HDF5 table
Storing tables in HDF5
The HDF5 table interface condenses the steps needed to create tables in HDF5. The datatype of the dataset that gets created is of type H5T_COMPOUND. The members of the table can have different datatypes.
Writting a table using Python (PyTables)
PyTables is a package for managing hierarchical datasets and designed to efficiently and easily cope with extremely large amounts of data. PyTables is built on top of the HDF5 library, using the Python language and the NumPy package. The following example shows how to store a table of 10 records with 8 members :
| Template:Hl2 | name | Template:Hl2 | ADCcount | Template:Hl2 | grid_i | Template:Hl2 | grid_j | Template:Hl2 | pressure | Template:Hl2 | energy | Template:Hl2 | idnumber | Template:Hl2 | pressure2 |
|---|---|---|---|---|---|---|---|
| 16-character String | Unsigned short integer | 32-bit integer | 32-bit integer | float (single-precision) | double (double-precision) | Signed 64-bit integer | 2-dim table of float (2*3) |
The script has been run on gpc with the following modules :
module load gcc/4.8.1 intel/14.0.0 python/2.7.2 hdf5/1811-v18-serial-gcc
PyTable 3.0.0 has been compiled in my scratch directory.
from tables import *
class Particle(IsDescription):
name = StringCol(16) # 16-character String
ADCcount = UInt16Col() # Unsigned short integer
grid_i = Int32Col() # 32-bit integer
grid_j = Int32Col() # 32-bit integer
pressure = Float32Col() # float (single-precision)
energy = Float64Col() # double (double-precision)
idnumber = Int64Col() # Signed 64-bit integer
pressure2 = Float32Col(shape=(2,3)) # array of floats (single-precision)
h5file = open_file("tutorial1.h5", mode = "w", title = "Test file")
group = h5file.create_group("/", 'detector', 'Detector information')
table = h5file.create_table(group, 'readout', Particle, "Readout example")
particle = table.row
for i in xrange(10):
particle['name'] = 'Particle: %6d' % (i)
particle['ADCcount'] = (i * 256) % (1 << 16)
particle['grid_i'] = i
particle['grid_j'] = 10 - i
particle['pressure'] = float(i*i)
particle['energy'] = float(particle['pressure'] ** 4)
particle['idnumber'] = i * (2 ** 34)
particle['pressure2'] = [
[0.5+float(i),1.5+float(i),2.5+float(i)],
[-1.5+float(i),-2.5+float(i),-3.5+float(i)]]
# Insert a new particle record
particle.append()
h5file.close()
Reading the table with a C++ code with MPI for parallel programming
The following example shows how to read the table in a MPI process (each MPI process will read one individual record). The code has been compiled and tested on BlueGene with the following modules :
module load vacpp/12.1 xlf/14.1 mpich2/xl hdf5/189-v18-mpich2-xlc mpixlcxx -I$SCINET_HDF5_INC -L$SCINET_ZLIB_LIB -L$SCINET_SZIP_LIB -L$SCINET_HDF5_LIB Test.cpp -o Test -lhdf5_hl -lhdf5 -lsz -lz
Test.cpp (the order of the variables (alphabetical) is important. The c++ code has to read the variables in the same order as in the hdf5 file : check with h5dump YourFile.h5 ) :
#include "hdf5.h"
#include "hdf5_hl.h"
#include <stdlib.h>
#include <iostream>
#include <stdint.h>
#include <mpi.h>
#define NFIELDS (hsize_t) 8
#define H5FILE_NAME "tutorial1.h5"
int main(int argc, char *argv[])
{
// DEF OF SIZE OF VARIABLES TO READ
typedef struct Particle
{
unsigned short int ADCcount;
double energy;
int grid_i;
int grid_j;
long idnumber;
char name[16];
float pressure;
float pressure2[2][3];
} Particle;
/* Calculate the size and the offsets of our struct members in memory */
size_t dst_size = sizeof( Particle );
size_t dst_offset[NFIELDS] = {
HOFFSET( Particle, ADCcount ),
HOFFSET( Particle, energy ),
HOFFSET( Particle, grid_i ),
HOFFSET( Particle, grid_j ),
HOFFSET( Particle, idnumber ),
HOFFSET( Particle, name ),
HOFFSET( Particle, pressure ),
HOFFSET( Particle, pressure2),
};
//////////////////////////////////////////////////////////////////////////////////////////////////////////////
//MPI
//HDF5 APIs definitions
hid_t file_id; /* file and dataset identifiers */
hid_t plist_id; /* property list identifier( access template) */
herr_t status;
// MPI variables
int mpi_size, mpi_rank;
MPI_Comm comm = MPI_COMM_WORLD;
MPI_Info info = MPI_INFO_NULL;
//Initialize MPI
MPI_Init(&argc, &argv);
MPI_Comm_size(comm, &mpi_size);
MPI_Comm_rank(comm, &mpi_rank);
// Set up file access property list with parallel I/O access
plist_id = H5Pcreate(H5P_FILE_ACCESS);//creates a new property list as an instance of some property list class
H5Pset_fapl_mpio(plist_id, comm, info);
// Read file collectively.
file_id = H5Fopen(H5FILE_NAME, H5F_ACC_RDONLY, plist_id);//H5F_ACC_RDONLY : read-only mode
Particle dst_buf[1];
size_t dst_sizes[NFIELDS] = {
sizeof( dst_buf[0].ADCcount),
sizeof( dst_buf[0].energy),
sizeof( dst_buf[0].grid_i),
sizeof( dst_buf[0].grid_j),
sizeof( dst_buf[0].idnumber),
sizeof( dst_buf[0].name),
sizeof( dst_buf[0].pressure),
sizeof( dst_buf[0].pressure2)
};
//READ FRACTION OF TABLE : example reading one record per MPI process
hsize_t start=mpi_rank;//read Record number mpi_rank
hsize_t nrecords=1;//read 1 record
status=H5TBread_records(file_id,"/detector/readout",start,nrecords,dst_size,dst_offset,dst_sizes,dst_buf);
std::cout<<"Rank = "<<mpi_rank
<<" ,ADCcount = "<<dst_buf[0].ADCcount
<<" ,idnumber = "<<dst_buf[0].idnumber
<<" ,grid_i = "<<dst_buf[0].grid_i
<<" ,grid_j = "<<dst_buf[0].grid_j
<<" ,pressure = "<<dst_buf[0].pressure
<<" ,name = "<<dst_buf[0].name
<<" ,energy = "<<dst_buf[0].energy
<<std::endl;
for(int j=0;j<2;j++){
std::cout<<"Rank = "<<mpi_rank<<" : "<<dst_buf[0].pressure2[j][0]<<" "<<dst_buf[0].pressure2[j][1]<<" "<<dst_buf[0].pressure2[j][2]<<std::endl;
}
//Close property list.
H5Pclose(plist_id);
// Close the file.
H5Fclose(file_id);
MPI_Finalize();
return 0;
}
The job has been launched on BlueGene :
runjob --np 10 --ranks-per-node=1 --envs OMP_NUM_THREADS=1 : /PATH_TO_THE_TEST_DIRECTORY/Test
The output of the job :
Rank = 9 ,ADCcount = 2304 ,idnumber = 154618822656 ,grid_i = 9 ,grid_j = 1 ,pressure = 81 ,name = Particle: 9 ,energy = 4.30467e+07 Rank = 9 : 9.5 10.5 11.5 Rank = 9 : 7.5 6.5 5.5 Rank = 8 ,ADCcount = 2048 ,idnumber = 137438953472 ,grid_i = 8 ,grid_j = 2 ,pressure = 64 ,name = Particle: 8 ,energy = 1.67772e+07 Rank = 8 : 8.5 9.5 10.5 Rank = 8 : 6.5 5.5 4.5 Rank = 2 ,ADCcount = 512 ,idnumber = 34359738368 ,grid_i = 2 ,grid_j = 8 ,pressure = 4 ,name = Particle: 2 ,energy = 256 Rank = 2 : 2.5 3.5 4.5 Rank = 2 : 0.5 -0.5 -1.5 Rank = 7 ,ADCcount = 1792 ,idnumber = 120259084288 ,grid_i = 7 ,grid_j = 3 ,pressure = 49 ,name = Particle: 7 ,energy = 5.7648e+06 Rank = 7 : 7.5 8.5 9.5 Rank = 7 : 5.5 4.5 3.5 Rank = 4 ,ADCcount = 1024 ,idnumber = 68719476736 ,grid_i = 4 ,grid_j = 6 ,pressure = 16 ,name = Particle: 4 ,energy = 65536 Rank = 4 : 4.5 5.5 6.5 Rank = 4 : 2.5 1.5 0.5 Rank = 5 ,ADCcount = 1280 ,idnumber = 85899345920 ,grid_i = 5 ,grid_j = 5 ,pressure = 25 ,name = Particle: 5 ,energy = 390625 Rank = 5 : 5.5 6.5 7.5 Rank = 5 : 3.5 2.5 1.5 Rank = 6 ,ADCcount = 1536 ,idnumber = 103079215104 ,grid_i = 6 ,grid_j = 4 ,pressure = 36 ,name = Particle: 6 ,energy = 1.67962e+06 Rank = 6 : 6.5 7.5 8.5 Rank = 6 : 4.5 3.5 2.5 Rank = 0 ,ADCcount = 0 ,idnumber = 0 ,grid_i = 0 ,grid_j = 10 ,pressure = 0 ,name = Particle: 0 ,energy = 0 Rank = 0 : 0.5 1.5 2.5 Rank = 0 : -1.5 -2.5 -3.5 Rank = 1 ,ADCcount = 256 ,idnumber = 17179869184 ,grid_i = 1 ,grid_j = 9 ,pressure = 1 ,name = Particle: 1 ,energy = 1 Rank = 1 : 1.5 2.5 3.5 Rank = 1 : -0.5 -1.5 -2.5 Rank = 3 ,ADCcount = 768 ,idnumber = 51539607552 ,grid_i = 3 ,grid_j = 7 ,pressure = 9 ,name = Particle: 3 ,energy = 6561 Rank = 3 : 3.5 4.5 5.5 Rank = 3 : 1.5 0.5 -0.5