# OutOfMemoryError in DMRG

**URL:** <https://itensor.discourse.group/t/outofmemoryerror-in-dmrg/1859>\
**Category:** DMRG and Numerical Methods\
**Tags:** julia, dmrg\
**Created:** [July 7, 2024, 6:23pm UTC](https://itensor.discourse.group/t/outofmemoryerror-in-dmrg/1859 "2024-07-07T18:23:46Z")\
**Posts on this page:** 5\
**Page:** 1

<div class="post-metadata">

**Author:** ![yedi](https://avatars.discourse-cdn.com/v4/letter/y/4da419/32.png) [@yedi](https://itensor.discourse.group/u/yedi)\
**Post date:** [July 7, 2024, 6:23pm UTC](https://itensor.discourse.group/t/outofmemoryerror-in-dmrg/1859/1 "2024-07-07T18:23:46Z")

</div>

Hi, dear developers

Recently, I used Itensor (Julia) to calculate the ground state energy of the two-dimensional Hubbard model. This system has 27 sites, so the filled electrons can be set to 1, 2, …, 53. In addition, the total z-component spin is not a good quantum number due to the Rashba-SOC term in the Hamiltonian. Unfortunately, due to memory limitations on my computer cluster, I could only calculate up to 7 filled electrons. Please forgive me as I am a new user of julia and Itensor. Is there some way to optimize the memory and increase the maximum number of filled electrons?  
Furthermore, [the reference article (FIG. 16)](https://journals.aps.org/prb/abstract/10.1103/PhysRevB.102.045105) claims that 48 sites with 24 filled electrons can be achieved.

My code is following:

```auto
using Distributed
addprocs(2)
@show nprocs()
@everywhere using Dates, DelimitedFiles, SharedArrays, ITensors, ITensorMPS

let
U = 0.5;
transNew = readdlm("transNew_27.txt");
N = length(union(transNew[:,1])); #number of sites: 27

####################################################################
nsweeps = 40;
maxdim = [20, 60, 80, 100, 200, 400, 600, 800, 1000, 2000, 3000, 6000, 8000, 10000, 20000, 30000, 50000, 100000];
cutoff = [1E-8];
noise = [1E-7, 1E-8, 1E-10, 0.];

####################################################################
sites = siteinds("Electron", N ; conserve_qns=true, conserve_sz=false);
lattice = Lattice(undef, size(transNew)[1]);
for i in range(start = 1 , step = 1 , stop = size(transNew)[1])
    lattice[i] = LatticeBond(convert(Int, transNew[i,1]), convert(Int, transNew[i,5]), transNew[i,2], transNew[i,3], transNew[i,6], transNew[i,7])
end

####################################################################
os = OpSum()
for (i,b) in enumerate(lattice)
    os -= transNew[i,9]+1im * transNew[i,10], (convert(Int, transNew[i,4]) == 0 ? "Cdagup" : "Cdagdn"), b.s1, (convert(Int, transNew[i,8]) == 0 ? "Cup" : "Cdn"), b.s2;
end
for n in 1:N
    os += U, "Nupdn", n;
end
H = MPO(os, sites);

@sync @distributed for indexx in range(start = 1 , step = 1 , stop = 53)
result = zeros(Float64, 2*N-1);
beginTime = Dates.Time(Dates.now());

Npart = indexx; #number of particles
p = Npart;
state = Vector{String}(undef, N);
for i in range(start=N, step=-1, stop=1)
	if p > i
		# println("Doubly occupying site ",i);
		state[i] = "UpDn";
		p -= 2;
	elseif p > 0
		# println("Singly occupying site ",i);
		state[i] = (isodd(i) ? "Up" : "Dn");
		p -= 1;
	else
		state[i] = "Emp";
	end
end

####################################################################
psi0 = random_mps(sites, state);
t1 = @elapsed begin
    energy, psi = dmrg(H, psi0; nsweeps, maxdim, cutoff, noise);
    end
result[indexx] = energy;
endTime = Dates.Time(Dates.now());
println("sites = $N, filled electons = $indexx, \t begin at $beginTime, \t end at $endTime, \t DMRG costs time: $t1");
writedlm("dataFile_$indexx.txt", result);
end
end

```

**transNew\_27.txt** contains information about the lattice and hopping parameters (periodic boundary condition in x direction, open boundary condition in y condition) as shown below:

```auto
1	-0.5	-0.2886751345948129	0	12	2.	0.	0	1.	0.
1	-0.5	-0.2886751345948129	0	12	2.	0.	1	-0.17320508075688776	-0.10000000000000003
1	-0.5	-0.2886751345948129	0	10	0.	0.	0	1.	0.
1	-0.5	-0.2886751345948129	0	10	0.	0.	1	0.17320508075688776	-0.10000000000000003
1	-0.5	-0.2886751345948129	1	12	2.	0.	0	0.17320508075688776	-0.10000000000000003
1	-0.5	-0.2886751345948129	1	12	2.	0.	1	1.	0.
1	-0.5	-0.2886751345948129	1	10	0.	0.	0	-0.17320508075688776	-0.10000000000000003
1	-0.5	-0.2886751345948129	1	10	0.	0.	1	1.	0.
2	0.5	-0.2886751345948129	0	10	0.	0.	0	1.	0.
2	0.5	-0.2886751345948129	0	10	0.	0.	1	-0.17320508075688776	-0.10000000000000003
2	0.5	-0.2886751345948129	0	11	1.	0.	0	1.	0.
2	0.5	-0.2886751345948129	0	11	1.	0.	1	0.17320508075688776	-0.10000000000000003
2	0.5	-0.2886751345948129	1	10	0.	0.	0	0.17320508075688776	-0.10000000000000003
2	0.5	-0.2886751345948129	1	10	0.	0.	1	1.	0.
2	0.5	-0.2886751345948129	1	11	1.	0.	0	-0.17320508075688776	-0.10000000000000003
2	0.5	-0.2886751345948129	1	11	1.	0.	1	1.	0.
3	1.5	-0.2886751345948129	0	11	1.	0.	0	1.	0.
3	1.5	-0.2886751345948129	0	11	1.	0.	1	-0.17320508075688776	-0.10000000000000003
3	1.5	-0.2886751345948129	0	12	2.	0.	0	1.	0.
3	1.5	-0.2886751345948129	0	12	2.	0.	1	0.17320508075688776	-0.10000000000000003
3	1.5	-0.2886751345948129	1	11	1.	0.	0	0.17320508075688776	-0.10000000000000003
3	1.5	-0.2886751345948129	1	11	1.	0.	1	1.	0.
3	1.5	-0.2886751345948129	1	12	2.	0.	0	-0.17320508075688776	-0.10000000000000003
3	1.5	-0.2886751345948129	1	12	2.	0.	1	1.	0.
4	0.	0.5773502691896257	0	15	2.5	0.8660254037844386	0	1.	0.
4	0.	0.5773502691896257	0	15	2.5	0.8660254037844386	1	-0.17320508075688776	-0.1
4	0.	0.5773502691896257	0	10	0.	0.	0	1.	0.
4	0.	0.5773502691896257	0	10	0.	0.	1	0.	0.2
4	0.	0.5773502691896257	0	13	0.5	0.8660254037844386	0	1.	0.
4	0.	0.5773502691896257	0	13	0.5	0.8660254037844386	1	0.17320508075688776	-0.1
4	0.	0.5773502691896257	1	15	2.5	0.8660254037844386	0	0.17320508075688776	-0.1
4	0.	0.5773502691896257	1	15	2.5	0.8660254037844386	1	1.	0.
4	0.	0.5773502691896257	1	10	0.	0.	0	0.	0.2
4	0.	0.5773502691896257	1	10	0.	0.	1	1.	0.
4	0.	0.5773502691896257	1	13	0.5	0.8660254037844386	0	-0.17320508075688776	-0.1
4	0.	0.5773502691896257	1	13	0.5	0.8660254037844386	1	1.	0.
5	1.	0.5773502691896257	0	11	1.	0.	0	1.	0.
5	1.	0.5773502691896257	0	11	1.	0.	1	0.	0.2
5	1.	0.5773502691896257	0	13	0.5	0.8660254037844386	0	1.	0.
5	1.	0.5773502691896257	0	13	0.5	0.8660254037844386	1	-0.17320508075688776	-0.1
5	1.	0.5773502691896257	0	14	1.5	0.8660254037844386	0	1.	0.
5	1.	0.5773502691896257	0	14	1.5	0.8660254037844386	1	0.17320508075688776	-0.1
5	1.	0.5773502691896257	1	11	1.	0.	0	0.	0.2
5	1.	0.5773502691896257	1	11	1.	0.	1	1.	0.
5	1.	0.5773502691896257	1	13	0.5	0.8660254037844386	0	0.17320508075688776	-0.1
5	1.	0.5773502691896257	1	13	0.5	0.8660254037844386	1	1.	0.
5	1.	0.5773502691896257	1	14	1.5	0.8660254037844386	0	-0.17320508075688776	-0.1
5	1.	0.5773502691896257	1	14	1.5	0.8660254037844386	1	1.	0.
6	2.	0.5773502691896257	0	12	2.	0.	0	1.	0.
6	2.	0.5773502691896257	0	12	2.	0.	1	0.	0.2
6	2.	0.5773502691896257	0	14	1.5	0.8660254037844386	0	1.	0.
6	2.	0.5773502691896257	0	14	1.5	0.8660254037844386	1	-0.17320508075688776	-0.1
6	2.	0.5773502691896257	0	15	2.5	0.8660254037844386	0	1.	0.
6	2.	0.5773502691896257	0	15	2.5	0.8660254037844386	1	0.17320508075688776	-0.1
6	2.	0.5773502691896257	1	12	2.	0.	0	0.	0.2
6	2.	0.5773502691896257	1	12	2.	0.	1	1.	0.
6	2.	0.5773502691896257	1	14	1.5	0.8660254037844386	0	0.17320508075688776	-0.1
6	2.	0.5773502691896257	1	14	1.5	0.8660254037844386	1	1.	0.
6	2.	0.5773502691896257	1	15	2.5	0.8660254037844386	0	-0.17320508075688776	-0.1
6	2.	0.5773502691896257	1	15	2.5	0.8660254037844386	1	1.	0.
7	0.5	1.4433756729740643	0	18	3.	1.7320508075688772	0	1.	0.
7	0.5	1.4433756729740643	0	18	3.	1.7320508075688772	1	-0.17320508075688776	-0.1
7	0.5	1.4433756729740643	0	13	0.5	0.8660254037844386	0	1.	0.
7	0.5	1.4433756729740643	0	13	0.5	0.8660254037844386	1	0.	0.2
7	0.5	1.4433756729740643	0	16	1.	1.7320508075688772	0	1.	0.
7	0.5	1.4433756729740643	0	16	1.	1.7320508075688772	1	0.17320508075688776	-0.1
7	0.5	1.4433756729740643	1	18	3.	1.7320508075688772	0	0.17320508075688776	-0.1
7	0.5	1.4433756729740643	1	18	3.	1.7320508075688772	1	1.	0.
7	0.5	1.4433756729740643	1	13	0.5	0.8660254037844386	0	0.	0.2
7	0.5	1.4433756729740643	1	13	0.5	0.8660254037844386	1	1.	0.
7	0.5	1.4433756729740643	1	16	1.	1.7320508075688772	0	-0.17320508075688776	-0.1
7	0.5	1.4433756729740643	1	16	1.	1.7320508075688772	1	1.	0.
8	1.5	1.4433756729740643	0	14	1.5	0.8660254037844386	0	1.	0.
8	1.5	1.4433756729740643	0	14	1.5	0.8660254037844386	1	0.	0.2
8	1.5	1.4433756729740643	0	16	1.	1.7320508075688772	0	1.	0.
8	1.5	1.4433756729740643	0	16	1.	1.7320508075688772	1	-0.17320508075688776	-0.1
8	1.5	1.4433756729740643	0	17	2.	1.7320508075688772	0	1.	0.
8	1.5	1.4433756729740643	0	17	2.	1.7320508075688772	1	0.17320508075688776	-0.1
8	1.5	1.4433756729740643	1	14	1.5	0.8660254037844386	0	0.	0.2
8	1.5	1.4433756729740643	1	14	1.5	0.8660254037844386	1	1.	0.
8	1.5	1.4433756729740643	1	16	1.	1.7320508075688772	0	0.17320508075688776	-0.1
8	1.5	1.4433756729740643	1	16	1.	1.7320508075688772	1	1.	0.
8	1.5	1.4433756729740643	1	17	2.	1.7320508075688772	0	-0.17320508075688776	-0.1
8	1.5	1.4433756729740643	1	17	2.	1.7320508075688772	1	1.	0.
9	2.5	1.4433756729740643	0	15	2.5	0.8660254037844386	0	1.	0.
9	2.5	1.4433756729740643	0	15	2.5	0.8660254037844386	1	0.	0.2
9	2.5	1.4433756729740643	0	17	2.	1.7320508075688772	0	1.	0.
9	2.5	1.4433756729740643	0	17	2.	1.7320508075688772	1	-0.17320508075688776	-0.1
9	2.5	1.4433756729740643	0	18	3.	1.7320508075688772	0	1.	0.
9	2.5	1.4433756729740643	0	18	3.	1.7320508075688772	1	0.17320508075688776	-0.1
9	2.5	1.4433756729740643	1	15	2.5	0.8660254037844386	0	0.	0.2
9	2.5	1.4433756729740643	1	15	2.5	0.8660254037844386	1	1.	0.
9	2.5	1.4433756729740643	1	17	2.	1.7320508075688772	0	0.17320508075688776	-0.1
9	2.5	1.4433756729740643	1	17	2.	1.7320508075688772	1	1.	0.
9	2.5	1.4433756729740643	1	18	3.	1.7320508075688772	0	-0.17320508075688776	-0.1
9	2.5	1.4433756729740643	1	18	3.	1.7320508075688772	1	1.	0.
10	0.	0.	0	21	2.5	0.2886751345948129	0	1.	0.
10	0.	0.	0	21	2.5	0.2886751345948129	1	0.17320508075688776	0.10000000000000003
10	0.	0.	0	4	0.	0.5773502691896257	0	1.	0.
10	0.	0.	0	4	0.	0.5773502691896257	1	0.	-0.2
10	0.	0.	0	1	-0.5	-0.2886751345948129	0	1.	0.
10	0.	0.	0	1	-0.5	-0.2886751345948129	1	-0.17320508075688776	0.10000000000000003
10	0.	0.	0	2	0.5	-0.2886751345948129	0	1.	0.
10	0.	0.	0	2	0.5	-0.2886751345948129	1	0.17320508075688776	0.10000000000000003
10	0.	0.	0	19	0.5	0.2886751345948129	0	1.	0.
10	0.	0.	0	19	0.5	0.2886751345948129	1	-0.17320508075688776	0.10000000000000003
10	0.	0.	0	10	0.	0.	0	0.6	0.
10	0.	0.	1	21	2.5	0.2886751345948129	0	-0.17320508075688776	0.10000000000000003
10	0.	0.	1	21	2.5	0.2886751345948129	1	1.	0.
10	0.	0.	1	4	0.	0.5773502691896257	0	0.	-0.2
10	0.	0.	1	4	0.	0.5773502691896257	1	1.	0.
10	0.	0.	1	1	-0.5	-0.2886751345948129	0	0.17320508075688776	0.10000000000000003
10	0.	0.	1	1	-0.5	-0.2886751345948129	1	1.	0.
10	0.	0.	1	2	0.5	-0.2886751345948129	0	-0.17320508075688776	0.10000000000000003
10	0.	0.	1	2	0.5	-0.2886751345948129	1	1.	0.
10	0.	0.	1	19	0.5	0.2886751345948129	0	0.17320508075688776	0.10000000000000003
10	0.	0.	1	19	0.5	0.2886751345948129	1	1.	0.
10	0.	0.	1	10	0.	0.	1	0.6	0.
11	1.	0.	0	5	1.	0.5773502691896257	0	1.	0.
11	1.	0.	0	5	1.	0.5773502691896257	1	0.	-0.2
11	1.	0.	0	2	0.5	-0.2886751345948129	0	1.	0.
11	1.	0.	0	2	0.5	-0.2886751345948129	1	-0.17320508075688776	0.10000000000000003
11	1.	0.	0	3	1.5	-0.2886751345948129	0	1.	0.
11	1.	0.	0	3	1.5	-0.2886751345948129	1	0.17320508075688776	0.10000000000000003
11	1.	0.	0	19	0.5	0.2886751345948129	0	1.	0.
11	1.	0.	0	19	0.5	0.2886751345948129	1	0.17320508075688776	0.10000000000000003
11	1.	0.	0	20	1.5	0.2886751345948129	0	1.	0.
11	1.	0.	0	20	1.5	0.2886751345948129	1	-0.17320508075688776	0.10000000000000003
11	1.	0.	0	11	1.	0.	0	0.6	0.
11	1.	0.	1	5	1.	0.5773502691896257	0	0.	-0.2
11	1.	0.	1	5	1.	0.5773502691896257	1	1.	0.
11	1.	0.	1	2	0.5	-0.2886751345948129	0	0.17320508075688776	0.10000000000000003
11	1.	0.	1	2	0.5	-0.2886751345948129	1	1.	0.
11	1.	0.	1	3	1.5	-0.2886751345948129	0	-0.17320508075688776	0.10000000000000003
11	1.	0.	1	3	1.5	-0.2886751345948129	1	1.	0.
11	1.	0.	1	19	0.5	0.2886751345948129	0	-0.17320508075688776	0.10000000000000003
11	1.	0.	1	19	0.5	0.2886751345948129	1	1.	0.
11	1.	0.	1	20	1.5	0.2886751345948129	0	0.17320508075688776	0.10000000000000003
11	1.	0.	1	20	1.5	0.2886751345948129	1	1.	0.
11	1.	0.	1	11	1.	0.	1	0.6	0.
12	2.	0.	0	1	-0.5	-0.2886751345948129	0	1.	0.
12	2.	0.	0	1	-0.5	-0.2886751345948129	1	0.17320508075688776	0.10000000000000003
12	2.	0.	0	6	2.	0.5773502691896257	0	1.	0.
12	2.	0.	0	6	2.	0.5773502691896257	1	0.	-0.2
12	2.	0.	0	3	1.5	-0.2886751345948129	0	1.	0.
12	2.	0.	0	3	1.5	-0.2886751345948129	1	-0.17320508075688776	0.10000000000000003
12	2.	0.	0	20	1.5	0.2886751345948129	0	1.	0.
12	2.	0.	0	20	1.5	0.2886751345948129	1	0.17320508075688776	0.10000000000000003
12	2.	0.	0	21	2.5	0.2886751345948129	0	1.	0.
12	2.	0.	0	21	2.5	0.2886751345948129	1	-0.17320508075688776	0.10000000000000003
12	2.	0.	0	12	2.	0.	0	0.6	0.
12	2.	0.	1	1	-0.5	-0.2886751345948129	0	-0.17320508075688776	0.10000000000000003
12	2.	0.	1	1	-0.5	-0.2886751345948129	1	1.	0.
12	2.	0.	1	6	2.	0.5773502691896257	0	0.	-0.2
12	2.	0.	1	6	2.	0.5773502691896257	1	1.	0.
12	2.	0.	1	3	1.5	-0.2886751345948129	0	0.17320508075688776	0.10000000000000003
12	2.	0.	1	3	1.5	-0.2886751345948129	1	1.	0.
12	2.	0.	1	20	1.5	0.2886751345948129	0	-0.17320508075688776	0.10000000000000003
12	2.	0.	1	20	1.5	0.2886751345948129	1	1.	0.
12	2.	0.	1	21	2.5	0.2886751345948129	0	0.17320508075688776	0.10000000000000003
12	2.	0.	1	21	2.5	0.2886751345948129	1	1.	0.
12	2.	0.	1	12	2.	0.	1	0.6	0.
13	0.5	0.8660254037844386	0	24	3.	1.1547005383792515	0	1.	0.
13	0.5	0.8660254037844386	0	24	3.	1.1547005383792515	1	0.17320508075688776	0.1
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13	0.5	0.8660254037844386	0	5	1.	0.5773502691896257	1	0.17320508075688776	0.1
13	0.5	0.8660254037844386	0	7	0.5	1.4433756729740643	0	1.	0.
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13	0.5	0.8660254037844386	0	19	0.5	0.2886751345948129	0	1.	0.
13	0.5	0.8660254037844386	0	19	0.5	0.2886751345948129	1	0.	-0.2
13	0.5	0.8660254037844386	0	22	1.	1.1547005383792515	0	1.	0.
13	0.5	0.8660254037844386	0	22	1.	1.1547005383792515	1	-0.17320508075688776	0.1
13	0.5	0.8660254037844386	0	13	0.5	0.8660254037844386	0	0.6	0.
13	0.5	0.8660254037844386	1	24	3.	1.1547005383792515	0	-0.17320508075688776	0.1
13	0.5	0.8660254037844386	1	24	3.	1.1547005383792515	1	1.	0.
13	0.5	0.8660254037844386	1	4	0.	0.5773502691896257	0	0.17320508075688776	0.1
13	0.5	0.8660254037844386	1	4	0.	0.5773502691896257	1	1.	0.
13	0.5	0.8660254037844386	1	5	1.	0.5773502691896257	0	-0.17320508075688776	0.1
13	0.5	0.8660254037844386	1	5	1.	0.5773502691896257	1	1.	0.
13	0.5	0.8660254037844386	1	7	0.5	1.4433756729740643	0	0.	-0.2
13	0.5	0.8660254037844386	1	7	0.5	1.4433756729740643	1	1.	0.
13	0.5	0.8660254037844386	1	19	0.5	0.2886751345948129	0	0.	-0.2
13	0.5	0.8660254037844386	1	19	0.5	0.2886751345948129	1	1.	0.
13	0.5	0.8660254037844386	1	22	1.	1.1547005383792515	0	0.17320508075688776	0.1
13	0.5	0.8660254037844386	1	22	1.	1.1547005383792515	1	1.	0.
13	0.5	0.8660254037844386	1	13	0.5	0.8660254037844386	1	0.6	0.
14	1.5	0.8660254037844386	0	5	1.	0.5773502691896257	0	1.	0.
14	1.5	0.8660254037844386	0	5	1.	0.5773502691896257	1	-0.17320508075688776	0.1
14	1.5	0.8660254037844386	0	6	2.	0.5773502691896257	0	1.	0.
14	1.5	0.8660254037844386	0	6	2.	0.5773502691896257	1	0.17320508075688776	0.1
14	1.5	0.8660254037844386	0	8	1.5	1.4433756729740643	0	1.	0.
14	1.5	0.8660254037844386	0	8	1.5	1.4433756729740643	1	0.	-0.2
14	1.5	0.8660254037844386	0	20	1.5	0.2886751345948129	0	1.	0.
14	1.5	0.8660254037844386	0	20	1.5	0.2886751345948129	1	0.	-0.2
14	1.5	0.8660254037844386	0	22	1.	1.1547005383792515	0	1.	0.
14	1.5	0.8660254037844386	0	22	1.	1.1547005383792515	1	0.17320508075688776	0.1
14	1.5	0.8660254037844386	0	23	2.	1.1547005383792515	0	1.	0.
14	1.5	0.8660254037844386	0	23	2.	1.1547005383792515	1	-0.17320508075688776	0.1
14	1.5	0.8660254037844386	0	14	1.5	0.8660254037844386	0	0.6	0.
14	1.5	0.8660254037844386	1	5	1.	0.5773502691896257	0	0.17320508075688776	0.1
14	1.5	0.8660254037844386	1	5	1.	0.5773502691896257	1	1.	0.
14	1.5	0.8660254037844386	1	6	2.	0.5773502691896257	0	-0.17320508075688776	0.1
14	1.5	0.8660254037844386	1	6	2.	0.5773502691896257	1	1.	0.
14	1.5	0.8660254037844386	1	8	1.5	1.4433756729740643	0	0.	-0.2
14	1.5	0.8660254037844386	1	8	1.5	1.4433756729740643	1	1.	0.
14	1.5	0.8660254037844386	1	20	1.5	0.2886751345948129	0	0.	-0.2
14	1.5	0.8660254037844386	1	20	1.5	0.2886751345948129	1	1.	0.
14	1.5	0.8660254037844386	1	22	1.	1.1547005383792515	0	-0.17320508075688776	0.1
14	1.5	0.8660254037844386	1	22	1.	1.1547005383792515	1	1.	0.
14	1.5	0.8660254037844386	1	23	2.	1.1547005383792515	0	0.17320508075688776	0.1
14	1.5	0.8660254037844386	1	23	2.	1.1547005383792515	1	1.	0.
14	1.5	0.8660254037844386	1	14	1.5	0.8660254037844386	1	0.6	0.
15	2.5	0.8660254037844386	0	4	0.	0.5773502691896257	0	1.	0.
15	2.5	0.8660254037844386	0	4	0.	0.5773502691896257	1	0.17320508075688776	0.1
15	2.5	0.8660254037844386	0	6	2.	0.5773502691896257	0	1.	0.
15	2.5	0.8660254037844386	0	6	2.	0.5773502691896257	1	-0.17320508075688776	0.1
15	2.5	0.8660254037844386	0	9	2.5	1.4433756729740643	0	1.	0.
15	2.5	0.8660254037844386	0	9	2.5	1.4433756729740643	1	0.	-0.2
15	2.5	0.8660254037844386	0	21	2.5	0.2886751345948129	0	1.	0.
15	2.5	0.8660254037844386	0	21	2.5	0.2886751345948129	1	0.	-0.2
15	2.5	0.8660254037844386	0	23	2.	1.1547005383792515	0	1.	0.
15	2.5	0.8660254037844386	0	23	2.	1.1547005383792515	1	0.17320508075688776	0.1
15	2.5	0.8660254037844386	0	24	3.	1.1547005383792515	0	1.	0.
15	2.5	0.8660254037844386	0	24	3.	1.1547005383792515	1	-0.17320508075688776	0.1
15	2.5	0.8660254037844386	0	15	2.5	0.8660254037844386	0	0.6	0.
15	2.5	0.8660254037844386	1	4	0.	0.5773502691896257	0	-0.17320508075688776	0.1
15	2.5	0.8660254037844386	1	4	0.	0.5773502691896257	1	1.	0.
15	2.5	0.8660254037844386	1	6	2.	0.5773502691896257	0	0.17320508075688776	0.1
15	2.5	0.8660254037844386	1	6	2.	0.5773502691896257	1	1.	0.
15	2.5	0.8660254037844386	1	9	2.5	1.4433756729740643	0	0.	-0.2
15	2.5	0.8660254037844386	1	9	2.5	1.4433756729740643	1	1.	0.
15	2.5	0.8660254037844386	1	21	2.5	0.2886751345948129	0	0.	-0.2
15	2.5	0.8660254037844386	1	21	2.5	0.2886751345948129	1	1.	0.
15	2.5	0.8660254037844386	1	23	2.	1.1547005383792515	0	-0.17320508075688776	0.1
15	2.5	0.8660254037844386	1	23	2.	1.1547005383792515	1	1.	0.
15	2.5	0.8660254037844386	1	24	3.	1.1547005383792515	0	0.17320508075688776	0.1
15	2.5	0.8660254037844386	1	24	3.	1.1547005383792515	1	1.	0.
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16	1.	1.7320508075688772	0	27	3.5	2.0207259421636903	1	0.1732050807568877	0.10000000000000005
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16	1.	1.7320508075688772	0	7	0.5	1.4433756729740643	1	-0.17320508075688776	0.1
16	1.	1.7320508075688772	0	8	1.5	1.4433756729740643	0	1.	0.
16	1.	1.7320508075688772	0	8	1.5	1.4433756729740643	1	0.17320508075688776	0.1
16	1.	1.7320508075688772	0	22	1.	1.1547005383792515	0	1.	0.
16	1.	1.7320508075688772	0	22	1.	1.1547005383792515	1	0.	-0.2
16	1.	1.7320508075688772	0	25	1.5	2.0207259421636903	0	1.	0.
16	1.	1.7320508075688772	0	25	1.5	2.0207259421636903	1	-0.1732050807568877	0.10000000000000005
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16	1.	1.7320508075688772	1	27	3.5	2.0207259421636903	1	1.	0.
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16	1.	1.7320508075688772	1	7	0.5	1.4433756729740643	1	1.	0.
16	1.	1.7320508075688772	1	8	1.5	1.4433756729740643	0	-0.17320508075688776	0.1
16	1.	1.7320508075688772	1	8	1.5	1.4433756729740643	1	1.	0.
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16	1.	1.7320508075688772	1	22	1.	1.1547005383792515	1	1.	0.
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17	2.	1.7320508075688772	0	8	1.5	1.4433756729740643	1	-0.17320508075688776	0.1
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17	2.	1.7320508075688772	0	9	2.5	1.4433756729740643	1	0.17320508075688776	0.1
17	2.	1.7320508075688772	0	23	2.	1.1547005383792515	0	1.	0.
17	2.	1.7320508075688772	0	23	2.	1.1547005383792515	1	0.	-0.2
17	2.	1.7320508075688772	0	25	1.5	2.0207259421636903	0	1.	0.
17	2.	1.7320508075688772	0	25	1.5	2.0207259421636903	1	0.1732050807568877	0.10000000000000005
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17	2.	1.7320508075688772	0	26	2.5	2.0207259421636903	1	-0.1732050807568877	0.10000000000000005
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17	2.	1.7320508075688772	1	8	1.5	1.4433756729740643	0	0.17320508075688776	0.1
17	2.	1.7320508075688772	1	8	1.5	1.4433756729740643	1	1.	0.
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17	2.	1.7320508075688772	1	9	2.5	1.4433756729740643	1	1.	0.
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17	2.	1.7320508075688772	1	23	2.	1.1547005383792515	1	1.	0.
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17	2.	1.7320508075688772	1	25	1.5	2.0207259421636903	1	1.	0.
17	2.	1.7320508075688772	1	26	2.5	2.0207259421636903	0	0.1732050807568877	0.10000000000000005
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18	3.	1.7320508075688772	0	7	0.5	1.4433756729740643	1	0.17320508075688776	0.1
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18	3.	1.7320508075688772	0	9	2.5	1.4433756729740643	1	-0.17320508075688776	0.1
18	3.	1.7320508075688772	0	24	3.	1.1547005383792515	0	1.	0.
18	3.	1.7320508075688772	0	24	3.	1.1547005383792515	1	0.	-0.2
18	3.	1.7320508075688772	0	26	2.5	2.0207259421636903	0	1.	0.
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18	3.	1.7320508075688772	1	7	0.5	1.4433756729740643	1	1.	0.
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18	3.	1.7320508075688772	1	9	2.5	1.4433756729740643	1	1.	0.
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18	3.	1.7320508075688772	1	24	3.	1.1547005383792515	1	1.	0.
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20	1.5	0.2886751345948129	0	11	1.	0.	0	1.	0.
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21	2.5	0.2886751345948129	1	15	2.5	0.8660254037844386	1	1.	0.
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22	1.	1.1547005383792515	0	13	0.5	0.8660254037844386	1	0.17320508075688776	-0.1
22	1.	1.1547005383792515	0	14	1.5	0.8660254037844386	0	1.	0.
22	1.	1.1547005383792515	0	14	1.5	0.8660254037844386	1	-0.17320508075688776	-0.1
22	1.	1.1547005383792515	0	16	1.	1.7320508075688772	0	1.	0.
22	1.	1.1547005383792515	0	16	1.	1.7320508075688772	1	0.	0.2
22	1.	1.1547005383792515	1	13	0.5	0.8660254037844386	0	-0.17320508075688776	-0.1
22	1.	1.1547005383792515	1	13	0.5	0.8660254037844386	1	1.	0.
22	1.	1.1547005383792515	1	14	1.5	0.8660254037844386	0	0.17320508075688776	-0.1
22	1.	1.1547005383792515	1	14	1.5	0.8660254037844386	1	1.	0.
22	1.	1.1547005383792515	1	16	1.	1.7320508075688772	0	0.	0.2
22	1.	1.1547005383792515	1	16	1.	1.7320508075688772	1	1.	0.
23	2.	1.1547005383792515	0	14	1.5	0.8660254037844386	0	1.	0.
23	2.	1.1547005383792515	0	14	1.5	0.8660254037844386	1	0.17320508075688776	-0.1
23	2.	1.1547005383792515	0	15	2.5	0.8660254037844386	0	1.	0.
23	2.	1.1547005383792515	0	15	2.5	0.8660254037844386	1	-0.17320508075688776	-0.1
23	2.	1.1547005383792515	0	17	2.	1.7320508075688772	0	1.	0.
23	2.	1.1547005383792515	0	17	2.	1.7320508075688772	1	0.	0.2
23	2.	1.1547005383792515	1	14	1.5	0.8660254037844386	0	-0.17320508075688776	-0.1
23	2.	1.1547005383792515	1	14	1.5	0.8660254037844386	1	1.	0.
23	2.	1.1547005383792515	1	15	2.5	0.8660254037844386	0	0.17320508075688776	-0.1
23	2.	1.1547005383792515	1	15	2.5	0.8660254037844386	1	1.	0.
23	2.	1.1547005383792515	1	17	2.	1.7320508075688772	0	0.	0.2
23	2.	1.1547005383792515	1	17	2.	1.7320508075688772	1	1.	0.
24	3.	1.1547005383792515	0	13	0.5	0.8660254037844386	0	1.	0.
24	3.	1.1547005383792515	0	13	0.5	0.8660254037844386	1	-0.17320508075688776	-0.1
24	3.	1.1547005383792515	0	15	2.5	0.8660254037844386	0	1.	0.
24	3.	1.1547005383792515	0	15	2.5	0.8660254037844386	1	0.17320508075688776	-0.1
24	3.	1.1547005383792515	0	18	3.	1.7320508075688772	0	1.	0.
24	3.	1.1547005383792515	0	18	3.	1.7320508075688772	1	0.	0.2
24	3.	1.1547005383792515	1	13	0.5	0.8660254037844386	0	0.17320508075688776	-0.1
24	3.	1.1547005383792515	1	13	0.5	0.8660254037844386	1	1.	0.
24	3.	1.1547005383792515	1	15	2.5	0.8660254037844386	0	-0.17320508075688776	-0.1
24	3.	1.1547005383792515	1	15	2.5	0.8660254037844386	1	1.	0.
24	3.	1.1547005383792515	1	18	3.	1.7320508075688772	0	0.	0.2
24	3.	1.1547005383792515	1	18	3.	1.7320508075688772	1	1.	0.
25	1.5	2.0207259421636903	0	16	1.	1.7320508075688772	0	1.	0.
25	1.5	2.0207259421636903	0	16	1.	1.7320508075688772	1	0.1732050807568877	-0.10000000000000005
25	1.5	2.0207259421636903	0	17	2.	1.7320508075688772	0	1.	0.
25	1.5	2.0207259421636903	0	17	2.	1.7320508075688772	1	-0.1732050807568877	-0.10000000000000005
25	1.5	2.0207259421636903	1	16	1.	1.7320508075688772	0	-0.1732050807568877	-0.10000000000000005
25	1.5	2.0207259421636903	1	16	1.	1.7320508075688772	1	1.	0.
25	1.5	2.0207259421636903	1	17	2.	1.7320508075688772	0	0.1732050807568877	-0.10000000000000005
25	1.5	2.0207259421636903	1	17	2.	1.7320508075688772	1	1.	0.
26	2.5	2.0207259421636903	0	17	2.	1.7320508075688772	0	1.	0.
26	2.5	2.0207259421636903	0	17	2.	1.7320508075688772	1	0.1732050807568877	-0.10000000000000005
26	2.5	2.0207259421636903	0	18	3.	1.7320508075688772	0	1.	0.
26	2.5	2.0207259421636903	0	18	3.	1.7320508075688772	1	-0.1732050807568877	-0.10000000000000005
26	2.5	2.0207259421636903	1	17	2.	1.7320508075688772	0	-0.1732050807568877	-0.10000000000000005
26	2.5	2.0207259421636903	1	17	2.	1.7320508075688772	1	1.	0.
26	2.5	2.0207259421636903	1	18	3.	1.7320508075688772	0	0.1732050807568877	-0.10000000000000005
26	2.5	2.0207259421636903	1	18	3.	1.7320508075688772	1	1.	0.
27	3.5	2.0207259421636903	0	16	1.	1.7320508075688772	0	1.	0.
27	3.5	2.0207259421636903	0	16	1.	1.7320508075688772	1	-0.1732050807568877	-0.10000000000000005
27	3.5	2.0207259421636903	0	18	3.	1.7320508075688772	0	1.	0.
27	3.5	2.0207259421636903	0	18	3.	1.7320508075688772	1	0.1732050807568877	-0.10000000000000005
27	3.5	2.0207259421636903	1	16	1.	1.7320508075688772	0	0.1732050807568877	-0.10000000000000005
27	3.5	2.0207259421636903	1	16	1.	1.7320508075688772	1	1.	0.
27	3.5	2.0207259421636903	1	18	3.	1.7320508075688772	0	-0.1732050807568877	-0.10000000000000005
27	3.5	2.0207259421636903	1	18	3.	1.7320508075688772	1	1.	0.

```

**Notice** : For safety reasons, we should not load too many CPU cores, and limit the number of filled electrons in the beginning.

```auto
@sync @distributed for indexx in range(start = 1 , step = 1 , stop = 3)

```

Thank you for your patience, I would be very grateful for any suggestions. I am looking to to hearing from you.

Regards,  
Y.D.Shen.

---

<div class="post-metadata">

**Author:** ![ryanlevy](https://yyz2.discourse-cdn.com/free1/user_avatar/itensor.discourse.group/ryanlevy/32/150_2.png) [@ryanlevy](https://itensor.discourse.group/u/ryanlevy)\
**Post date:** [July 7, 2024, 11:58pm UTC](https://itensor.discourse.group/t/outofmemoryerror-in-dmrg/1859/2 "2024-07-07T23:58:29Z")

</div>

Some thoughts:

- See this page: [High-Performance Computing FAQs · ITensors.jl](https://itensor.github.io/ITensors.jl/dev/faq/HPC.html)
- Do you actually need a max dim of 100,000? That seems quite high, especially as the paper you linked had a maxdim of 1,000
  - Also I recommend converging the lower bond dimensions, i.e. doing multiple sweeps before raising the maxdim

- If you have access to enough disk space while running, you can have DMRG output the tensors that are not currently being optimized to disk with the keyword arguments to `dmrg`: `write_when_maxdim_exceeds` and `write_path` . See the docs for [`dmrg`](https://itensor.github.io/ITensors.jl/stable/DMRG.html)
  - This is slow, so it sacrifices time for less memory required
  - This may be complicated using with `@distributed`

- Does the code crash when not using distributed for the hardest problem?

---

<div class="post-metadata">

**Author:** ![yedi](https://avatars.discourse-cdn.com/v4/letter/y/4da419/32.png) [@yedi](https://itensor.discourse.group/u/yedi)\
**Post date:** [July 8, 2024, 4:18pm UTC](https://itensor.discourse.group/t/outofmemoryerror-in-dmrg/1859/3 "2024-07-08T16:18:02Z")

</div>

Thank you very much for your reply.

- After reviewing your first suggestion, I realized that using _distributed for loops_ might lead to excessive memory accumulation that wasn’t released promptly. To address this, I added `GC.gc()` at the start of each _distributed for loop_. Additionally, when running Julia, I appended `julia --heap-size-hint=180G`.
- Regarding your suggestion to perform multiple sweeps before increasing maxdim, should it be set as follows?  
`maxdim = [10, 10, 20, 20, 60, 80, 100, 100, 200, 200, 400, 500, 500, 600, 800, 1000];`  
I’m uncertain why the same `maxdim` values are repeated in many tutorials. Furthermore, when I set `maxdim = 1000` and subsequently observe `maxlinkdim = 1000` after all sweeps, does this imply that `maxdim = 1000` might not be sufficient?
- The code does not crash when not using distributed.

I have three more questions.

1. We set cutoff and nsweeps in DMRG. If the cutoff is reached before all sweeps are completed, can the program be terminated early?
2. And, what the meaning of `maxerr` in the DMRG output?
3. Finally, how can we determine whether our result reaches the cutoff, or if it’s possible that the cutoff isn’t reached due to too few `nsweeps`?

Thank you for your patience. I am looking to to hearing from you.

Regards,  
Y.D.Shen.

---

<div class="post-metadata">

**Author:** ![ryanlevy](https://yyz2.discourse-cdn.com/free1/user_avatar/itensor.discourse.group/ryanlevy/32/150_2.png) [@ryanlevy](https://itensor.discourse.group/u/ryanlevy)\
**Post date:** [July 8, 2024, 6:11pm UTC](https://itensor.discourse.group/t/outofmemoryerror-in-dmrg/1859/4 "2024-07-08T18:11:29Z")

</div>

> [@yedi](#):
>
> I’m uncertain why the same `maxdim` values are repeated in many tutorials

A quick heuristic answer to this - if you can decrease the energy at a smaller `maxdim`, it’ll be faster than a larger `maxdim`. Also e.g. observables may need some time to stabilize due to the linear sweeping, for doing say cutoff extrapolation.

> [@yedi](#):
>
> 1. We set cutoff and nsweeps in DMRG. If the cutoff is reached before all sweeps are completed, can the program be terminated early?
> 2. And, what the meaning of `maxerr` in the DMRG output?
> 3. Finally, how can we determine whether our result reaches the cutoff, or if it’s possible that the cutoff isn’t reached due to too few `nsweeps`?

1. Check out the `checkdone!` method here [Observer System for DMRG · ITensors.jl](https://itensor.github.io/ITensors.jl/stable/Observer.html#Overloading-the-checkdone!-method)  
You would need to determine the criteria for stopping early (for example, checking that the cutoff is reached and/or the energy has stopped decreasing)
2. `maxerr` is the maximum truncation error during a dmrg sweep. The truncation error is the sum of the squares of the “thrown away” singular values during the truncation step inside of dmrg
3. As with 1, you’ll have to set some criteria for considering a calculation to be “converged.” You can always try to extrapolate observables as a function of the cutoff as well

---

<div class="post-metadata">

**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/itensor/original/1X/d3072b13e047cd06df7f3981547c0917d940dfa4.png) [@system](https://itensor.discourse.group/u/system)\
**Post date:** [July 18, 2024, 6:11pm UTC](https://itensor.discourse.group/t/outofmemoryerror-in-dmrg/1859/5 "2024-07-18T18:11:32Z")

</div>

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