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Libraries (Hunter)

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Libraries (Hunter)

On Hunter it is strongly recommended to use optimized libraries whenever possible.

Numerical Libraries

TBD

I/O Libraries

For parallel I/O three libraries are provided along with the Cray Programming environment

  • Cray HDF5
  • Cray NetCDF
  • Cray parallel Netcdf

HDF5

On Hunter the Cray HDF5 version of the Hierarchical Data Format (HDF) is installed. Latest release notes can be found at https://cpe.ext.hpe.com/docs/latest/csml/cray_hdf5.html.

To use the HDF5 library and corresponding tools load the following module: 

module load cray-hdf5-parallel

Make sure the include- and library-paths are provided to the compiler and linker. Within the Cray programming Environment this can be done by providing the -l and -I options on the command line.

For Fortran use: 

<compiler_wrapper> -L${NETCDF_DIR}/lib/ -I${NETCDF_DIR}/include -lhdf5 -lhdf5_fortran <your_sources>

Fortran Example

Here we provide a very simple example for the usage of HDF5 in Fortran. 

program hdf5_io

  implicit none

  use mpi
  use hdf5
  
  ! Init kind parameters ------------------------------------------------------
  Integer, Parameter                :: ik=4, rk=8
  ! Some data -----------------------------------------------------------------
  Real(kind=rk), Target             :: Data_out(4, 8)
  ! Variables for netcdf ------------------------------------------------------
  Integer(kind=ik)                  :: ncid
  Integer(kind=ik)                  :: xDimID
  Integer(kind=ik)                  :: yDimID
  Integer(kind=ik)                  :: varID
  Integer(kind=ik)                  :: DimIDs(2)
  Integer(kind=ik)                  :: ncError
  Integer(kind=ik)                  :: Start(2)
  ! Variables for mpi ---------------------------------------------------------
  Integer(kind=ik)                  :: mpiError,rank
  ! Variables for hdf5-------------------------------------------------------
  integer(kind=ik)                  :: h5error
  integer(HID_T)                    :: plist_id
  integer(kind=4)                   :: info = MPI_INFO_NULL
  integer(HID_T)                    :: file_id
  integer(kind=ik)                  :: dataset_rank=2
  integer(HID_T)                    :: filespace
  integer(HID_T)                    :: memspace
  integer(HID_T)                    :: dset_id
  character(len=chl)                :: dataset_name = 'TestDataSet'
  type(C_PTR)                       :: buffer
  integer(HID_T)                    :: dsetparams
  real(kind=rk)                     :: t_start =0.0_8

  ! MPI init ------------------------------------------------------------------
  Call mpi_init(mpiError)
  Call mpi_comm_rank(MPI_COMM_WORLD,rank,mpiError)

  ! Tell who's there and init data --------------------------------------------
  Write(*,*)"Rank",rank,"netcdf_io ... "
  
  data_out = Real(rank)*0.5_rk
  ! Init hdf5 -----------------------------------------------------------------
  call h5open_f(h5error)
  ! Set hdf5 propeties --------------------------------------------------------
  call h5pcreate_f(H5P_FILE_ACCESS_F, plist_id, h5error)
  call h5pset_fapl_mpio_f(plist_id, MPI_COMM_WORLD, info, h5error)
  ! Create hdf5 file ----------------------------------------------------------
  call h5fcreate_f("hdf5_io.h5", H5F_ACC_TRUNC_F, file_id, h5error, access_prp=plist_id)
  call h5pclose_f(plist_id, h5error)
  ! Create hdf5 dataspace and dataset -----------------------------------------
  CALL H5PCREATE_F(H5P_DATASET_CREATE_F,dsetparams,h5error)
  call h5screate_simple_f(dataset_rank, GlobalGridPoints, filespace, h5error)
  call h5dcreate_f(file_id, trim(dataset_name), H5T_NATIVE_DOUBLE, filespace, dset_id, h5error, dsetparams)
  call h5sclose_f(filespace, h5error)
  call h5screate_simple_f(dataset_rank, LocalGridPoints, memspace, h5error)
  call h5dget_space_f(dset_id, filespace, h5error)
  call h5sselect_hyperslab_f(filespace, H5S_SELECT_SET_F, Offset, LocalGridPoints, h5error)
  ! Set dataset properties ----------------------------------------------------
  call h5pcreate_f(H5P_DATASET_XFER_F, plist_id, h5error)
  call h5pset_dxpl_mpio_f(plist_id, H5FD_MPIO_COLLECTIVE_F, h5error)

  ! Write data ----------------------------------------------------------------
  buffer = C_LOC(Data_out)
  call h5dwrite_f(dset_id, H5T_NATIVE_DOUBLE, buffer, h5error, &
       file_space_id=filespace, mem_space_id=memspace, xfer_prp=plist_id)

  ! Close hdf5 objects and file -----------------------------------------------
  call h5sclose_f(filespace, h5error)
  call h5sclose_f(memspace, h5error)
  call h5dclose_f(dset_id, h5error)
  call h5pclose_f(plist_id, h5error)
  call h5fclose_f(file_id, h5error)
  call h5close_f(h5error)

  ! Goodbye and finalize ------------------------------------------------------
  Write(*,*)"Rank",rank,"done."
  
  Call mpi_finalize(mpiError)
  
end program hdf5_io

To build an executable of the following example use the programming environment of your choise, in our example we stick to the default one. 

#!/bin/bash
ftn -L${HDF5_ROOT}/lib/ -I ${HDF5_ROOT}/include -lhdf5 -lhdf5_fortran hdf5_io.f90 -o hdf5_io

The demo can be executed by submitting the following PBS script batch script can be used. 

#!/bin/bash
#PBS -N Test_NetCDF
#PBS -l select=1:node_type=mi300a:mpiprocs=4
#PBS -l walltime=00:01:00
#PBS -q test 

cd $PBS_O_WORKDIR

module load cray-hdf5-parallel/1.14.3.1

mpiexec -n 4 --ppn 4 ./hdf5_io

As can be seen below, four mpi-ranks produce a single netcdf file with each rank writing a rectangular chunk in x-direction of the contained 2D-datafield. 



The result output can be generated from the written file hdf5_io.h5 with the h5dump tool.



h5dump






NetCDF


On Hunter the Cray NetCDF version of the Network Common Data Form (NetCDF) library is installed. Latest release notes can be found at https://cpe.ext.hpe.com/docs/latest/csml/cray_parallel_netcdf.html.

Since Cray NetCDF uses HDF5 as a parallel backend one has to load the following modules to use the library.



module load cray-hdf5-parallel
module load cray-netcdf-hdf5parallel



Make sure the include- and library-paths are provided to the compiler and linker. Within the Cray programming Environment this can be done by providing the -l and -I options on the command line.

For Fortran use:



<compiler_wrapper> -L${NETCDF_DIR}/lib/ -I${NETCDF_DIR}/include -lnetcdf -lnetcdff <your_sources>






Fortran Example


Here we provide a very simple example for the usage of NetCDF in Fortran.



Program netcdf_io

  ! Use mpi and netcdf --------------------------------------------------------
  Use mpi
  Use netcdf

  Implicit None

  ! Init kind parameters ------------------------------------------------------
  Integer, Parameter                :: ik=4, rk=8
  ! Some data -----------------------------------------------------------------
  Real(kind=rk), Target             :: Data_out(4, 8)
  ! Variables for netcdf ------------------------------------------------------
  Integer(kind=ik)                  :: ncid
  Integer(kind=ik)                  :: xDimID
  Integer(kind=ik)                  :: yDimID
  Integer(kind=ik)                  :: varID
  Integer(kind=ik)                  :: DimIDs(2)
  Integer(kind=ik)                  :: ncError
  Integer(kind=ik)                  :: Start(2)
  ! Variables for mpi ---------------------------------------------------------
  Integer(kind=ik)                  :: mpiError,rank

  ! MPI init ------------------------------------------------------------------
  Call mpi_init(mpiError)
  Call mpi_comm_rank(MPI_COMM_WORLD,rank,mpiError)

  ! Tell who's there and init data --------------------------------------------
  Write(*,*)"Rank",rank,"netcdf_io ... "
  
  data_out = Real(rank)*0.5_rk

  ! Create a netcdf file ------------------------------------------------------
  ncError = nf90_create( "netcdf_io.nc", &
       Ior(NF90_NETCDF4, NF90_MPIIO), &
       ncid, comm=MPI_COMM_WORLD, info=MPI_INFO_NULL)

  ! Define global dimensions --------------------------------------------------
  ncError = nf90_def_dim(ncid, "x", Int(16,4), xDimID)
  ncError = nf90_def_dim(ncid, "y", Int( 8,4), yDimID)

  DimIDs = (/xDimID, yDimID/)

  ! Define a netcdf variable --------------------------------------------------
  ncError = nf90_def_var(ncid, "data", NF90_DOUBLE, DimIDs, varID, &
       chunksizes=(/4_4,8_4/) )

  ! Finalize netcdf definitions -----------------------------------------------
  ncError = nf90_enddef(ncid)

  ! Set local offset per rank -------------------------------------------------
  Start = (/ rank*4_4 , 0_4 /) + 1_4

  ! Write data ----------------------------------------------------------------
  ncError = nf90_put_var(ncid, varID, Data_out, start=Start, count=(/4_4,8_4/))

  ! Close file ----------------------------------------------------------------
  ncError = nf90_close(ncid)

  ! Goodbye and finalize ------------------------------------------------------
  Write(*,*)"Rank",rank,"done."

  Call mpi_finalize(mpiError)

End Program netcdf_io



To build an executable of the following example use the programming environment of your choise, in our example we stick to the default one.



#!/bin/bash
ftn -L${NETCDF_DIR}/lib/ -I${NETCDF_DIR}/include -lnetcdf -lnetcdff netcdf_io.f90 -o netcdf_io



The demo can be executed by submitting the following PBS script batch script can be used.



#!/bin/bash
#PBS -N Test_NetCDF
#PBS -l select=1:node_type=mi300a:mpiprocs=4
#PBS -l walltime=00:01:00
#PBS -q test 

cd $PBS_O_WORKDIR

module load cray-hdf5-parallel/1.14.3.1
module load cray-netcdf-hdf5parallel/4.9.0.13

mpiexec -n 4 --ppn 4 ./netcdf_io

ncdump netcdf_io.nc > netcdf_io.nc.dump



As can be seen below, four mpi-ranks produce a single netcdf file with each rank writing a rectangular chunk in x-direction of the contained 2D-datafield.



netcdf netcdf_io {
dimensions:
    x = 16 ;
    y = 8 ;
variables:
    double data(y, x) ;
data:

 data =
  0, 0, 0, 0, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1.5, 1.5, 1.5, 1.5,
  0, 0, 0, 0, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1.5, 1.5, 1.5, 1.5,
  0, 0, 0, 0, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1.5, 1.5, 1.5, 1.5,
  0, 0, 0, 0, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1.5, 1.5, 1.5, 1.5,
  0, 0, 0, 0, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1.5, 1.5, 1.5, 1.5,
  0, 0, 0, 0, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1.5, 1.5, 1.5, 1.5,
  0, 0, 0, 0, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1.5, 1.5, 1.5, 1.5,
  0, 0, 0, 0, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1.5, 1.5, 1.5, 1.5 ;
}



The result output can be generated from the generated file netcdf.demo.nc with the ncdump tool.



ncdump netcdf_io.nc









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