Difference between revisions of "Parallel job submission"

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[[Sandbox Home | Home]]
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== Shared memory job (OpenMP) ==
 
== Shared memory job (OpenMP) ==
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<br>
 
<br>
 
[[Image:shared_memory_computing.png|300px]]
 
[[Image:shared_memory_computing.png|300px]]
 
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* In shared-memory parallel computing, multiple processors (or "threads") perform tasks independently but share a common global memory. If a processor modified an array in the memory, all other processors can see this update as well
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In shared-memory parallel computing, multiple processors (or "threads") perform tasks independently but share a common global memory. If a processor modified an array in the memory, all other processors can see this update as well
 
* A serial code can be parallelized by marking code sections, which should be run in parallel, with [https://www.openmp.org/ openMP directives].  
 
* A serial code can be parallelized by marking code sections, which should be run in parallel, with [https://www.openmp.org/ openMP directives].  
 
* An openMP code can run on a single compute node and use up to maximum number of processors in that compute node, e.g., 24, 36, or 128 processors.  
 
* An openMP code can run on a single compute node and use up to maximum number of processors in that compute node, e.g., 24, 36, or 128 processors.  
* To run an openMP code, define number of processors(threads) in $OMP_NUM_THREADS
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* To compile an openMP code:
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$ module load gcc/6.3.0
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$ gcc -o hello_omp -fopenmp hello_omp.c
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* To run an openMP code, first define number of processors(threads) in $OMP_NUM_THREADS
  
 
  $ export OMP_NUM_THREADS=8
 
  $ export OMP_NUM_THREADS=8
  $ bsub -n 8 ./program
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  $ bsub -n 8 ./hello_omp
 
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== Distributed memory job (MPI) ==
 
== Distributed memory job (MPI) ==
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<br>
 
<br>
[[Image:distributed_memory_computing.png|300px]]
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[[Image:distributed_memory_computing.png|350px]]
 
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* In distributed-memory parallel computing, multiple processors (or "cores") perform tasks independently while each processor possesses its own private memory resource. If a processor modify an array in its memory, this processor has to communicate to other processors so that the others see this update.
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In distributed-memory parallel computing, multiple processors (or "cores") perform tasks independently while each processor possesses its own private memory resource. If a processor modify an array in its memory, this processor has to communicate to other processors so that the others see this update.
 
* Massage Passing Interface (MPI) library implements distributed memory parallel computing which can be programmed in C, C++ and Fortran
 
* Massage Passing Interface (MPI) library implements distributed memory parallel computing which can be programmed in C, C++ and Fortran
 
* An MPI program can run on a single compute node as well as on multiple compute nodes
 
* An MPI program can run on a single compute node as well as on multiple compute nodes
* An MPI program must be launched using "mpirun"
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* To compile an MPI code, use MPI wrappers, e.g., mpicc for C code, mpif90 for Fortran code
 
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$ module load gcc/6.3.0 openmpi/4.0.2
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$ mpicc -o mpi_hello_world mpi_hello_world.c
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* To run an MPI program, launch the program with "mpirun"
 
  $ module load gcc/6.3.0 openmpi/4.0.2
 
  $ module load gcc/6.3.0 openmpi/4.0.2
  $ bsub -n 240 mpirun ./program
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  $ bsub -n 240 mpirun ./mpi_hello_world
 
 
 
 
 
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== Further readings ==
 
== Further readings ==
 
* [[Parallel computing]]
 
* [[Parallel computing]]
* [https://sis.id.ethz.ch/services/consultingtraining/mpi_openmp_course.html Four-day course in parallel programming with MPI/OpenMP]
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* [https://sis.id.ethz.ch/services/consultingtraining/mpi_openmp_course.html Four-day course in parallel programming with MPI/OpenMP at ETH]
  
 
== Helper ==
 
== Helper ==
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<td style="width: 35%; text-align:center">
 
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[[Sandbox Home | Home]]
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[[Main Page | Home]]
 
</td>
 
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Latest revision as of 09:25, 1 October 2021

< Submit a job

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Shared memory job (OpenMP)


Shared memory computing.png

In shared-memory parallel computing, multiple processors (or "threads") perform tasks independently but share a common global memory. If a processor modified an array in the memory, all other processors can see this update as well

  • A serial code can be parallelized by marking code sections, which should be run in parallel, with openMP directives.
  • An openMP code can run on a single compute node and use up to maximum number of processors in that compute node, e.g., 24, 36, or 128 processors.
  • To compile an openMP code:
$ module load gcc/6.3.0
$ gcc -o hello_omp -fopenmp hello_omp.c
  • To run an openMP code, first define number of processors(threads) in $OMP_NUM_THREADS
$ export OMP_NUM_THREADS=8
$ bsub -n 8 ./hello_omp

Distributed memory job (MPI)


Distributed memory computing.png

In distributed-memory parallel computing, multiple processors (or "cores") perform tasks independently while each processor possesses its own private memory resource. If a processor modify an array in its memory, this processor has to communicate to other processors so that the others see this update.

  • Massage Passing Interface (MPI) library implements distributed memory parallel computing which can be programmed in C, C++ and Fortran
  • An MPI program can run on a single compute node as well as on multiple compute nodes
  • To compile an MPI code, use MPI wrappers, e.g., mpicc for C code, mpif90 for Fortran code
$ module load gcc/6.3.0 openmpi/4.0.2
$ mpicc -o mpi_hello_world mpi_hello_world.c
  • To run an MPI program, launch the program with "mpirun"
$ module load gcc/6.3.0 openmpi/4.0.2
$ bsub -n 240 mpirun ./mpi_hello_world

Examples

Further readings

Helper


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