pm_arraySplit Module Reference

This module contains procedures and generic interfaces for splitting arrays of various types at the specified instances of occurrence of pattern. More...

Data Types
interface	setSplit
	Return the parts of the input array split at the requested occurrences of the input sep. More...

Variables
character(*, SK), parameter	MODULE_NAME = "@pm_arraySplit"

Detailed Description

This module contains procedures and generic interfaces for splitting arrays of various types at the specified instances of occurrence of pattern.

Benchmarks:

Benchmark :: The runtime performance of setSplit for scalar vs. vector input sep argument. ⛓

! Test the performance of split with `vector_sep` vs. `scalar_sep`.
program benchmark
 
    use iso_fortran_env, only: error_unit
    use pm_container, only: cvi_type
    use pm_kind, only: IK, LK, RK, SK
    use pm_bench, only: bench_type
 
    implicit none
 
    integer(IK)                         :: i
    integer(IK)                         :: isize
    integer(IK)                         :: fileUnit
    integer(IK) , parameter             :: NSIZE = 15_IK
    integer(IK) , parameter             :: NBENCH = 2_IK
    integer(IK)                         :: arraySize(NSIZE)
    logical(LK)                         :: dummy = .true._LK
    integer(IK) , allocatable           :: array(:)
    integer(IK) , parameter             :: sep(1) = 0_IK
    type(bench_type)                    :: bench(NBENCH)
    type(cvi_type), allocatable   :: ArraySplit(:)
 
    bench(1) = bench_type(name = SK_"scalar_sep", exec = scalar_sep , overhead = setOverhead)
    bench(2) = bench_type(name = SK_"vector_sep", exec = vector_sep , overhead = setOverhead)
 
    arraySize = [( 2_IK**isize, isize = 1_IK, NSIZE )]
 
    write(*,"(*(g0,:,' '))")
    write(*,"(*(g0,:,' '))") "scalar_sep() vs. vector_sep()"
    write(*,"(*(g0,:,' '))")
 
    open(newunit = fileUnit, file = "main.out", status = "replace")
 
        write(fileUnit, "(*(g0,:,','))") "arraySize", (bench(i)%name, i = 1, NBENCH)
 
        loopOverArraySize: do isize = 1, NSIZE
 
            write(*,"(*(g0,:,' '))") "Benchmarking with size", arraySize(isize)
            allocate(array(arraySize(isize)))
 
            do i = 1, NBENCH
                bench(i)%timing = bench(i)%getTiming(minsec = 0.05_RK)
            end do
 
            deallocate(array)
            write(fileUnit,"(*(g0,:,','))") arraySize(isize), (bench(i)%timing%mean, i = 1, NBENCH)
 
        end do loopOverArraySize
        write(*,"(*(g0,:,' '))") dummy
        write(*,"(*(g0,:,' '))")
 
    close(fileUnit)
 
contains
 
    !%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    ! procedure wrappers.
    !%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
    subroutine setOverhead()
        call initialize()
        call finalize()
    end subroutine
 
    subroutine initialize()
        array(:) = 1_IK
    end subroutine
 
    subroutine finalize()
        dummy = dummy .and. size(ArraySplit, kind = IK) == 1_IK
    end subroutine
 
    subroutine scalar_sep()
        use pm_arraySplit, only: setSplit
        call initialize()
        call setSplit(ArraySplit, array, sep(1))
        call finalize()
    end subroutine
 
    subroutine vector_sep()
        block
            use pm_arraySplit, only: setSplit
            call initialize()
            call setSplit(ArraySplit, array, sep)
            call finalize()
        end block
    end subroutine
 
end program benchmark

Example Unix compile command via Intel ifort compiler ⛓

#!/usr/bin/env sh
rm main.exe
ifort -fpp -standard-semantics -O3 -Wl,-rpath,../../../lib -I../../../inc main.F90 ../../../lib/libparamonte* -o main.exe
./main.exe

Example Windows Batch compile command via Intel ifort compiler ⛓

del main.exe
set PATH=..\..\..\lib;%PATH%
ifort /fpp /standard-semantics /O3 /I:..\..\..\include main.F90 ..\..\..\lib\libparamonte*.lib /exe:main.exe
main.exe

Example Unix / MinGW compile command via GNU gfortran compiler ⛓

#!/usr/bin/env sh
rm main.exe
gfortran -cpp -ffree-line-length-none -O3 -Wl,-rpath,../../../lib -I../../../inc main.F90 ../../../lib/libparamonte* -o main.exe
./main.exe

Postprocessing of the benchmark output ⛓

#!/usr/bin/env python
 
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
 
fontsize = 14
 
methods = ["scalar_sep", "vector_sep"]
 
df = pd.read_csv("main.out")
 
 
 
ax = plt.figure(figsize = 1.25 * np.array([6.4,4.6]), dpi = 200)
ax = plt.subplot()
 
for method in methods:
    plt.plot( df["arraySize"].values
            , df[method].values
            , linewidth = 2
            )
 
plt.xticks(fontsize = fontsize)
plt.yticks(fontsize = fontsize)
ax.set_xlabel("Array Size", fontsize = fontsize)
ax.set_ylabel("Runtime [ seconds ]", fontsize = fontsize)
ax.set_title("Splitting array with sep(1) (scalar) vs. sep(1:1) (vector).\nLower is better.", fontsize = fontsize)
ax.set_xscale("log")
ax.set_yscale("log")
plt.minorticks_on()
plt.grid(visible = True, which = "both", axis = "both", color = "0.85", linestyle = "-")
ax.tick_params(axis = "y", which = "minor")
ax.tick_params(axis = "x", which = "minor")
ax.legend   ( methods
           #, loc='center left'
           #, bbox_to_anchor=(1, 0.5)
            , fontsize = fontsize
            )
 
plt.tight_layout()
plt.savefig("benchmark.scalarSep_vs_vectorSep.runtime.png")
 
 
 
ax = plt.figure(figsize = 1.25 * np.array([6.4,4.6]), dpi = 200)
ax = plt.subplot()
 
plt.plot( df["arraySize"].values
        , np.ones(len(df["arraySize"].values))
       #, linestyle = "--"
       #, color = "black"
        , linewidth = 2
        )
plt.plot( df["arraySize"].values
        , df["vector_sep"].values / df["scalar_sep"].values
        , linewidth = 2
        )
 
plt.xticks(fontsize = fontsize)
plt.yticks(fontsize = fontsize)
ax.set_xlabel("Array Size", fontsize = fontsize)
ax.set_ylabel("Runtime compared to scalar_sep()", fontsize = fontsize)
ax.set_title("Runtime Ratio: split with sep(1:1) / split with sep(1).\nLower means faster. Lower than 1 means faster than scalar_sep.", fontsize = fontsize)
ax.set_xscale("log")
#ax.set_yscale("log")
plt.minorticks_on()
plt.grid(visible = True, which = "both", axis = "both", color = "0.85", linestyle = "-")
ax.tick_params(axis = "y", which = "minor")
ax.tick_params(axis = "x", which = "minor")
ax.legend   ( ["scalar_sep", "vector_sep"]
           #, bbox_to_anchor = (1, 0.5)
           #, loc = "center left"
            , fontsize = fontsize
            )
 
plt.tight_layout()
plt.savefig("benchmark.scalarSep_vs_vectorSep.runtime.ratio.png")

Visualization of the benchmark output ⛓

Benchmark moral ⛓

1. The procedures under the generic interface setSplit take both scalar and vector sep arguments.
   As evidenced by the above benchmark, when the input sep is vector of length 1, it is much faster, up to 4X, to pass sep as a scalar instead of a whole array of length 1.
   Note that this benchmark is likely irrelevant to removing substrings from Fortran strings.
   

Test:

test_pm_arraySplit

Todo:

Normal Priority: A benchmark comparing the performance of output index array vs. output jagged array would be informative here.

Final Remarks ⛓

---

If you believe this algorithm or its documentation can be improved, we appreciate your contribution and help to edit this page's documentation and source file on GitHub.
For details on the naming abbreviations, see this page.
For details on the naming conventions, see this page.
This software is distributed under the MIT license with additional terms outlined below.

1. If you use any parts or concepts from this library to any extent, please acknowledge the usage by citing the relevant publications of the ParaMonte library.
   
2. If you regenerate any parts/ideas from this library in a programming environment other than those currently supported by this ParaMonte library (i.e., other than C, C++, Fortran, MATLAB, Python, R), please also ask the end users to cite this original ParaMonte library.
   

This software is available to the public under a highly permissive license.
Help us justify its continued development and maintenance by acknowledging its benefit to society, distributing it, and contributing to it.

Copyright

Computational Data Science Lab

Author:

Fatemeh Bagheri, Wednesday 12:20 AM, October 13, 2021, Dallas, TX

Variable Documentation

MODULE_NAME

character(*, SK), parameter pm_arraySplit::MODULE_NAME = "@pm_arraySplit"

Definition at line 54 of file pm_arraySplit.F90.