pm_mathCumSum Module Reference

This module contains the procedures and interfaces for computing the cumulative sum of an array. More...

Data Types
interface	getCumSum
	Generate and return the cumulative sum of the input array, optionally in the backward direction and, optionally reverse the output cumulative sum array upon return. More...
interface	setCumSum
	Return the cumulative sum of the input array, optionally in the backward direction and optionally, reverse the output cumulative sum array upon return. More...

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

Detailed Description

This module contains the procedures and interfaces for computing the cumulative sum of an array.

Benchmarks:

Benchmark :: The runtime performance of getCumSum vs. setCumSum ⛓

! Test the performance of `getCumSum()` vs. `setCumSum()`.
program benchmark
 
    use iso_fortran_env, only: error_unit
    use pm_kind, only: IK, RK, SK
    use pm_bench, only: bench_type
 
    implicit none
 
    integer(IK)                         :: i
    integer(IK)                         :: iarr
    integer(IK)                         :: fileUnit
    integer(IK)     , parameter         :: NARR = 11_IK
    integer(IK)                         :: arraySize(NARR)
    real(RK)                            :: dummySum = 0._RK
    real(RK)        , allocatable       :: array(:)
    real(RK)        , allocatable       :: cumsum(:)
    type(bench_type), allocatable       :: bench(:)
 
    bench = [ bench_type(name = SK_"setCumSum", exec = setCumSum, overhead = setOverhead) &
            , bench_type(name = SK_"getCumSum", exec = getCumSum, overhead = setOverhead) &
            , bench_type(name = SK_"setCumSum_overwrite", exec = setCumSum_overwrite, overhead = setOverhead) &
            , bench_type(name = SK_"getCumSum_withBounds", exec = getCumSum_withBounds, overhead = setOverhead) &
            ]
 
    arraySize = [( 2_IK**iarr, iarr = 1_IK, NARR )]
 
    write(*,"(*(g0,:,' '))")
    write(*,"(*(g0,:,' '))") "setCumSum() vs. getCumSum() vs. getCumSum_withBounds()"
    write(*,"(*(g0,:,' '))")
 
    open(newunit = fileUnit, file = "main.out", status = "replace")
 
        write(fileUnit, "(*(g0,:,','))") "arraySize", (bench(i)%name, i = 1, size(bench))
 
        loopOverArraySize: do iarr = 1, NARR
 
            allocate(array(arraySize(iarr)))
            allocate(cumsum(arraySize(iarr)), source = 0._RK)
            write(*,"(*(g0,:,' '))") "Benchmarking with array size", arraySize(iarr)
 
            do i = 1, size(bench)
                bench(i)%timing = bench(i)%getTiming() !, minsec = 0.1_RK)
            end do
            write(fileUnit,"(*(g0,:,','))") arraySize(iarr), (bench(i)%timing%mean, i = 1, size(bench))
 
            deallocate(array, cumsum)
 
        end do loopOverArraySize
        write(*,"(*(g0,:,' '))") dummySum
        write(*,"(*(g0,:,' '))")
 
    close(fileUnit)
 
contains
 
    !%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    ! procedure wrappers.
    !%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
    subroutine setOverhead()
        call setArray()
        call getDummy()
    end subroutine
 
    subroutine setArray()
        call random_number(array)
    end subroutine
 
    subroutine getDummy()
        dummySum = dummySum + cumsum(1) + array(1)
    end subroutine
 
    subroutine setCumSum()
        block
            use pm_mathCumSum, only: setCumSum
            call setArray()
            call setCumSum(cumsum, array)
            call getDummy()
        end block
    end subroutine
 
    subroutine setCumSum_overwrite()
        block
            use pm_mathCumSum, only: setCumSum
            call setArray()
            call setCumSum(array)
            call getDummy()
        end block
    end subroutine
 
    subroutine getCumSum()
        block
            use pm_mathCumSum, only: getCumSum
            call setArray()
            cumsum = getCumSum(array)
            call getDummy()
        end block
    end subroutine
 
    subroutine getCumSum_withBounds()
        block
            use pm_mathCumSum, only: getCumSum
            call setArray()
            cumsum(:) = getCumSum(array)
            call getDummy()
        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
 
import os
dirname = os.path.basename(os.getcwd()) 
 
fontsize = 14
 
df = pd.read_csv("main.out", delimiter = ",")
colnames = list(df.columns.values)
 
 
 
ax = plt.figure(figsize = 1.25 * np.array([6.4,4.6]), dpi = 200)
ax = plt.subplot()
 
for colname in colnames[1:]:
    plt.plot( df[colnames[0]].values
            , df[colname].values
            , linewidth = 2
            )
 
plt.xticks(fontsize = fontsize)
plt.yticks(fontsize = fontsize)
ax.set_xlabel(colnames[0], fontsize = fontsize)
ax.set_ylabel("Runtime [ seconds ]", fontsize = fontsize)
ax.set_title(" vs. ".join(colnames[1:])+"\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   ( colnames[1:]
           #, loc='center left'
           #, bbox_to_anchor=(1, 0.5)
            , fontsize = fontsize
            )
 
plt.tight_layout()
plt.savefig("benchmark." + dirname + ".runtime.png")
 
 
 
ax = plt.figure(figsize = 1.25 * np.array([6.4,4.6]), dpi = 200)
ax = plt.subplot()
 
plt.plot( df[colnames[0]].values
        , np.ones(len(df[colnames[0]].values))
        , linestyle = "--"
       #, color = "black"
        , linewidth = 2
        )
for colname in colnames[2:]:
    plt.plot( df[colnames[0]].values
            , df[colname].values / df[colnames[1]].values
            , linewidth = 2
            )
 
plt.xticks(fontsize = fontsize)
plt.yticks(fontsize = fontsize)
ax.set_xlabel(colnames[0], fontsize = fontsize)
ax.set_ylabel("Runtime compared to {}".format(colnames[1]), fontsize = fontsize)
ax.set_title("Runtime Ratio Comparison. Lower means faster.\nLower than 1 means faster than {}().".format(colnames[1]), 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   ( colnames[1:]
           #, bbox_to_anchor = (1, 0.5)
           #, loc = "center left"
            , fontsize = fontsize
            )
 
plt.tight_layout()
plt.savefig("benchmark." + dirname + ".runtime.ratio.png")

Visualization of the benchmark output ⛓

Benchmark moral ⛓

1. The procedures under the generic interface getCumSum are functions while the procedures under the generic interface setCumSum are subroutines.
   From the benchmark results, it appears that the functional interface performs less efficiently than the subroutine interface.
   
2. Furthermore, specifying the array bounds on the left-hand-side (LHS) assignment in the case of the functional interface (to avoid automatic reallocation) does not appear to enhance the performance of the functional interface in any meaningful way.
   In other words, the compiler appears to be smart enough to not reallocate the LHS needlessly.
   

Test:

test_pm_mathCumSum

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:

Amir Shahmoradi, April 25, 2015, 2:21 PM, National Institute for Fusion Studies, The University of Texas Austin

Variable Documentation

MODULE_NAME

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

Definition at line 58 of file pm_mathCumSum.F90.