computing applications by harnessing the power of GPUs.
In GPU-accelerated applications, the sequential part of the workload runs on the CPU – which is optimized for single-threaded performance – while the compute intensive portion of the application runs on thousands of GPU cores in parallel. When using CUDA, developers program in popular languages such as C, C++, Fortran, Python and MATLAB and express parallelism through extensions in the form of a few basic keywords. We refer to the NVIDIA CUDA Parallel Computing Platform documentation.
Debugger and Profilers
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