Page History

System Architecture

Image Added

Compute Nodes:

• 528 636 computing nodes each 2 x CPU Intel CascadeLake 8260,  with 24 cores each, 2.4 GHz, 384GB RAM, subdivided in:
  
  • •  348 422 standard nodes ("thin nodes") 480 GB SSD
    • 180 180  data processing nodes ("fat nodes") 2TB SSD, 3TB Intel OptainOptane
    •  36 GPU nodes   34  GPU nodes  (visualization "viz" ) with 2x NVIDIA GPU V100 with 100Gbs Infiniband interconnection and 2TB SSD.
• 77 computing server servers OpenStack for cloud computing (ADA CLOUD), 2x CPU 8260 Intel CascadeLake, 24 cores, 2.4 GHz, 768 GB RAM, with 100Gbs Ethernet interconnection.
• 20 PB of active storage accessible from both cloud and HPC nodes.5 PB of fast storage for HPC system.
• 1 PB Ceph storage for Cloud (full NVMe/SSD)
• 720 TB fast storage (IME DDN solution)

Login and Service nodes:

10 login nodes and 5 service nodes. All the nodes are interconnected through an Infiniband network, with OPA v10.6, capable of a maximum bandwidth of 100Gbit/s between each pair of nodes.

Accounting

For more information about accounting, please consult our dedicated section.

Budget Linearization policy

On GALILEO100 a linearization policy for the usage of project budgets has been defined and implemented. For each account, a monthly quota is defined as:

monthTotal = (total_budget / total_no_of_months)

Starting from the first day of each month, the collaborators of any account are allowed to use the quota at full priority. As long as the budget is consumed, the jobs submitted from the account will gradually lose priority, until the monthly budget (monthTotal) is fully consumed. At that moment, their jobs will still be considered for execution, but with a lower priority than the jobs from accounts that still have some monthly quota left.

This policy is similar to those already applied by other important HPC centers in Europe and worldwide. 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 organisation conforms to the CINECA infrastructure (see Section "Data storage and Filesystems") . In addition to the home directory ($HOME), for each user is defined a scratch area $CINECA_SCRATCH, a large disk for storing run time data and files. $WORK is defined for each active project on the system, reserved for all the collaborators of the project. This is a safe storage area to keep run time data for the whole life of the project.

• permanent/backed up, user specific, local

• temporary, user specific, local
• automatic cleaning procedure of data older than 50 days (time interval can be reduced in case of critical usage ratio of the area. In this case, users will be notified via HPC-News)

• permanent, project specific, local
• extensions can be considered if needed (mailto: superc@cineca.it)

It is also available a temporary storage local on compute nodes generated when the job starts and accessible via environment variable $TMPDIR. For more details please see the dedicated section of UG2.5: Data storage and FileSystems. On Galileo the $TMPDIR local area has 49 GB of available space.

$DRES points to the shared repository where Data RESources are maintained. This is a data archive area available only on-request, shared with all CINECA HPC systems and among different projects.

$DRES is not mounted on the compute nodes. This means that you can't access it within a batch job: all data needed during the batch execution has to be moved on $WORK or $CINECA_SCRATCH before the run starts.

Since all the filesystems are based on gpfs (General Parallel FIle System), the usual unix command "quota" is not working. Use the local command "cindata" to query for disk usage and quota ("cindata -h" for help):

  > cindata

Modules environment

The software modules are collected in different profiles and organized by functional category (compilers, libraries, tools, applications,..).

On GALILEO100 the profiles are of two types, “domain” type (chem, phys, lifesc,..) for the production activity and “programming” type (base and advanced) for compilation, debugging and profiling activities and that 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 (intel e gnu 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 load preventively the corresponding profile:

>module load profile/<profile name>

>module load autoload <module name>

For listing all profiles you have loaded you can use the following command:

>module list

In order to detect all profiles, categories and modules available on GALILEO the command “modmap” is available:

>modmap

With modmap you can see if the desired module is available and which profile you have to load to use it.

>modmap -m <module name>

Production environment

Since GALILEO100 is a general purpose system and it is used by several users at the same time, long production jobs must be submitted using a queuing system. This guarantees that access to the resources is as fair as possible.

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:

> ./program

This is allowed only for very short runs, since the interactive environment set on the login nodes has a 10 minutes time limit: for longer runs please use the "batch" mode.

A parallel program can be executed interactively only by submitting an "Interactive" SLURM batch job, using the "srun" command: the job is queued and scheduled as any other job, but when executed, the standard input, output, and error streams are connected to the terminal session from which srun was launched.

For example, to start an interactive session with the MPI program "myprogram", using one node and two processors, you can launch the command:

> srun -N 1 --ntasks-per-node=2 -A <account_name> --pty /bin/bash

SLURM will then schedule your job to start, and your shell will be unresponsive until free resources are allocated for you. If not specified, the default time limit for this kind of jobs is one hour.

When the shell returns a prompt inside the compute node, you can execute your program by typing:

> srun ./myprogram

or

> mpirun ./myprogram

SLURM automatically exports the environment variables you defined in the source shell, so that if you need to run your program "myprogram" in a controlled environment (i.e. with specific library paths or options), you can prepare the environment in the login shell and be sure to find it again in the interactive shell o the compute node.

Batch

As usual, on systems using SLURM, you can submit a script script.x using the command:

You can get a list of defined partitions with the command:

> sinfo

For more information and examples of job scripts, see section Batch Scheduler SLURM.

Submitting serial Batch jobs

The  partition is available with one core and a maximum walltime of  hours. It runs on two nodes and it is designed for pre/post-processing serial analysis, and for moving your data (via rsync, scp etc.) in case more than 10 minutes are required to complete the data transfer. In order to use this partition you have to specify the SLURM flag "-p":

#SBATCH -p gll_all_serial

The gll_all_serial partition has a limit of 1 task per job and 4GB of memory per job. If you wish to ask for more than a core on a single job, remember to add on your jobscript the specific about the memory limit, since the default per core is 3.5GB and therefore your job won't enter because the required memory exceeds the partition limit.

Graphic session

If a graphic session is desired we recommend to use the tool "RCM". See the corresponding session to know more about how to download and use RCM.

A complete reconfiguration of the RCM environment is in progress. This guide will be completed as soon as a final configuration will be implemented.

Submitting parallel Batch jobs

To run parallel batch jobs on GALILEO you need to specify the partition gll_usr_prod, or any other partition described in this user guide.

Users who need to run on GPU-equipped nodes need to specify the partition gll_usr_gpuprod.

If you do not specify the partition, your jobs will try to run on the default partition bdw_all_serial, meant for serial jobs, eventually failing if specific partition limits (maximum four tasks per job and maximum walltime of 4 hours) are violated.

The minimum number of cores you can request for a batch job is 1. The maximum number of cores that you can request is 2304 (64 nodes). It is also possible to request a maximum walltime of 24 hours. Defaults are as follows:

• If you do not specify the walltime (by means of the #SBATCH --time directive), a default value of 30 minutes will be assumed.

• If you do not specify the number of cores (by means of the "SBATCH -n" directive) a default value of 36 will be assumed.

• If you do not specify the amount of memory (as the value of the "SBATCH --mem" DIRECTIVE), a default value of 3000MB will be assumed.

• The maximum memory per node is 118000MB

A special QOS (qos_special) is also available for not-ordinary types of jobs, such as a walltime larger than 24 hours. Since it violates our standard policy, there are restrictions in its usage, and users who want to use it need to be enabled by the User Support staff. Please write to superc@cineca.it in case you think you need to use it. Your request will be evaluated and, if approved, you will be allowed to use the special QOS for a limited period of time.

Use of GPUs on Galileo100

A special QOS (qos_special) is also available for not-ordinary types of jobs, such as a walltime larger than 24 hours. Since it violates our standard policy, there are restrictions in its usage, and users who want to use it need to be enabled by the User Support staff. Please write to superc@cineca.it in case you think you need to use it. Your request will be evaluated and, if approved, you will be allowed to use the special QOS for a limited period of time.

The gll_usr_gpuprod partition is defined on 42 Broadwell nodes (18-cores Intel Xeon E5-2697 v4 @ 2.40GHz), each equipped with 1 nVidia K80 GPUs (seen as two K40 gpus). All users using an account with available budget can submit jobs on this partition and use GPU nodes on GALILEO.  

The maximum number of nodes that can be required on gll_usr_gpuprod is 4 for a maximum walltime of 08:00:00 hours. The maximum memory is 118000 MB. 

In regards of writing a SLURM jobscript, you need to request the GPU as "gres":

#SBATCH --partition=gll_usr_gpuprod

#SBATCH --gres=gpu:kepler:N     (N=1,2)

GALILEO is also equipped with two nodes with one nVIDIA Volta (V100) GPUs each, accessible for tests for a limited period of time. Please write to superc@cineca.it if you are interested to test the Volta GPUs, with a brief motivation for your request. Once your request is approved and you are enabled to use these resources (via the association to a special QOS "gll_qos_gpudev"), you can submit jobs to the Volta node with the following options:

#SBATCH --partition=gll_usr_gpudev

#SBATCH --qos=gll_qos_gpudev

#SBATCH --gres=gpu:volta:1

Users with reserved resources

Users of projects that require reserved resources (such as industrial users or users associated to an agreement that involves dedicated resources) will be associated to a QOS.

Using the specific QOS (i.e. specifying the QOS in the submission script) , and specifying the partition gll_spc_prod, users associated to the allowed project will run their jobs on reserved nodes in the gll_spc_prod partition:

>#SBATCH --partition=gll_spc_prod

>#SBATCH --qos=<specific_qos>

Summary

In the following table, you can find all the main features and limits imposed on the SLURM partitions and QOS.

SLURM

partition

QOS

# cores per job

max walltime

max running jobs per user/

max n. of cpus/nodes per user

max memory per node

(MB)

priority

notes

g100_all_serial

(default partition)

noQOS

1

04:00:00

4 jobs / 4 cpus

g100_usr_interactive

8:00:00

g100_usr_prod

noQOS

g100_qos_dbg

g100_qos_bprod

min = 1

max =  (64 nodes)

min = 1

max =

min =

max = 64 nodes

24:00:00

02:00:00

24:00:00

1536000M

95

85

gll_usr_gpuprod

noQOS

min = 1

max = 144

08:00:00

4 nodes

gll_spc_prod

Every account needs to have a valid QOS to access this partition

Depending on the QOS used by the particular account

24:00:00

/

Partition dedicated to specific kind of users.

gll_meteo_prod

Accounting

For more information about accounting, please consult our dedicated section.

Budget Linearization policy

On GALILEO100 a linearization policy for the usage of project budgets has been defined and implemented. For each account, a monthly quota is defined as:

monthTotal = (total_budget / total_no_of_months)

Starting from the first day of each month, the collaborators of any account are allowed to use the quota at full priority. As long as the budget is consumed, the jobs submitted from the account will gradually lose priority, until the monthly budget (monthTotal) is fully consumed. At that moment, their jobs will still be considered for execution, but with a lower priority than the jobs from accounts that still have some monthly quota left.

This policy is similar to those already applied by other important HPC centers in Europe and worldwide. 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 ($HOME), for each user is defined a scratch area $CINECA_SCRATCH, a large disk for storing run time data and files. The new variable $SCRATCH is also available, resolving in the same path of $CINECA_SCRATCH. $WORK is defined for each active project on the system, reserved for all the collaborators of the project. This is a safe storage area to keep run time data for the whole life of the project.

The filesystem organization is based on LUSTRE open source parallel file system.

It is also available a temporary storage local on compute nodes generated when the job starts and accessible via environment variable $TMPDIR. For more details please see the dedicated section of UG2.5: Data storage and FileSystems. On Galileo100 the $TMPDIR local area has 293 GB of available space.

$DRES points to the shared repository where Data RESources are maintained. This is a data archive area available only on-request, shared with all CINECA HPC systems and among different projects.

$DRES is not mounted on the compute nodes. This means that you can't access it within a batch job: all data needed during the batch execution has to be moved on $WORK or $CINECA_SCRATCH before the run starts.

Use the local command "cindata" to query for disk usage and quota ("cindata -h" for help):

  > cindata

or the tool "cinQuota" available in the module cintools

  > cinQuota

For more details about both these commands, please consult the section dedicated to how to monitor the occupancy

Dedicated node for Data transfer and download

A time limit of 10 cpu-minutes for processes running on login nodes has been set.
For Data transfer or download that may require more time, we set up a dedicated "data" VM accessible with a dedicated alias.
Login via ssh to this VM is not allowed. Environment variables as $HOME or $WORK are not defined, so you always have to make the complete path to the files you need to copy explicit.
For example to copy data to Galileo100 using rsync you can run the following command:

rsync -PravzHS </data_path_from/file> <your_username>@data.g100.cineca.it:<complete_data_path_to>

You can also use the "data" VM onto login nodes to move data from Galileo100 to another location with public IP:

ssh -xt data.g100.cineca.it rsync -PravzHS <complete_data_path_from/file> </data_path_to> 

this command will open a session on the VM that will not be closed until the rsync command is completed.

In similar ways you can use also scp and sftp commands if you prefer them.

The data VM also offers wget and curl commands. If you need to wget a large amount of data for a public site, you can run the following command:

ssh -xt data.g100.cineca.it wget <url/file> -P </data_path_to> 

this command will open a session on the VM that will not be closed until the wget command is completed.

Modules environment

The software modules are collected in different profiles and organized by functional category (compilers, libraries, tools, applications,..).

On GALILEO100 the profiles are of two types, “domain” type (bioinf, chem-phys, lifesc,..) for the production activity and “programming” type (base and advanced) for compilation, debugging and profiling activities and that 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 (intel e gnu 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 load preventively the corresponding profile:

>module load profile/<profile name>

>module load autoload <module name>

For listing all profiles you have loaded you can use the following command:

>module list

In order to detect all profiles, categories and modules available on GALILEO100 the command “modmap” is available:

>modmap

With modmap you can see if the desired module is available and which profile you have to load to use it.

>modmap -m <module name>

Spack environment 

In case you don't find a software you are interested in, you can install it by yourself. 
In this case, on GALILEO100 we also offer the possibility to use the “spack” environment by loading the corresponding module. Please refer to the dedicated section in UG2.6: Production Environment.

Production environment

Since GALILEO100 is a general purpose system and it is used by several users at the same time, long production jobs must be submitted using a queuing system. This guarantees that access to the resources is as fair as possible.

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:

> ./program

This is allowed only for very short runs, since the interactive environment set on the login nodes has a 10 minutes time limit: for longer runs please use the "batch" mode.

A parallel program can be executed interactively only by submitting an "Interactive" SLURM batch job, using the "srun" command: the job is queued and scheduled as any other job, but when executed, the standard input, output, and error streams are connected to the terminal session from which srun was launched.

For example, to start an interactive session with the MPI program "myprogram", using one node and two processors, you can launch the command:

> salloc -N 1 --ntasks-per-node=2 -A <account_name> 

SLURM will then schedule your job to start, and your shell will be unresponsive until free resources are allocated for you. If not specified, the default time limit for this kind of jobs is one hour.

When the shell returns a prompt inside the compute node, you can execute your program by typing:

> srun ./myprogram

(srun is recommended with respect to mpirun for this environment)

SLURM automatically exports the environment variables you defined in the source shell, so that if you need to run your program "myprogram" in a controlled environment (i.e. with specific library paths or options), you can prepare the environment in the login shell and be sure to find it again in the interactive shell o the compute node.

On systems using SLURM, you can submit a script script.x using the command:

> sbatch script.x

You can get a list of defined partitions with the command:

> sinfo

For more information and examples of job scripts, see section Batch Scheduler SLURM.

Submitting serial Batch Jobs

The partition will be available in the full production.

Graphic session

If a graphic session is desired we recommend to use the tool "RCM". Please install the latest version of RCM. See the corresponding paragraph to know more about how to download and use RCM.

Submitting parallel Batch Jobs

To run parallel batch jobs on GALILEO100 you need to specify the partition and the qos that are described in this user guide.

If you do not specify the partition, your jobs will try to run on the default partition g100_all_serial.

The minimum number of cores you can request for a batch job is 1. The maximum number of cores that you can request is 3072 (64 nodes). It is also possible to request a maximum walltime of 24 hours. Defaults are as follows:

• If you do not specify the walltime (by means of the #SBATCH --time directive), a default value of 30 minutes will be assumed.

• If you do not specify the number of cores (by means of the "SBATCH -n" directive) a default value of 1 core will be assumed.

• If you do not specify the amount of memory (as the value of the "SBATCH --mem" DIRECTIVE), a default value of 7800 MB per core will be assumed on g100_usr_prod and g100_usr_smem partitions. While on the g100_usr_bmem partition, the default value of memory assumed is  63200 MB per core.

The maximum memory per node is 375300MB (366.5GB) for thin and viz nodes, about 3TB for fat nodes. A study on the performances of the thin and fat nodes is ongoing.

On GALILEO100 there are four partitions dedicated to the production:

• g100_usr_prod: this partition collects all the thin and persistent memory nodes (memory per node 375300 MB).
• g100_usr_smem: this partition is composed only of thin nodes (memory per node 375300 MB).
• g100_usr_pmem: this partition is composed only of Persistent memory nodes - more info in a following update (memory per node 375300 MB).
• g100_usr_bmem: this partition is composed only of fat nodes (memory per node 3 TB). It is reserved for jobs that need more than 375300 MB of memory per node and is out of the g100_usr_prod partition.

We encourage you to use the partition more suitable for your job. For more information on the partitions look to Summary.

Processor affinity

Processor affinity, or CPU pinning, enables the binding of processes and threads to a CPU (or group of CPUs). It is crucial to ensure the correct affinity so to avoid the CPUs overallocation, with a significant reduction of performances. It becomes a critical matter when you ask for a full node but, for your specific reasons (memory needs etc.) you don't use all the cores.  

The following indications work when running your executables with srun, which is the recommended option against mpirun. We refer to a hybrid MPI/OpenMP case compiled with the Intel oneAPI suite.

Given your optimal value of OMP_NUM_THREADS and number of processes, to obtain the full node ask for a number of task such that  ( --ntasks-per-node * --cpus-per-task )= 48.

• To avoid the processes over allocation of cores rely on the --cpu-bind=cores option of srun  (you can skip it if you use all the requested cores)
• To enforce the threads affinity use the Intel parameter KMP_AFFINITY, or the OpenMP parameter OMP_PLACES
• To distribute the MPI tasks consecutively inside the sockets, use the -m block:block option of srun (or the equivalent sbatch directive #SBATCH -m block:block)

#!/bin/bash
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=12
#SBATCH --cpus-per-task=4
#SBATCH --account=<your_account>

module load autoload intelmpi/oneapi-2021--binary

export OMP_NUM_THREADS=4

export KMP_AFFINITY=compact    # or OMP_PLACES=cores

srun --cpu-bind=cores -m block:block <your_exe>

Use of GPUs on Galileo100

to be soon defined

Users with reserved resources

Users of projects that require reserved resources (such as industrial users or users associated to an agreement that involves dedicated resources) will be associated to a QOS qos_ind.

Using the  qos_ind (i.e. specifying the QOS in the submission script) , and specifying the partition g100_spc_prod, users associated to the allowed project will run their jobs on reserved nodes in the g100_spc_prod partition with the features and limits imposed for the particular account.

>#SBATCH --partition=g100_spc_prod

>#SBATCH --qos=qos_ind

Summary

In the following table, you can find all the main features and limits imposed on the SLURM partitions and QOS. For all partitions, if the memory is not explicitly requested, a DefMemPerCPU = 7800 will be assigned for each requested core. If the walltime is not explicitly requested, a DefaultTime=00:30:00 will be assigned to the job. If the partition is not explicitly requested, the default g100_all_serial partition will be assigned.

Intel Compilers

The native, and recommended, compilers on GALILEO100 are the Intel ones, since the architecture is based on Intel processors and therefore using the Intel compilers may result in a significant improvement in performance and stability of your code. On the cluster is installed the new suite Intel OneAPI. Initialize the environment with the module command:

> module load intel/<VERSION>

The suite contains the new Intel oneAPI nextgen compilers (icx, icpx, ifx) and the classic compilers (icc, icpc, ifort):

NOTE:

• ICX is a new compiler. It has functional and behavioral differences compared to ICC. You can expect some porting will be needed for existing applications using ICC. According to Intel, the transition from ICC Classic to ICX is smooth and effortless. However, you must port and tune any existing applications from ICC Classic to ICX. Please refer to the official Intel Porting Guide for ICC Users to DPCPP or ICX
• IFORT is a completely new compiler. According to Intel, although considerable effort is being made to make the transition from ifort to ifx as smooth and as effortless as possible, customers can expect that some effort may be required to tune their application. IFORT will remain Intel’s recommended production compiler until ifx has performance and features superior to ifort. Please refer to the official Intel Porting Guide for ifort Users to ifx
• Please refer to the official Intel C++ Developer Guide and Reference and Fortran Developer Guide and Reference for an exhaustive list of compiler options

The documentation can be obtained with the man command after loading the relevant module:

> man ifort
> man icc

Some miscellaneous flags are described in the following:

-extend-source    Extend over the 77 column F77's limit 
-free / -fixed    Free/Fixed form for Fortran
-ip               Enables additional interprocedural optimization for single-file compilation
-ipo              Enables interprocedural optimization between files - whole program optimisation
-qopenmp          enables the parallelizer to generate multi-threaded code based on OpenMP directives

NOTE for the migration from Galileo to Galileo100: In principle, binaries generated on Galileo should work, but we strongly recommend you to reinstall all your software applications since on Galileo100 there is a different Operating System (Centos 8.3).

GNU compilers

The gnu compilers are always available but they are not the best optimizing compilers, especially for an Intel OneAPI-based cluster like GALILEO100.

For a more recent version of the compiler, initialize the environment with the module command:

> module load gnu

The name of the GNU compilers are:

• g77: Fortran77 compiler
• gfortran: Fortran compiler with "gnu" standard
• gcc: C compiler
• g++: C++ compiler

The "gnu" standard is the default value for the -std option. It specifies a superset of the latest Fortran standard that includes all of the extensions supported by GNU Fortran, although warnings will be given for obsolete extensions not recommended for use in new code. To change the standard to which the program is expected to conform, set the -std option to one of the possible values (f95, f2003, f2008, f2018, gnu, or legacy).

The documentation can be obtained with the man command:

> man gfortan
> man gcc

Some miscellaneous flags are described in the following:

-ffixed-line-length-132       To extend over the 77 column F77's limit
-ffree-form / -ffixed-form    Free/Fixed form for Fortran
-fopenmp

Compilers

You can check the complete list of available compilers on GALILEO with the command:

> module available

checking the "compilers" section.

In general, the available compilers are:

• INTEL (ifort, icc, icpc) : ► module load intel
• PGI - Portland Group (pgf77,pgf90,pgf95,pghpf, pgcc, pgCC): ► module load pgi (profile/advanced)
• GNU (gcc, g77, g95): ► module load gnu

After loading the appropriate module, use the "man" command to get the complete list of the flags supported by the compiler, for example:

> module load intel
> man ifort

There are some flags that are common for all these compilers. Others are more specific. The most common are reported later for each compiler.

1. If you want to use a specific library or a particular include file, you have to give their paths, using the following options

-I/path_include_files        specify the path of the include files
-L/path_lib_files -l<xxx>    specify a library lib<xxx>.a in /path_lib_files

1. If you want to debug your code you have to turn off optimisation and turn on run time checkings: these flags are described in the following section.
2. If you want to compile your code for normal production you have to turn on optimization by choosing a higher optimization level

-O2 or -O3      Higher optimisation levels

INTEL Compiler

Intel family compiler suite is recommended on GALILEO, since the architecture is based on Intel processors and therefore using the Intel compilers may result in a significant improvement in performance and stability of your code. Initialize the environment with the module command:

> module load intel

The names of the Intel compilers are:

• ifort: Fortran77 and Fortran90 compiler
• icc: C compiler
• icpc: C++ compiler

The documentation can be obtained with the man command after loading the relevant module:

> man ifort
> man icc

Some miscellaneous flags are described in the following:

PORTLAND Group (PGI)

Initialize the environment with the module command:

> module load profile/advanced
> module load pgi

The name of the PGI compilers are:

• pgf77: Fortran77 compiler
• pgf90: Fortran90 compiler
• pgf95: Fortran95 compiler
• pghpf: High Performance Fortran compiler
• pgcc: C compiler
• pgCC: C++ compiler

The documentation can be obtained with the man command after loading the relevant module:

> man pgf95
> man pgcc

Some miscellaneous flags are described in the following:

GNU compilers

The gnu compilers are always available but they are not the best optimizing compilers, especially for an Intel-based cluster like GALILEO. The default version is 4.8.5, you do not need to load the module for using it.

For a more recent version of the compiler, initialize the environment with the module command:

> module load gnu

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:

> man gfortan
> man gcc

Some miscellaneous flags are described in the following:

-ffixed-line-length-132       To extend over the 77 column F77's limit
-ffree-form / -ffixed-form    Free/Fixed form for Fortran

Debuggers and Profilers

If your code aborts at runtime, there may be a problem with it. In order to solve it, you can decide to analyze the core file (feature not available if the code is compiled with PGI) or to run your code using a debugger.

In both cases, you need to enable compiler runtime checks, by putting specific flags during the compilation phase. In the following we describe those flags for the different Fortran compilers: if you are using the C or C++ compiler, please keep in mind that the flags may differ.

The following flags are generally available for all compilers and are mandatory for an easier debugging session:

-O0     Lower level of optimisation
-g      Produce debugging information

Other flags are compiler-specific and are described in the following.

INTEL Fortran compiler

The following flags are useful (in addition to "-O0 -g") for debugging your code:

-traceback        generate extra information to provide source file traceback at run time
-fp-stack-check   generate extra code to ensure that the floating-point stack is in the expected state
-check bounds     enables checking for array subscript expressions
-fpe0             allows some control over floating-point exception handling at run-time

PORTLAND Group (PGI) Compilers

The following flags are useful (in addition to "-O0 -g") for debugging your code:

                      Enable 

handling 

of 

OpenMP 

directives 

GNU Fortran compilers

The following flags are useful (in addition to "-O0 -g") for debugging your code:

"#pragma omp" in C/C++ and "!$omp" in Fortran. 
         

       When -fopenmp is specified, the compiler generates parallel code according to the 
                              OpenMP Application Program Interface v4.5. This option implies -pthread and -fopenmp-simd

Debuggers and Profilers

If your code aborts at runtime, there may be a problem with it. In order to solve it, you can decide to analyze the core file or to run your code using a debugger.

-O0     Lower level of optimisation
-g      Produce debugging information

-traceback                   generate extra information to provide source file traceback at run time
-fp-stack-check              generate extra code to ensure that the floating-point stack is in the expected state
-fvar-tracking               Generates enhanced debug information useful in finding scalar local variables
-fvar-tracking-assignments Generates enhanced debug information useful for breakpoints and stepping. It 
 tells the debugger to stop only at machine instructions that achieve the final 
 effect of a source statement. 
-check bounds                enables checking for array subscript expressions
-fpe0                        allows some control over floating-point exception handling at run-time
 

-Wall             Enables warnings pertaining to usage that should be avoided
-fbounds-check    Checks for array subscripts

> pgdbg [options] ./myexec [args]
> mpirun [options] -dbg=pgdbg ./myexec [args]

> module load profile/advanced
> module load pgi
> pgf90 -O0 -g -C -Ktrap=ovf,divz,inv  -o myexec myprog.f90
> pgdbg ./myexec

> module load gnu
> gfortran -O0 -g -Wall -fbounds-check -o myexec myprog.f90
> gdb ./myexec

> ulimit -c unlimited (bash)
> limit coredumpsize unlimited (csh/tcsh)

> export decfort_dump_flag=TRUE  (bash)       
> setenv decfort_dump_flag TRUE  (csh/tcsh)

> module load intel
> ifort -O0 -g -traceback -fp-stack-check -check bounds -fpe0 -o myexec prog.f90
> ulimit -c unlimited
> export decfort_dump_flag=TRUE
> ./myexec
> ls -lrt
  -rwxr-xr-x 1 aer0 cineca-staff   9652 Apr  6 14:34 myexec
  -rw------- 1 aer0 cineca-staff 319488 Apr  6 14:35 core.25629
> idbc ./myexec core.25629

> module load profile/advenced
> module load pgi
> pgf90 -O0 -g -C -Ktrap=ovf,divz,inv  -o myexec myprog.f90
> ulimit -c unlimited
> ./myexec
> ls -lrt
  -rwxr-xr-x 1 aer0 cineca-staff   9652 Apr  6 14:34 myexec
  -rw------- 1 aer0 cineca-staff 319488 Apr  6 14:35 core.25666
> pgdbg -text -core core.25666 ./myexec 

> module load gnu
> gfortran -O0 -g -Wall -fbounds-check -o myexec prog.f90
> ulimit -c unlimited
> ./myexec
> ls -lrt
  -rwxr-xr-x 1 aer0 cineca-staff   9652 Apr  6 14:34 myexec
  -rw------- 1 aer0 cineca-staff 319488 Apr  6 14:35 core.25555
> gdb ./myexec core.2555

  mpirun -np 4 valgrind (valgrind-options) myprog arg1 arg2

Totalview
Totalview is a parallel debugger with a practical GUI that assist users to debug their parallel code. It has functionalities like stopping and reprising a code mid-run, setting breakpoints, checking the value of variables anytime, browse between the different tasks and threads to see the different behaviours, memory check functions and so on. For information about how to run the debugger (by connecting the compute nodes to your display via RCM), type the command:

> module help totalview

Scalasca
Scalasca is a tool for profiling parallel scientific and engineering applications that make use of MPI and OpenMP.
Details how to use scalasca in
http://www.scalasca.org/software/scalasca-2.x/documentation.html

Profilers (gprof)

In software engineering, profiling is the investigation of a program's behaviour using information gathered as the program executes. The usual purpose of this analysis is to determine which sections of a program to optimize - to increase its overall speed, decrease its memory requirement or sometimes both.

A (code) profiler is a performance analysis tool that, most commonly, measures only the frequency and duration of function calls, but there are other specific types of profilers (e.g. memory profilers) in addition to more comprehensive profilers, capable of gathering extensive performance data.

gprof

The GNU profiler gprof is a useful tool for measuring the performance of a program. It records the number of calls to each function and the amount of time spent there, on a per-function basis. Functions which consume a large fraction of the run-time can be identified easily from the output of gprof. Efforts to speed up a program should concentrate first on those functions which dominate the total run-time.

gprof uses data collected by the -pg compiler flag to construct a text display of the functions within your application (call tree and CPU time spent in every subroutine). It also provides quick access to the profiled data, which let you identify the functions that are the most CPU-intensive. The text display also lets you manipulate the display in order to focus on the application's critical areas.

Usage:

>  gfortran -pg -O3 -o myexec myprog.f90
> ./myexec
> ls -ltr
   .......
   -rw-r--r-- 1 aer0 cineca-staff    506 Apr  6 15:33 gmon.out
> gprof myexec gmon.out

It is also possible to profile at code line-level (see "man gprof" for other options). In this case you must use also the “-g” flag at compilation time:

>  gfortran -pg -g -O3 -o myexec myprog.f90
> ./myexec
> ls -ltr
   .......
   -rw-r--r-- 1 aer0 cineca-staff    506 Apr  6 15:33 gmon.out
> gprof -annotated-source myexec gmon.out

Totalview
Totalview is a parallel debugger with a practical GUI that assist users to debug their parallel code. It has functionalities like stopping and reprising a code mid-run, setting breakpoints, checking the value of variables anytime, browse between the different tasks and threads to see the different behaviours, memory check functions and so on. For information about how to run the debugger (by connecting the compute nodes to your display via RCM), type the command:

> module help totalview

Scalasca
Scalasca is a tool for profiling parallel scientific and engineering applications that make use of MPI and OpenMP.
Details how to use scalasca in
http://www.scalasca.org/software/scalasca-2.x/documentation.html

Profilers

In software engineering, profiling is the investigation of a program's behaviour using information gathered as the program executes. The usual purpose of this analysis is to determine which sections of a program to optimize - to increase its overall speed, decrease its memory requirement or sometimes both.

A (code) profiler is a performance analysis tool that, most commonly, measures only the frequency and duration of function calls, but there are other specific types of profilers (e.g. memory profilers) in addition to more comprehensive profilers, capable of gathering extensive performance data.

It is possible to profile MPI programs. In this case, the environment variable GMON_OUT_PREFIX must be defined in order to allow to each task to write a different statistical file. Setting

export GMON_OUT_PREFIX=<name>

 once the run is finished each task will create a file with its process ID (PID) extension

<name>.$PID

Scientific libraries (MKL)

MKL

The Intel Math Kernel Library (Intel MKL) enables improving performance of scientific, engineering, and financial software that solves large computational problems. Intel MKL provides a set of linear algebra routines, fast Fourier transforms, as well as vectorized math and random number generation functions, all optimized for the latest Intel processors, including processors with multiple cores.

Intel MKL is thread-safe and extensively threaded using the OpenMP technology.

documentation Examples can be found by loading the mkl module and searching in the directory:

${MKLROOT}/../Documentation/en_US/mklexamples

To use the MKL in your code you to load the module, then to define includes and libraries at compile and linking time:

> module load mkl
> icc -I$MKL_INC -L$MKL_LIB  -lmkl_intel_lp64 -lmkl_core -lmkl_sequential

For more informations , please refer to the documentationIntel oneAPI Math Kernel Library Link Line Advisor.