diff --git a/benchmarks/Artifacts.toml b/benchmarks/Artifacts.toml
new file mode 100644
index 0000000..f63edc3
--- /dev/null
+++ b/benchmarks/Artifacts.toml
@@ -0,0 +1,15 @@
+[punched_pmn]
+git-tree-sha1 = "f225ac0deac2e86eebd5b79884fa74aae3b0772c"
+lazy = true
+
+    [[punched_pmn.download]]
+    sha256 = "71a26d6799a1f60cc4dd2f56a670c903b8fad483a5752beb85495de55f7d7844"
+    url = "https://github.com/exanauts/CompressedSensingIPM-data/raw/refs/heads/main/punched_pmn.tar.gz"
+
+[z3d_movo]
+git-tree-sha1 = "94c7cdf175da37afc308dd73bb6ab3df58bb5e71"
+lazy = true
+
+    [[z3d_movo.download]]
+    sha256 = "42c844714629e3f5fb0e2fa6a65a1443cc04d90705ccfc7aa736bb30eb9ec6e3"
+    url = "https://github.com/exanauts/CompressedSensingIPM-data/raw/refs/heads/main/z3d_movo.tar.gz"
diff --git a/benchmarks/Manifest.toml b/benchmarks/Manifest.toml
index 131aabd..c9ea01b 100644
--- a/benchmarks/Manifest.toml
+++ b/benchmarks/Manifest.toml
@@ -2,7 +2,7 @@
 
 julia_version = "1.11.5"
 manifest_format = "2.0"
-project_hash = "2666337025370a30df588af12f9e237460cb4cef"
+project_hash = "494b26c5879bbcc087d16564fa42a65dc3279867"
 
 [[deps.AMD]]
 deps = ["LinearAlgebra", "SparseArrays", "SuiteSparse_jll"]
diff --git a/benchmarks/Project.toml b/benchmarks/Project.toml
index 3a0c4e2..7ac4ffa 100644
--- a/benchmarks/Project.toml
+++ b/benchmarks/Project.toml
@@ -7,6 +7,7 @@ DelimitedFiles = "8bb1440f-4735-579b-a4ab-409b98df4dab"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 FFTW = "7a1cc6ca-52ef-59f5-83cd-3a7055c09341"
 LaplaceInterpolation = "afb1b68e-e51f-4e49-a799-0e085170423d"
+LazyArtifacts = "4af54fe1-eca0-43a8-85a7-787d91b784e3"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MadNLP = "2621e9c9-9eb4-46b1-8089-e8c72242dfb6"
 MadNLPGPU = "d72a61cc-809d-412f-99be-fd81f4b8a598"
diff --git a/benchmarks/crystal.jl b/benchmarks/crystal.jl
index bf02889..d368836 100644
--- a/benchmarks/crystal.jl
+++ b/benchmarks/crystal.jl
@@ -3,7 +3,10 @@ using LinearAlgebra, SparseArrays
 using LaplaceInterpolation, NPZ
 using DelimitedFiles
 using MadNLP, MadNLPGPU, CUDA
-using Test
+using Test, LazyArtifacts
+
+include("../test/fft_wei.jl")
+include("../test/punching_centering.jl")
 
 function ipm_solve!(solver::MadNLP.MadNLPSolver)
     MadNLP.print_init(solver)
@@ -27,7 +30,7 @@ function punch_3D_cart(center, radius, x, y, z; linear = false)
     end 
 end
 
-function fft_vishwas(z3d; variant::Bool=false, gpu::Bool=false, rdft::Bool=false)
+function crystal(z3d; variant::Bool=false, gpu::Bool=false, rdft::Bool=false)
     if !variant
         dx = 0.02
         dy = 0.02
@@ -128,8 +131,9 @@ end
 gpu = true
 rdft = false
 variant = true
-z3d = variant ? npzread("../punched_pmn") : npzread("../z3d_movo.npy")
-nlp, solver, results, timer = fft_vishwas(z3d; variant, gpu, rdft)
+path_z3d = variant ? joinpath(artifact"punched_pmn", "punched_pmn.npy") : joinpath(artifact"z3d_movo", "z3d_movo.npy")
+z3d = npzread(path_z3d)
+nlp, solver, results, timer = crystal(z3d; variant, gpu, rdft)
 N = length(results.solution) ÷ 2
 beta_MadNLP = results.solution[1:N]
 println("Timer: $(timer)")