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Since all the filesystems are based on IBM Spectrum Scale™ file system (formerly GPFS), the usual unix command "quota" is not working. Use the local command cindata to query for disk usage and quota ("cindata -h" for help):
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If a graphic session is desired we recommend to use the tool RCM (Remote Connection Manager). For additional information visit Remote Visualization section on our User Guide.
Programming environment
The programming environment of the M100 cluster 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 compilers on MARCONI with the command:
> module availableand checking the "compilers" section. The available compilers are:
- CUDA
- XL
- GNU
- PGI
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 the NVIDIA CUDA Parallel Computing Platform documentation.
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XL
The XL compiler family offers C, C++, and Fortran compilers designed for optimization and improvement of code generation, exploiting the inherent opportunities in Power Architecture.
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GNU compilers
The gnu compilers are always available but they are not the best optimizing compilers. You do not need to load the module for using them.
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 FortranPORTLAND Group (PGI)
Initialize the environment with the module command:
> module load profile/advanced
> module load pgiThe 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