This module contains procedures and generic interfaces for shuffling arrays of various types.
More...
This module contains procedures and generic interfaces for shuffling arrays of various types.
- Benchmarks:
Benchmark :: The runtime performance of getShuffled vs. setShuffled ⛓
4 use iso_fortran_env,
only:
error_unit
11 integer(IK) :: fileUnit
12 integer(IK) ,
parameter :: NSIZE
= 15_IK
13 integer(IK) ,
parameter :: NBENCH
= 2_IK
14 integer(IK) :: arraySize(NSIZE)
15 real(RK) :: dummy
= 0._RK
16 real(RK) ,
allocatable :: Array(:)
17 type(bench_type) :: bench(NBENCH)
19 bench(
1)
= bench_type(name
= SK_
"setShuffled", exec
= setShuffled, overhead
= setOverhead)
20 bench(
2)
= bench_type(name
= SK_
"getShuffled", exec
= getShuffled, overhead
= setOverhead)
22 arraySize
= [(
2_IK**isize, isize
= 1_IK, NSIZE )]
24 write(
*,
"(*(g0,:,' '))")
25 write(
*,
"(*(g0,:,' '))")
"setShuffled() vs. getShuffled()"
26 write(
*,
"(*(g0,:,' '))")
28 open(newunit
= fileUnit, file
= "main.out", status
= "replace")
30 write(fileUnit,
"(*(g0,:,','))")
"arraySize", (bench(i)
%name, i
= 1, NBENCH)
32 loopOverArraySize:
do isize
= 1, NSIZE
34 write(
*,
"(*(g0,:,' '))")
"Benchmarking with size", arraySize(isize)
36 allocate(Array(arraySize(isize)))
37 call random_number(Array)
39 bench(i)
%timing
= bench(i)
%getTiming(minsec
= 0.07_RK)
43 write(fileUnit,
"(*(g0,:,','))") arraySize(isize), (bench(i)
%timing
%mean, i
= 1, NBENCH)
45 end do loopOverArraySize
46 write(
*,
"(*(g0,:,' '))") dummy
47 write(
*,
"(*(g0,:,' '))")
57 subroutine setOverhead()
62 dummy
= dummy
+ Array(
1)
65 subroutine setShuffled()
73 subroutine getShuffled()
Perform an unbiased random shuffling of the input array, known as the Knuth or Fisher-Yates shuffle,...
Perform an unbiased random shuffling of the input array, known as the Knuth or Fisher-Yates shuffle.
Generate and return an object of type timing_type containing the benchmark timing information and sta...
This module contains procedures and generic interfaces for shuffling arrays of various types.
This module contains abstract interfaces and types that facilitate benchmarking of different procedur...
This module defines the relevant Fortran kind type-parameters frequently used in the ParaMonte librar...
integer, parameter RK
The default real kind in the ParaMonte library: real64 in Fortran, c_double in C-Fortran Interoperati...
integer, parameter LK
The default logical kind in the ParaMonte library: kind(.true.) in Fortran, kind(....
integer, parameter IK
The default integer kind in the ParaMonte library: int32 in Fortran, c_int32_t in C-Fortran Interoper...
integer, parameter SK
The default character kind in the ParaMonte library: kind("a") in Fortran, c_char in C-Fortran Intero...
This is the class for creating benchmark and performance-profiling objects.
Example Unix compile command via Intel ifort
compiler ⛓
3ifort -fpp -standard-semantics -O3 -Wl,-rpath,../../../lib -I../../../inc main.F90 ../../../lib/libparamonte* -o main.exe
Example Windows Batch compile command via Intel ifort
compiler ⛓
2set PATH=..\..\..\lib;%PATH%
3ifort /fpp /standard-semantics /O3 /I:..\..\..\include main.F90 ..\..\..\lib\libparamonte*.lib /exe:main.exe
Example Unix / MinGW compile command via GNU gfortran
compiler ⛓
3gfortran -cpp -ffree-line-length-none -O3 -Wl,-rpath,../../../lib -I../../../inc main.F90 ../../../lib/libparamonte* -o main.exe
Postprocessing of the benchmark output ⛓
3import matplotlib.pyplot
as plt
9methods = [
"setShuffled",
"getShuffled"]
11df = pd.read_csv(
"main.out")
17ax = plt.figure(figsize = 1.25 * np.array([6.4,4.6]), dpi = 200)
21 plt.plot( df[
"arraySize"].values
26plt.xticks(fontsize = fontsize)
27plt.yticks(fontsize = fontsize)
28ax.set_xlabel(
"Array Size", fontsize = fontsize)
29ax.set_ylabel(
"Runtime [ seconds ]", fontsize = fontsize)
30ax.set_title(
"setShuffled() vs. getShuffled().\nLower is better.", fontsize = fontsize)
34plt.grid(visible =
True, which =
"both", axis =
"both", color =
"0.85", linestyle =
"-")
35ax.tick_params(axis =
"y", which =
"minor")
36ax.tick_params(axis =
"x", which =
"minor")
44plt.savefig(
"benchmark.getShuffled_vs_setShuffled.runtime.png")
50ax = plt.figure(figsize = 1.25 * np.array([6.4,4.6]), dpi = 200)
53plt.plot( df[
"arraySize"].values
54 , np.ones(len(df[
"arraySize"].values))
58plt.plot( df[
"arraySize"].values
59 , df[
"getShuffled"].values / df[
"setShuffled"].values
63plt.xticks(fontsize = fontsize)
64plt.yticks(fontsize = fontsize)
65ax.set_xlabel(
"Array Size", fontsize = fontsize)
66ax.set_ylabel(
"Runtime compared to setShuffled()", fontsize = fontsize)
67ax.set_title(
"getShuffled() to setShuffled() Runtime Ratio.\nLower means faster. Lower than 1 means faster than setShuffled().", fontsize = fontsize)
71plt.grid(visible =
True, which =
"both", axis =
"both", color =
"0.85", linestyle =
"-")
72ax.tick_params(axis =
"y", which =
"minor")
73ax.tick_params(axis =
"x", which =
"minor")
74ax.legend ( [
"setShuffled()",
"getShuffled()",
"Direct Method"]
81plt.savefig(
"benchmark.getShuffled_vs_setShuffled.runtime.ratio.png")
Visualization of the benchmark output ⛓
Benchmark moral ⛓
- The procedures under the generic interface getShuffled are functions while the procedures under the generic interface setShuffled are subroutines.
The current implementation of the functional interface requires making a copy of the input array that is subsequently passed to the subroutine interface for random shuffling.
As such, the observed performance degradation is expected.
Note that an extra copy of the output array to the user-specified object is also needed, making the functional interface two-copies more expensive than the subroutine interface.
- See also
- pm_arrayRemap
pm_arrayChange
pm_arrayChoice
pm_arrayShuffle
pm_distUnif
- Test:
- test_pm_arrayShuffle
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.
-
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.
-
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, September 1, 2017, 12:20 AM, Institute for Computational Engineering and Sciences (ICES), The University of Texas Austin