DOI: .. image:: https://zenodo.org/badge/22829423.svg

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A Massively Parallel Sorting Library. The library implements a histogram sort. The scaling of MP-sort up to 160,000 MPI ranks has been studied by [1]. MP-Sort is the sorting module in BlueTides Simulation [2].

MP-sort can be very useful for BigData simulation and analysis on traditional HPC platforms with MPI.

We provide

• a C interface that can be easily integrated into data producers, such as large scale simulation applications; and
• a Python interface that can be easily integrated into data consumers, such as parallel Python scripts that query simulation data.

A real world Python example is https://github.com/bccp/nbodykit/blob/master/ichalo.py , where mpsort is used twice to match up properties of particles cross two different snapshots, to establish the motion of Dark Matter halos cross cosmic times.

Use the Makefile to build / install the .a targets, and link against

-lradixsort -lmpsort-mpi

Makefile supports overriding CC, MPICC and CFLAGS

The header to include is

#include <mpsort.h>

The python binding can be installed with

python setup.py install [--user]

The same Makefile overrides are supported, if the environment variables are set.

The basic C interface is:

void mpsort_mpi(void * base, size_t nmemb, size_t size,
    void (*radix)(const void * ptr, void * radix, void * arg),
    size_t rsize,
    void * arg, MPI_Comm comm);

/*
Parameters
----------
base :
    base pointer of the local data
nmemb :
    number of items in the local data
elsize :
    size of an item in the local data
radix (ptr, radix, arg):
    the function calculates the radix of an item at ptr;
    the raxis shall be stored at memory location pointed to radix
rsize :
    the size of radix array in bytes; only the behavior for 8 is well-defined:
    the radix is interpreted as uint64.
arg   :
    argument to pass into radix()
comm  :
    the MPI communicator for the sort.

*/

The basic Python interface is:

import mpsort

mpsort.sort(localdata, orderby=None, comm=None, tuning=[])

"""
Sort an distributed array in place.

Parameters
----------
localdata : array_like
    local data, must be C_CONTIGUOUS, and of a struct-dtype.
    for example, :code:`localdata = numpy.empty(10, dtype=[('key', 'i4'), ('value', 'f4')])`.
orderby : scalar
    the field to be sorted by. The field must be of an integral type. 'i4', 'i8', 'u4', 'u8'.

"""

For runs with very large number of ranks, we may experience slow down due to the backend selecting a conservative MPI_Allreducev implementation. For Cray MPI, the following environment helps:

export MPICH_ALLREDUCE_BLK_SIZE=$((4096*1024*2))

There are also flags controlling the algorithm used by MPI_Alltoallv.

If the communication is very sparse (e.g. mostly sorted data). Then using a sparse algorithm based on non-blocking send / recv may provide better performance; some MPI implementations do not automatically switch the algorithm. We can allow mpsort to automatically select a sparse algorithm by setting the environment MPSORT_ENABLE_SPARSE_ALLTOALLV, calling mpsort_mpi_set_option(MPSORT_ENABLE_SPARSE_ALLTOALLV), or passing 'ENABLE_SPARSE_ALLTOALLV' to the tuning argument of the python interface.