/fortran-vs-julia

Fortran-Julia syntax comparison and Maxwell Solver in 2D using Yee numerical scheme and MPI topology

Primary LanguageFortranGNU General Public License v3.0GPL-3.0

Julia Syntax: Comparison with Fortran

This is a simple cheatsheet and some performance comparison for scientific programmers who are interested in discover Julia. It is not an exhaustive list. This page is inspired from A Cheatsheet for Fortran 2008 Syntax: Comparison with Python 3.

Fortran Julia
Top-level constructs
the main program 
program my_program
    ...
end program
 
function my_program()
    ...
end
my_program()
Not required but it is recommended to use a function for your main program
modules 
module my_module
    ...
end module my_module
 
module MyModule
    ...
end
subroutines 
subroutine my_subroutine
    ...
end subroutine my_subroutine
 
function my_subroutine!
    ...
end
The bang in the function name is a convention if a function mutates one or more of its arguments. The convention is that the modified arguments should (if possible) come first.
functions 
function f(x) result(res)
    res = ...
end function f
 
function my_function(x)
    ...
    return res
end
Generic interface 
module cube_root_functions
interface cube_root
    function s_cube_root(x)
      real :: s_cube_root
      real, intent(in) :: x
    end function s_cube_root
    function d_cube_root(x)
      double precision :: d_cube_root
      double precision, intent(in) :: x
    end function d_cube_root
  end interface
end module cube_root_functions
function s_cube_root(x)
    real :: s_cube_root
    real, intent(in) :: x
    s_cube_root = x ** (1.0/3.0)
end function s_cube_root
function d_cube_root(x)
    double precision :: d_cube_root
    double precision, intent(in) :: x
    d_cube_root = x ** (1.0d0/3.0d0)
end function d_cube_root
 
cube_root( x :: Float32 ) :: Float32 = x^(1/3)
cube_root( x :: Float64 ) :: Float64 = x^(1/3)
submodules 
module main_module
    ...
contains
<submodule statements>
end module main_module
 
module MainModule
    ...
    module SubModule 
        ...
    end
end
import statement 
use my_module
use my_module, only : fun1, var1
 
using MyModule
import MyModule: fun1, var1
call subroutines and functions 
call my_subroutine(args)
my_function(args)
 
my_function(args)
abort a program 
stop
 
exit()
inline comments 
! This is a comment
 
# This is a comment
include external source files 
include 'source_file_name'
 
include("source_file_name")
Control flow patterns
if construct 
if <logical expr> then
    ...
else if <logical expr> then
    ...
else
    ...
end if
 
if <logical expr>
    ...
elseif <logical expr>
    ...
else
    ...
end
case construct 
select case <expr>
    case <value>
        ...
    case <value>
        ...
    case default
        ...
end select
 Not supported. Possible alternative is to use the ternary 
 ? :
syntax: 
function case(x)
    x == 1     ? println(1) : 
    x + 1 == 3 ? println(2) :
    x == 3     ? println(3) :
    println("greater than 3")
end
do construct 
do i = start_value, end_value, step
    ...
end do
 
for i in start:step:end
    ...
end
do while construct 
do while <logical expr>
    ...
end do
 
while <logical expr>
    ...
end
break from a loop 
exit
 
break
leave this iteration and continue to the next iteration 
cycle
 
continue
Data types
declaration 
integer(kind=8) :: n = 0
real(kind=8) :: x = 0.
 
n = 0
n :: Int64 = 0
x = 0.
x :: Float64 = 0.
named constants 
integer, parameter :: answer = 42
real(8), parameter :: pi = 4d0 * atan(1d0)
 
const answer = 42
pi is a named constant in Julia standard.
complex number 
complex :: z = (1., -1.)
 
z = 1 - 1im
z = complex(1, -1)
string 
character(len=10) :: str_fixed_length
character(len=:), allocatable :: str_var_length
 
string = "this is a string"
pointer 
real, pointer :: p
real, target :: r
p => r
 
p = Ref(r)
boolean 
.true.
.false.
 
true
false
logical operators 
.not.
.and.
.or.
.eqv.
.neqv.
 
!
&&
||
Other logical operators do not have built-in support.
equal to 
==, .eq.
 
==
not equal to 
/=, .ne.
 
!==
greater than 
>, .gt.
 
>
less than 
<, .lt.
 
<
greater than or equal to 
>=, .ge.
 
>=
less than or equal to 
<=, .ge.
 
<=
array declaration 
real(8), dimension(3) :: a = [1., 2., 3.]
 
a = [1., 2., 3.]
string array declaration 
character(len=20), dimension(3, 4) :: char_arr
 
char_arr = String[]
push!(char_arr, new_string)
There is no easy way to preallocate space for strings.
elementwise array operations 
a op b
op can be +, -, *, /, **, =, ==, etc.
This is supported since the Fortran 90 standard. 		Supported by using the broadcast operator `.`and the `f.(x)` syntax
first element 
a(1)
 
a[1] (or a[begin] for general indexing)
slicing 
a(1:5)
This slice includes a(5). 
a[1:5] (or @view(a[1:5]) for non-allocating slicing)
This slice includes a[5].
slicing with steps 
a(1:100:2)
 
a[1:2:100]
size 
size(a)
 
length(a)
shape 
shape(a)
 
size(a)
shape along a dimension 
size(a, dim)
 
size(a, dim)
Type conversion
to integer by truncation 
int(x)
 
trunc(Int, x )
to integer by rounding 
nint()
 
round()
integer to float 
real(a[, kind])
 
float()
complex to real 
real(z[, kind])
 
real()
to complex 
cmplx(x [, y [, kind]])
 
complex()
to boolean 
logical()
 
Bool()
Derived data types
definition 
type Point
    real(8) :: x, y
end type Point
 
struct Point
    x :: Float64
    y :: Float64
end
instantiation 
type(Point) :: point1 = Point(-1., 1.)
 
point1 = Point(-1., 1.)
get attributes 
point1%x
point1%y
 
point1.x
point1.y
array of derived type 
type(Point), dimension(:), allocatable :: point_arr
 
point_arr = Vector{Point}
type bound procedures (aka class method) Assume that Circle has a type bound procedure (subroutine) print_area. 
type(Circle) :: c
call c%print_area
 Assume that Circle has a method print_area(c :: Circle). 
c = Circle()
print_area(c)
Built-in mathematical functions
functions with the same names 
abs(), cos(), cosh(), exp(), floor(), log(),
log10(), max(), min(), sin(), sinh(), sqrt(),
sum(), tan(), tanh(), acos(), asin(), atan()
 Have the same name in Julia.
functions with different names 
aimag()
atan2(x, y)
ceiling()
conjg(z)
modulo()
call random_number()
 
imag()
atan(x, y)
ceil()
conj()
mod(), %
Random.rand()
Built-in string functions
string length 
len()
 
length()
string to ASCII code 
iachar()
 
Int()
ASCII code to string 
achar()
 
String()
string slicing Same as 1D array slicing. Same as 1D array slicing.
find the position of a substring 
index(string, substring)
 
findfirst(substring, string)
string concatenation 
"hello" // "world"
 
"hello" * "world"
string("hello", "world")
Array constructs
where construct 
where a > 0
    b = 0
elsewhere
    b = 1
end where
 
b[a .> 0] .= 0
b[a .<= 0] .= 1
or (faster - does not allocate an intermediate array): 
for i in eachindex(a)
    a[i] > 0 ? b[i] = 0 : b[i] = 1
end
Comprehension 
    
integer, parameter :: n = 20
integer, parameter :: m = n*(n+1)/2
integer :: i, j
complex, dimension(m) :: a
a = [ ( ( cmplx(i,j), i=j,n), j=1,n) ]
 
a = [ complex(i,j) for j=1:n for i=j:n]
forall construct 
real, dimension(10, 10) :: a = 0
int :: i, j
...
forall (i = 1:10, j = 1:10, i <= j)
    a(i, j) = i + j
end forall
 
a = zeros(Float32, 10, 10)
for i in 1:10, j in 1:10
    if i <= j
        a[i, j] = i + j
    end
end
CPU time 
call cpu_time()
 
time = @elapsed begin
...
end
command line arguments 
call command_argument_count()
call get_command()
call get_command_argument()
 For basic parsing, use ARGS
Input/output
print 
print fmt, <output list>
 
println()
read from the command prompt 
read fmt, <input list>
 
readline()
open a file 
open(unit, file, ...)
 
f = open(file, 'r')
read from a file 
read(unit, fmt, ...) <input list>
 
read(f)
readlines(f)
write to a file 
write(unit, fmt, ...) <output list>
 
write(f, ...)
close a file 
close(unit, ...)
 
close(f)
file inquiry 
inquire(unit, ...)
 
isfile("my_file.txt")
backspace in a file 
backspace(unit, ...)
 
skip(f, -1)
end of file (EOF) 
endfile(unit, ...)
 
seekend(f)
return to the start of a file 
rewind(unit, ...)
 
seekstart(f)

Maxwell parallel solver in 2D

Here an example of a Fortran to Julia translation. We use the Yee numerical scheme FDTD: Finite-Difference Time-Domain method and MPI topology. You can find a serial version and a parallel version using MPI library.

Test your MPI.jl installation with

$ mpirun -np 4 julia --project hello_mpi.jl
Hello world, I am 0 of 4
Hello world, I am 3 of 4
Hello world, I am 1 of 4
Hello world, I am 2 of 4

Performances (without disk IO)

On small program like this in Julia is really fast.

Serial computation

1200 x 1200 and 1000 iterations.

  • julia -O3 --check-bounds=no maxwell_serial.jl : 14 seconds
  • make && time ./maxwell_serial_fortran : 31 seconds

1200 x 1200 on 9 processors and 1000 iterations

  • make && time mpirun -np 9 ./maxwell_mpi_fortran : 7 seconds
  • mpirun -np 9 julia --project -O3 --check-bounds=no : 5 seconds

Plot the magnetic field

Uncomment the plot_fields call in Julia programs or change idiag value in input_data for fortran.

gnuplot bz.gnu