(updated: May 2018 )
In this page:
hostname: login.galileo.cineca.it
early availability: 05/03/2018
start of production: 12/03/2018
Model: IBM NeXtScale Architecture: Linux Infiniband Cluster Nodes: 360 Processors: 2 x 18-cores Intel Xeon E5-2697 v4 (Broadwell) at 2.30 GHz |  |
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The GALILEO supercomputer has been introduced first in January 2015 and it has been available to Italian public and industrial researchers until January 2018. It has been the the national Tier-1 system for scientific research.
Starting from January 2018 GALILEO has been reconfigured with Intel Xeon E5-2697 v4 (Broadwell) nodes, inherited from MARCONI system.
Starting from March 2018 GALILEO is again in production, available for italian research community.
System Architecture
Compute Nodes: There are 360 36-core compute nodes. Each one contains 2 18-cores Intel Xeon E5-2697 v4 (Broadwell) at 2.30 GHz. All the compute nodes have 128 GB of memory. 15 of these compute nodes are equipped with 2 nVidia K80 GPU.
Login and Service nodes: 8 Login & Viz node are available, equipped with 2 nVidia K40 GPU each. 8 service nodes for I/O and cluster management.
All the nodes are interconnected through a Infiniband network, with OFED v1.5.3, capable of a maximum bandwidth of 40Gbit/s between each pair of nodes.
Accounting
For accounting information please consult our dedicated section.
Budget Linearization policy
On GALILEO 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 of 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.
| Total Dimension (TB) | Quota (GB) | Notes | |
|---|---|---|---|
| $HOME | 200 | 50 |
|
| $CINECA_SCRATCH | 2.500 | no quota |
|
| $WORK | 7.100 | 1.000 |
|
$DRES points to the shared repository where Data RESources are maintained. This is a data archive area availble 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
As usual, the software modules are collected in different profiles and organized by functional category (compilers, libraries, tools, applications,..).
On GALILEO a new feature has been added to the module environment: 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 others 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 use the following command:
>module list
In order to detect all profiles, categories and modules available on GALILEO the command “modmap” is available:
>modmap
We allow you to load old-Galileo environment modules.
To switch from new environmengt (the default one) to the old and vice-versa, you can load the superc module
>module load autoload superc
and run one of the following commands:
FOR BASH USERS
. $SUPERC_SWITCH/switch_to_old.sh
to switch from new ro old environment, or
. $SUPERC_SWITCH/switch_to_new.sh
to switch from old to new environment.
FOR CSH/TCSH USERS:
source $SUPERC_SWITCH/switch_to_old.csh
to switch from new ro old environment, or
source $SUPERC_SWITCH/switch_to_new.csh
to switch from old to new environment.
Production environment
Since GALILEO 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 the 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 and Tools".
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 has a 10 minutes time limit: for longer runs please use the "batch" mode.
A parallel program can be executed interactively only within 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, two processors, launch the command:
> srun -N1 -n2 --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.
When the shell come back with the prompt, you can execute your program by typing:
> mpirun ./myprogram
or
> srun ./myprogram
The default SLURM MPI type has been set equal to PMI2.
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. specific library paths or options), you can prepare the environment in the origin shell being sure to find it in the interactive shell.
Batch
As usual 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 -a
For more information and examples of job scripts, see section Batch Scheduler SLURM.
Submitting serial Batch jobs
The gll_all_serial partition is available with one core and a maximum walltime of 4 hours. It runs on the login 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 4 tasks 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 RCM tool or the EnginFrame environment. Both of them are under construction, we will report here all required information asap.
Submitting parallel Batch jobs
To run parallel batch jobs on GALILEO you need to specify the partition gll_usr_prod, or any other partition we invited you to use.
Users who need to run on GPU-equipped nodes need to specify che partition gll_usr_gpuprod.
If you do not specify the partition, your jobs will try to run on the bdw_all_serial partition, eventually failing if specific partition limits (maximum one core for maximum walltime of 4 hours) are violated.
The minimum number of cores to require is 1. The maximum number of cores that you can request is the 2304 (about 167 nodes) with a maximum walltime of 24 hours:
- 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 (117000 MB for gpu nodes)
Example of batch script to submit a batch job
The special QOS (bdw_qos_special) is designed for not-ordinary types of jobs, and users need to be enabled in order to use it. Please write to superc@cineca.it in case you think you need to use it.
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 | HBM/clustering mode | notes |
gll_all_serial (default partition) | gll_all_serial | 1 | 04:00:00 | Max 12 running jobs Max 4 jobs/user | 3000 | |||
| gll_usr_prod | noQOS | min = 1 max = 2304 | 24:00:00 | 20/2304 | 118000 | |||
| gll_usr_gpuprod | noQOS | min = 1 max = 64 | 08:00:00 | 4 | 117000 | --gres=gpu:kepler:N (N=1,4) | ||
| gll_spc_prod | Every account needs to have a valid QOS to access this partition | Depending on kind of users | 24:00:00 | / | 118000 | Partition dedicated to specific kind of users. | ||
| gll_meteo_prod | Partition reserved to meteo services, NOT opened to production |
PLEASE NOTE: the SLURM characteristics have not completely defined for GALILEO, some changes will be possible.
Use of GPUs on GALILEO
gll_usr_gpuprod partiton is defined on 14 Haswell nodes (2*8-cores Intel Xeon E5-2630 v3 @ 2.40GHz), each equipped with + 2 nVidia K80 GPUs. All users using an account with positive budget can launch jobs on this partition.
The maximum number of nodes that can be require on gll_usr_gpuprod is 4, for a maximum walltime of 08:00:00 hours. The maximum memory is 117000 MB.
You need to request the GPU as gres:
#SBATCH --partition=gll_usr_gpuprod
#SBATCH --gres=gpu:kepler:N (N=1,4)
Galileo is also equipped of a node with two nVIDIA Volta (V100) GPUs, 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 enabled to use these resources (via the association to the QOS gll_qos_gpudev) you can submit jobs to the node with the following options:
#SBATCH --partition=gll_usr_gpudev
#SBATCH --qos=gll_qos_gpudev
#SBATCH --gres=gpu:volta:N (N=1,2)
Users with reserved resources
Users of projects that require reserved resources will be associated to a QOS.
Using the specific QOS (i.e. specifying the QOS in the submission script) , an specifying the partition gll_spc_prod partition, 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>
Programming environment
The programming environment of the GALILEO machine consists of a choice of compilers for the main scientific languages (Fortran, C and C++), debuggers to help users in finding bugs and errors in the codes, profilers to help in code optimisation. In general you must "load" the correct environment also for using programming tools like compilers, since "native" compilers are not available.
If you use a given set of compilers and libraries to create your executable, very probably you have to define the same "environment" when you want to run it. This is because, since by default linking is dynamic on Linux systems, at runtime the application will need the compiler shared libraries as well as other proprietary libraries. This means that you have to specify "module load" for compilers and libraries, both at compile time and at run time. To minimize the number of needed modules at runtime, use static linking to compile the applications.
Compilers
You can check the complete list of available compiler on GALILEO with the command:
> module available
and 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
- 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 specifics. The most common are reported later for each compiler.
- 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
- 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.
- If you want to compile your code for normal production you have to turn on optimisation by choosing a higher optimisation level
-O2 or -O3 Higher optimisation levels
Other flags are available for specific compilers and are reported later.
INTEL Compiler
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 miscellanous 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 interprocedural optimization for single-file compilation -ipo Enables interprocedural optimization between files - whole program optimisation
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 miscellanous flags are described in the following:
-Mextend To extend over the 77 column F77's limit -Mfree / -Mfixed Free/Fixed form for Fortran -fast Chooses generally optimal flags for the target platform -fastsse Chooses generally optimal flags for a processor that supports SSE instructions
GNU compilers
The gnu compilers are always available but they are not the best optimizing compilers. The default version is 4.8.2, you do not need to load the module for using them.
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 miscellanous 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
Hybrid programming (Intel PHI)
Some of the nodes of the system are equipped with 2 accelerators per node. They can be addressed within C or Fortran programs by means of the Intel compilers suite.
You can find a brief guide on the production and programming environment for the Intel Xeon Phi (MIC) nodes in a separate document here.
Debuggers and Profilers
If at runtime your code dies, then there is a problem. In order to solve it, you can decide to analyze the core file (core not available with PGI compilers) or to run your code using the debugger.
Compiler flags
Whatever your decision, in any case you need 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 check before because the flags may differ.
The following flags are generally available for all compilers and are mandatory for an easier debuggin 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 usefull (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 usefull (in addition to "-O0 -g")for debugging your code:
-C Add array bounds checking
-Ktrap=ovf,divz,inv Controls the behavior of the processor when exceptions occur:
FP overflow, divide by zero, invalid operands
GNU Fortran compilers
The following flags are usefull (in addition to "-O0 -g")for debugging your code:
-Wall Enables warnings pertaining to usage that should be avoided -fbounds-check Checks for array subscripts.
Debuggers available (Totalview, Scalasca, TAU)
We plan to make available on GALILeO the three applications riported above in a short time. Detailed information will be published asap.
In the following we report information about other ways to debug your codes:
PGI: pgdbg (serial/parallel debugger)
pgdbg is the Portland Group Inc. symbolic source-level debugger for F77, F90, C, C++ and assembly language programs. It is capable of debugging applications that exhibit various levels of parallelism, including:
- Single-thread, serial applications
- Multi-threaded applications
- Distributed MPI applications
- Any combination of the above
There are two forms of the command used to invoke pgdbg. The first is used when debugging non-MPI applications, the second form, using mpirun, is used when debugging MPI applications:
> pgdbg [options] ./myexec [args] > mpirun [options] -dbg=pgdbg ./myexec [args]
More details in the on line documentation, using the "man pgdbg" command after loading the module.
To use this debugger, you should compile your code with one of the pgi compilers and the debugging command-line options described above, then you run your executable inside the "pgdbg" environment:
> module load profile/advanced
> module load pgi > pgf90 -O0 -g -C -Ktrap=ovf,divz,inv -o myexec myprog.f90 > pgdbg ./myexec
By default, pgdbg presents a graphical user interface (GUI). A command-line interface is also provided though the "-text" option.
GNU: gdb (serial debugger)
GDB is the GNU Project debugger and allows you to see what is going on 'inside' your program while it executes -- or what the program was doing at the moment it crashed.
GDB can do four main kinds of things (plus other things in support of these) to help you catch bugs in the act:
- Start your program, specifying anything that might affect its behavior.
- Make your program stop on specified conditions.
- Examine what has happened, when your program has stopped.
- Change things in your program, so you can experiment with correcting the effects of one bug and go on to learn about another.
More details in the on line documentation, using the "man gdb" command.
To use this debugger, you should compile your code with one of the gnu compilers and the debugging command-line options described above, then you run your executable inside the "gdb" environment:
> module load gnu
> gfortran -O0 -g -Wall -fbounds-check -o myexec myprog.f90
> gdb ./myexec
VALGRIND
Valgrind is a framework for building dynamic analysis tools. There are Valgrind tools that can automatically detect many memory management and threading bugs, and profile your programs in detail. The Valgrind distribution currently includes six production-quality tools: a memory error detector, two thread error detectors, a cache and branch-prediction profiler, a call-graph generating cache profiler, and a heap profiler.
Valgrind is Open Source / Free Software, and is freely available under the GNU General Public License, version 2.
To analyse a serial application:
- Load Valgrind module --> module load valgrind
- Load module for the compiler and compile your code with the compiler you prefer (Use -O0 -g flags)
- Run the executable under Valgrind.
If you normally run your program like this:
myprog arg1 arg2
Use this command line:
valgrind (valgrind-options) myprog arg1 arg2
Memcheck is the default tool. You can add the --leak-ceck option that turns on the detailed memory leak detector. Your program will run much slower than normal, and use a lot more memory. Memcheck will issue messages about memory errors and leaks that it detects.
- Load Valgrind module --> module load valgrind
- Load modules for compiler and openmpi libraries (at present only available for intel and gnu)
- Compile your code with the "-O0 -g" flags both at compiling and linking time
- Run the executable under Valgrind (both in interactive than in bacth mode)
mpirun -np 4 valgrind (valgrind-options) myprog arg1 arg2
Core file analysis
In order to understand what problem was affecting you code, you can also try a "Core file" analysis. Since core files are usually quite large, be sure to work in the /scratch area.
There are several steps to follow:
- Increase the limit for possible core dumping
> ulimit -c unlimited (bash) > limit coredumpsize unlimited (csh/tcsh)
- If you are using Intel compilers, set to TRUE the decfort_dump_flag environment variable
> export decfort_dump_flag=TRUE (bash) > setenv decfort_dump_flag TRUE (csh/tcsh)
- Compile your code with the debug flags described above.
- Run your code and create the core file.
- Analyze the core file using different tools depending on the original compiler.
INTEL compilers
> 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
PGI compilers
> 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
GNU Compilers
> 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
Profilers (gprof)
In software engineering, profiling is the investigation of a program's behavior 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
It is possilbe 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
If the environmental variable is not set every task will write the same gmon.out file.
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 can be found in:
${MKLROOT}/../Documentation/en_US/mkl
To use the MKL in your code you to load the module, then to define includes and libraries at compile and linking time:
> module load mkl > icc -I$MKL_INC -L$MKL_LIB -lmkl_intel_lp64 -lmkl_core -lmkl_sequential
For more inormation please refer to the documentation.
Parallel programming
The parallel programming on Galileo is based on IntelMPI and OpenMPI versions of MPI. The libraries and special wrappers to compile and link the personal programs are contained in several modules, one for each supported suite of compilers.
The main four parallel-MPI commands for compilation are:
- mpif90 (Fortran90)
- mpif77 (Fortran77)
- mpicc (C)
- mpiCC (C++)
These command names refers to wrappers around the actual compilers, they behave differently depending on the module you have loaded.
> module avail intelmpi intelmpi/2017--binary > module load autoload intelmpi/2017--binary > man mpif90 > mpif90 -o myexec myprof.f90 (uses the ifort compiler)
> module avail openmpi openmpi/2.1.1--gnu--6.1.0
> module load autoload openmpi/2.1.1--gnu--6.1.0
> man mpif90
> mpif90 -o myexec myprof.f90 (uses the gfortran compiler)
In all cases the parallel applications have to be executed with the command:
> mpirun ./myexec
There are limitations on running parallel programs in the login shell. You should use the "Interactive SLURM" mode, as described in the "Interactive" section, previously in this page.
Outgoing links: