/ph5concat

Parallel Data Concatenation for High Energy Physics Data Analysis

Primary LanguageC++OtherNOASSERTION

Parallel HDF5 Dataset Concatenation for High Energy Physics Data Analysis

MPICH

This software package contains C++ programs for concatenating HDF5 datasets across multiple files into a single file by appending individual datasets one after another. In a typical neutrino particle collision experiment, the detector collects data into files over a period of time. It is a common practice to see each file is labeled in its file name by IDs of 'run' and 'subrun'. Runs are divided into subruns. A run can be, for example, one day of data taking and a subrun can be about an hour of data taking. Each file may contains thousands of two-dimensional HDF5 datasets, organized into hundreds of HDF5 group, containing data describing the properties of a given particle type. To analyze the data, individual datasets are required to be concatenated one after another across all files, preferably in an increasing order of their run and subrun IDs. As the data amount and number of files from a given experiment can become very large, the performance scalability of such parallel data concatenation is important.

Requirements for Input HDF5 Files

  • Each input file may contain multiple HDF5 group objects, but the number of groups and group names must be the same among all input files.
  • Each group may contain multiple HDF5 dataset objects. The number of datasets in a group can be different from other groups. For a given group, the number of datasets and their names must be the same among the same groups across all input files.
  • All datasets must be defined as 2D arrays. When the 2nd dimension size of a dataset is 1, they are referred to as 1D dataset in this README file.
  • All datasets in the same group of the same input file must share the size of their 1st (most significant) dimension. Their 2nd dimension sizes may be different.
  • The 1st dimension size of datasets in different groups may be different.
  • Datasets can be of size zero where their 1st dimension is of size 0.
  • All the files have the same "schema", i.e. same structures of groups and datasets in groups, e.g. numbers of groups, and number of datasets in each group, and their names.
  • The size of 1st dimension of a dataset in a group of an input file may be different from the one with the same name and group membership in a different input file.
  • The same datasets in the same group but in different input files share the their 2nd dimension sizes.

Output HDF5 File

  • A single HDF5 output file will be created (the default create mode), unless the '-a' append mode is enabled. In append mode case, the output file must be a file that has been previously concatenated.
  • For create mode, the output file shares the same schema as the input files, i.e. the same numbers and names of groups and datasets.
  • The size of 1st dimension (most significant) of individual datasets are sum of the 1st dimension of the same dataset from all input files.
  • HDF5 compression and data chunking settings can be customized by command-line options (see below.)

Compiler and Software Requirements

  • A C++ compiler that support ISO C++0x standard or higher
  • MPI C and C++ compilers
  • An HDF5 library version 1.10.5 and later built with parallel I/O feature enabled

Instructions to Build

  1. If building from a git clone of this repository, then please run commands below first. Otherwise, if building from an official release, this step can be skipped. 
    git clone https://github.com/NU-CUCIS/ph5concat.git
cd ph5concat
autoreconf -i
  2. Run command 'configure'. An example is given below. 
    ./configure --with-mpi=$HOME/MPICH/3.3 \
            --with-hdf5=$HOME/HDF5/1.10.5 \
            CFLAGS="-O2 -DNDEBUG" \
            CXXFLAGS="-O2 -DNDEBUG" \
            LIBS="-ldl -lz" \
            --enable-profiling
    • Option '--enable-profiling' enables timing measurement for internal functions and to report timing breakdowns to the standard output.
  3. Run command 'make' to create the executable file named "ph5_concat"

Command to Run

  • Command-line options are: 
    % ./ph5_concat -h
mpiexec -n <np> ./ph5_concat [-h|-q|-a|-d|-r|-s|-p] [-t num] [-m size] [-k base_name] [-z level] [-b size] [-o outfile] [-i infile]

[-h]           print this command usage message
[-q]           enable quiet mode (default: disable)
[-a]           append concatenated data to an existing HDF5 file (default: no)
[-d]           disable in-memory I/O (default: enable)
[-r]           disable chunk caching for raw data (default: enable)
[-s]           one process creates followed by all processes open file (default: off)
[-p]           use MPI-IO to open input files (default: POSIX)
[-t num]       use parallel I/O strategy 1 or 2 (default: 2)
[-m size]      disable compression for datasets of size smaller than 'size' MiB
[-k base_name] dataset name in group /spill to generate partitioning keys
[-z level]     GZIP compression level (default: 6)
[-c]           enforces the contiguous layout for all datasets (default: false)
[-b size]      I/O buffer size per process (default: 128 MiB)
[-o outfile]   output file name (default: out.h5)
[-i infile]    input file containing HEP data files (default: list.txt)
    • <np>: Number of MPI processes.
    • partitioning keys: when command-line option '-k' is used, a new dataset, referred as the 'partition key dataset', will be created in each group in the output file, which can be used by applications to partition datasets among processes when performing parallel read operations. The name of this new dataset is 'base_name.seq' where base_name is the dataset name specified in the command-line option '-k'. Contents of the partition key dataset will be generated based on the dataset base_name in group '/spill'. Thus all input files must contain group '/spill', if option '-k' is used. This base_name dataset should contain integral values sorted in a non-decreasing order, i.e. a latter element is either equal or bigger than the former and are not necessarily incremented by one. Because the concatenation implemented in ph5_concat is based on the order of given input file names, the values of base_name dataset in one file are always treated as larger values than the files concatenated before it.
    • An example use of option '-k' is '-k evt', which is the event ID. A common practice of data partitioning strategy used in parallel reads is to assign dataset elements associated with the same values of 'run', 'subrun', and 'evt' to the same MPI process. These 3 datasets are referred to as 3-tuple index datasets. If the order of data is important, then users are recommended to first run utility program utils/sort_file_list to sort the input file names, and then use the sorted file name list to run ph5_concat.
    • Note the unique ID values in the key datasets generated in the output file are consistent across all the groups.
    • When option '-k' is not used, the partition key dataset will not be created. In this case, users can later run utility program add_key to add partitioning key datasets.
    • I/O buffer size: when command-line option '-b' is used with value 0, this is equivalent to set the size to unlimited, i.e. ph5_concat will allocate a buffer large enough to write each dataset in a single call to H5Dwrite. In this case, users may encounter out-of-memory errors.
    • I/O strategies: two I/O strategies (1 and 2) are currently supported. Both strategies share the same method for reading and writing the 1D datasets. For 1D datasets, input files are first assigned disjointly and evenly among all processes. Each process reads each 1D dataset entirely from the assigned files and writes it to the output files using collective I/O. The difference between strategies 1 and 2 are for the 2D datasets. In strategy 1, all processes open all input files collectively using MPI-IO and read all individual 2D datasets collectively (i.e. shared-file reads), followed by all processes collectively writing individual datasets to the output file. In this strategy, all reads and writes are collective for each dataset. In strategy 2, each process reads datasets only from the disjointly assigned file (i.e. no shared-file reads) and then all processes collectively write each of the datasets to the output file. In this strategy, reads are independent but writes are collective.
    • When using '-a' append mode, the output file must exist. The input files will be concatenated into the output file.
    • When using both options '-a' and '-k', the partitioning key datasets must have been created previously in the output file and their names must be the same as the one used in '-k' option.

Run example

An example run with small input files for illustration is available in folder examples.

Publications

Development Team:

Questions/Comments:

Project funding supports:

This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Scientific Discovery through Advanced Computing (SciDAC) program. This work is a collaboration of RAPIDS Institute and HEP Data Analytics on HPC. This work is also supported in part by the DOE awards, United States DE-SC0014330 and DE-SC0019358.