Optimizing my 2D Ising model code in Julia The 2019 Stack Overflow Developer Survey Results...
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Optimizing my 2D Ising model code in Julia
The 2019 Stack Overflow Developer Survey Results Are In
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern)Neural Network in JuliaJulia RPN calculator algorithm ported from Python versionKernel density regression in JuliaImplementing cat function in JuliaComputing the double Integral using MonteCarlo techniques using JuliaSearch algorithms in juliaSort Algorithms in JuliaParsing simple log files in Julia vs PythonWeighted logistic regression in JuliaWebSocket based API library 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 24 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 24 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 24 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 24 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 24 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 24 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 24 mins ago
KaiKai
1012
asked 24 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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