Optimizing my 2D Ising model code in Julia
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I'm just starting to learn Julia, I work primarily in physics and am used to writing most of my code in Fortran90 and occasionally Python for Tensorflow (also Mathematica but that's less relevant). Julia has been recommended to me and I started checking it out; I like it a lot in theory as a middle ground between the speed of Fortran and the syntax of Python. To test it out I wrote a simple 2D Ising model code implementing a basic single-spin-flip Metropolis Monte Carlo algorithm. However, this code runs very slowly compared to an equivalent code in Fortran. Am I doing something wrong which is significantly affecting the performance of the code? I know almost nothing beyond what I've done here. I am using the Juno IDE in Atom on Windows 10. As an aside, I would also like to know how I can make multiple plots in the Atom plot tab, but that's secondary.
using Printf
using Plots
L = 20
n_sweep = 20
n_therm = 1000
n_data = 100
temps = 4.0:-0.3:0.1
e1 = Array(1:n_data)
m1 = Array(1:n_data)
et =
mt =
energy = e1
magnetization = m1
s = ones(Int32,L,L)
function measure(i)
en = 0
m = 0
for x = 1:L
for y = 1:L
u = 1+mod(y,L)
r = 1+mod(x,L)
en -= s[x,y]*(s[x,u]+s[r,y])
m += s[x,y]
end
end
energy[i] = en
magnetization[i] = abs(m)
end
function sweep(n,T)
for i = 1:n
for x = 1:L
for y = 1:L
flip(x,y,T)
end
end
end
end
function flip(x,y,T)
u = 1+mod(y,L)
d = 1+mod(y-2,L)
r = 1+mod(x,L)
l = 1+mod(x-2,L)
de = 2*s[x,y]*(s[x,u]+s[x,d]+s[l,y]+s[r,y])
if (de < 0)
s[x,y] = -s[x,y]
else
p = rand()
if (p < exp(-de/T))
s[x,y] = -s[x,y]
end
end
end
for T in temps
sweep(n_therm, T)
energy = e1
magnetization = m1
for i = 1:n_data
sweep(n_sweep, T)
measure(i)
end
en1 = sum(energy)/n_data
ma1 = sum(magnetization)/n_data
push!(et,en1/(L*L))
push!(mt,ma1/(L*L))
@printf("%8.3f %8.3f n", en1/(L*L), ma1/(L*L))
end
plot(temps,mt)
performance beginner julia
asked 3 mins ago
KaiKai
1012
New contributor
Kai is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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add a comment |
$begingroup$
I'm just starting to learn Julia, I work primarily in physics and am used to writing most of my code in Fortran90 and occasionally Python for Tensorflow (also Mathematica but that's less relevant). Julia has been recommended to me and I started checking it out; I like it a lot in theory as a middle ground between the speed of Fortran and the syntax of Python. To test it out I wrote a simple 2D Ising model code implementing a basic single-spin-flip Metropolis Monte Carlo algorithm. However, this code runs very slowly compared to an equivalent code in Fortran. Am I doing something wrong which is significantly affecting the performance of the code? I know almost nothing beyond what I've done here. I am using the Juno IDE in Atom on Windows 10. As an aside, I would also like to know how I can make multiple plots in the Atom plot tab, but that's secondary.
using Printf
using Plots
L = 20
n_sweep = 20
n_therm = 1000
n_data = 100
temps = 4.0:-0.3:0.1
e1 = Array(1:n_data)
m1 = Array(1:n_data)
et =
mt =
energy = e1
magnetization = m1
s = ones(Int32,L,L)
function measure(i)
en = 0
m = 0
for x = 1:L
for y = 1:L
u = 1+mod(y,L)
r = 1+mod(x,L)
en -= s[x,y]*(s[x,u]+s[r,y])
m += s[x,y]
end
end
energy[i] = en
magnetization[i] = abs(m)
end
function sweep(n,T)
for i = 1:n
for x = 1:L
for y = 1:L
flip(x,y,T)
end
end
end
end
function flip(x,y,T)
u = 1+mod(y,L)
d = 1+mod(y-2,L)
r = 1+mod(x,L)
l = 1+mod(x-2,L)
de = 2*s[x,y]*(s[x,u]+s[x,d]+s[l,y]+s[r,y])
if (de < 0)
s[x,y] = -s[x,y]
else
p = rand()
if (p < exp(-de/T))
s[x,y] = -s[x,y]
end
end
end
for T in temps
sweep(n_therm, T)
energy = e1
magnetization = m1
for i = 1:n_data
sweep(n_sweep, T)
measure(i)
end
en1 = sum(energy)/n_data
ma1 = sum(magnetization)/n_data
push!(et,en1/(L*L))
push!(mt,ma1/(L*L))
@printf("%8.3f %8.3f n", en1/(L*L), ma1/(L*L))
end
plot(temps,mt)
performance beginner julia
asked 3 mins ago
KaiKai
1012
New contributor
Kai is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I'm just starting to learn Julia, I work primarily in physics and am used to writing most of my code in Fortran90 and occasionally Python for Tensorflow (also Mathematica but that's less relevant). Julia has been recommended to me and I started checking it out; I like it a lot in theory as a middle ground between the speed of Fortran and the syntax of Python. To test it out I wrote a simple 2D Ising model code implementing a basic single-spin-flip Metropolis Monte Carlo algorithm. However, this code runs very slowly compared to an equivalent code in Fortran. Am I doing something wrong which is significantly affecting the performance of the code? I know almost nothing beyond what I've done here. I am using the Juno IDE in Atom on Windows 10. As an aside, I would also like to know how I can make multiple plots in the Atom plot tab, but that's secondary.
using Printf
using Plots
L = 20
n_sweep = 20
n_therm = 1000
n_data = 100
temps = 4.0:-0.3:0.1
e1 = Array(1:n_data)
m1 = Array(1:n_data)
et =
mt =
energy = e1
magnetization = m1
s = ones(Int32,L,L)
function measure(i)
en = 0
m = 0
for x = 1:L
for y = 1:L
u = 1+mod(y,L)
r = 1+mod(x,L)
en -= s[x,y]*(s[x,u]+s[r,y])
m += s[x,y]
end
end
energy[i] = en
magnetization[i] = abs(m)
end
function sweep(n,T)
for i = 1:n
for x = 1:L
for y = 1:L
flip(x,y,T)
end
end
end
end
function flip(x,y,T)
u = 1+mod(y,L)
d = 1+mod(y-2,L)
r = 1+mod(x,L)
l = 1+mod(x-2,L)
de = 2*s[x,y]*(s[x,u]+s[x,d]+s[l,y]+s[r,y])
if (de < 0)
s[x,y] = -s[x,y]
else
p = rand()
if (p < exp(-de/T))
s[x,y] = -s[x,y]
end
end
end
for T in temps
sweep(n_therm, T)
energy = e1
magnetization = m1
for i = 1:n_data
sweep(n_sweep, T)
measure(i)
end
en1 = sum(energy)/n_data
ma1 = sum(magnetization)/n_data
push!(et,en1/(L*L))
push!(mt,ma1/(L*L))
@printf("%8.3f %8.3f n", en1/(L*L), ma1/(L*L))
end
plot(temps,mt)
performance beginner julia
asked 3 mins ago
KaiKai
1012
New contributor
Kai is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I'm just starting to learn Julia, I work primarily in physics and am used to writing most of my code in Fortran90 and occasionally Python for Tensorflow (also Mathematica but that's less relevant). Julia has been recommended to me and I started checking it out; I like it a lot in theory as a middle ground between the speed of Fortran and the syntax of Python. To test it out I wrote a simple 2D Ising model code implementing a basic single-spin-flip Metropolis Monte Carlo algorithm. However, this code runs very slowly compared to an equivalent code in Fortran. Am I doing something wrong which is significantly affecting the performance of the code? I know almost nothing beyond what I've done here. I am using the Juno IDE in Atom on Windows 10. As an aside, I would also like to know how I can make multiple plots in the Atom plot tab, but that's secondary.
using Printf
using Plots
L = 20
n_sweep = 20
n_therm = 1000
n_data = 100
temps = 4.0:-0.3:0.1
e1 = Array(1:n_data)
m1 = Array(1:n_data)
et =
mt =
energy = e1
magnetization = m1
s = ones(Int32,L,L)
function measure(i)
en = 0
m = 0
for x = 1:L
for y = 1:L
u = 1+mod(y,L)
r = 1+mod(x,L)
en -= s[x,y]*(s[x,u]+s[r,y])
m += s[x,y]
end
end
energy[i] = en
magnetization[i] = abs(m)
end
function sweep(n,T)
for i = 1:n
for x = 1:L
for y = 1:L
flip(x,y,T)
end
end
end
end
function flip(x,y,T)
u = 1+mod(y,L)
d = 1+mod(y-2,L)
r = 1+mod(x,L)
l = 1+mod(x-2,L)
de = 2*s[x,y]*(s[x,u]+s[x,d]+s[l,y]+s[r,y])
if (de < 0)
s[x,y] = -s[x,y]
else
p = rand()
if (p < exp(-de/T))
s[x,y] = -s[x,y]
end
end
end
for T in temps
sweep(n_therm, T)
energy = e1
magnetization = m1
for i = 1:n_data
sweep(n_sweep, T)
measure(i)
end
en1 = sum(energy)/n_data
ma1 = sum(magnetization)/n_data
push!(et,en1/(L*L))
push!(mt,ma1/(L*L))
@printf("%8.3f %8.3f n", en1/(L*L), ma1/(L*L))
end
plot(temps,mt)
performance beginner julia
performance beginner julia
asked 3 mins ago
KaiKai
1012
New contributor
Kai is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 3 mins ago
KaiKai
1012
New contributor
Kai is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 3 mins ago
KaiKai
1012
New contributor
Kai is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 3 mins ago
KaiKai
1012
asked 3 mins ago
KaiKai
1012
1012
New contributor
Kai is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Kai is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Kai is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
add a comment |
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