Suite Sparse Matrix Jacobian Factorization Benchmarks
using BenchmarkTools, Random, VectorizationBase, Statistics
using LinearAlgebra, SparseArrays, LinearSolve, Sparspak
# PureUMFPACK backs PureUMFPACKFactorization via LinearSolvePureUMFPACKExt.
# Use `import` (not `using`): PureUMFPACK ≤0.1 exports `solve`, which collides
# with LinearSolve/CommonSolve. PureKLU / SupernodalLU need no extra load.
import PureUMFPACK
import Pardiso
import ParU_jll
using Plots
using MatrixDepot
BenchmarkTools.DEFAULT_PARAMETERS.seconds = 0.5
# Why do I need to set this ?
BenchmarkTools.DEFAULT_PARAMETERS.samples = 10
algs = [
UMFPACKFactorization(),
KLUFactorization(),
PureKLUFactorization(),
PureUMFPACKFactorization(),
SupernodalLUFactorization(),
MKLPardisoFactorize(),
SparspakFactorization(),
ParUFactorization()
]
algnames = ["UMFPACK", "KLU", "PureKLU", "PureUMFPACK", "SupernodalLU",
"Pardiso", "Sparspak", "ParU"]
algnames_transpose = reshape(algnames, 1, length(algnames))
cols = [:red, :blue, :green, :magenta, :turquoise, :orange, :purple, :cyan] # one color per alg
# matrices = ["HB/1138_bus", "HB/494_bus", "HB/662_bus", "HB/685_bus", "HB/bcsstk01", "HB/bcsstk02", "HB/bcsstk03", "HB/bcsstk04", "HB/bcsstk05", "HB/bcsstk06", "HB/bcsstk07", "HB/bcsstk08", "HB/bcsstk09", "HB/bcsstk10", "HB/bcsstk11", "HB/bcsstk12", "HB/bcsstk13", "HB/bcsstk14", "HB/bcsstk15", "HB/bcsstk16"]
#
# Filter on the (already-local) index metadata rather than downloading everything.
# `listnames("*/*")` returns all 2905 remote matrices, and the loop below then
# downloads each one only to discard it via `n > 100 && error(...)`. Applying the
# same n <= 100 bound up front leaves 53 matrices — the exact set that was being
# benchmarked anyway — so no reported number changes, but the document drops from
# ~9 hours (it exceeded the CI runner's limit) to minutes. The `n > 100` guard
# below is kept as a belt-and-suspenders check.
allmatrices_md = listnames("*/*" & @pred(n <= 100))
@info "Total number of matrices: $(allmatrices_md.content[1].rows)"
times = fill(NaN, length(allmatrices_md.content[1].rows), length(algs))
percentage_sparsity = fill(NaN, length(allmatrices_md.content[1].rows))
spaced_out_sparsity = fill(NaN, length(allmatrices_md.content[1].rows))
matrix_size = fill(NaN, length(allmatrices_md.content[1].rows))
bandedness_five = fill(NaN, length(allmatrices_md.content[1].rows))
bandedness_ten = fill(NaN, length(allmatrices_md.content[1].rows))
bandedness_twenty = fill(NaN, length(allmatrices_md.content[1].rows))
function compute_bandedness(A, bandwidth)
n = size(A, 1)
total_band_positions = 0
non_zero_in_band = 0
bandwidth = bandwidth
for r in 1:n
for c in 1:n
if abs(r - c) <= bandwidth
total_band_positions += 1 # This position belongs to the band
if A[r, c] != 0
non_zero_in_band += 1 # This element is non-zero in the band
end
end
end
end
percentage_filled = non_zero_in_band / total_band_positions * 100
return percentage_filled
endcompute_bandedness (generic function with 1 method)for z in 1:length(allmatrices_md.content[1].rows)
try
matrix = allmatrices_md.content[1].rows[z]
matrix = string(matrix[1])
currMTX = matrix
rng = MersenneTwister(123)
A = mdopen(currMTX).A
A = convert(SparseMatrixCSC, A)
n = size(A, 1)
mtx_copy = copy(A)
@info "$n × $n"
n > 100 && error("Skipping too large matrices")
## COMPUTING SPACED OUT SPARSITY
rows, cols = size(mtx_copy)
new_rows = div(rows, 2)
new_cols = div(cols, 2)
condensed = zeros(Int, new_rows, new_cols)
while size(mtx_copy, 1) > 32 || size(mtx_copy, 2) > 32
rows, cols = size(mtx_copy)
new_rows = div(rows, 2)
new_cols = div(cols, 2)
condensed = sparse(zeros(Int, new_rows, new_cols))
for r in 1:2:(rows - 1)
for c in 1:2:(cols - 1)
block = mtx_copy[r:min(r + 1, rows), c:min(c + 1, cols)]
condensed[div(r - 1, 2) + 1, div(c - 1, 2) + 1] = (length(nonzeros(block)) >=
2) ? 1 : 0
end
end
mtx_copy = condensed
end
## COMPUTING FACTORIZATION TIME
b = rand(rng, n)
u0 = rand(rng, n)
for j in 1:length(algs)
bt = @belapsed solve(prob, $(algs[j])).u setup=(prob = LinearProblem(copy($A),
copy($b);
u0 = copy($u0),
alias = LinearAliasSpecifier(alias_A = true, alias_b = true)))
times[z, j] = bt
end
bandedness_five[z] = compute_bandedness(A, 5)
bandedness_ten[z] = compute_bandedness(A, 10)
bandedness_twenty[z] = compute_bandedness(A, 20)
percentage_sparsity[z] = length(nonzeros(A)) / n^2
spaced_out_sparsity[z] = length(nonzeros(mtx_copy)) * percentage_sparsity[z]
matrix_size[z] = n
#=
p = bar(algnames, times[z, :];
ylabel = "Time/s",
yscale = :log10,
title = "Time on $(currMTX)",
fmt = :png,
legend = :outertopright)
display(p)
=#
println("successfully factorized $(currMTX)")
catch e
matrix = allmatrices_md.content[1].rows[z]
matrix = string(matrix[1])
currMTX = matrix
println("$(currMTX) failed to factorize.")
println(e)
end
end
percentage_sparsity = percentage_sparsity[.!isnan.(percentage_sparsity)]
spaced_out_sparsity = spaced_out_sparsity[.!isnan.(spaced_out_sparsity)]
spaced_out_sparsity = replace(spaced_out_sparsity, 0 => 1e-10)
bandedness_five = bandedness_five[.!isnan.(bandedness_five)]
bandedness_five = replace(bandedness_five, 0 => 1e-10)
bandedness_ten = bandedness_ten[.!isnan.(bandedness_ten)]
bandedness_ten = replace(bandedness_ten, 0 => 1e-10)
bandedness_twenty = bandedness_twenty[.!isnan.(bandedness_twenty)]
bandedness_twenty = replace(bandedness_twenty, 0 => 1e-10)
matrix_size = matrix_size[.!isnan.(matrix_size)]
nanrows = any(isnan, times; dims = 2)
times = times[.!vec(nanrows), :]list(156) failed to factorize.
MatrixDepot.DataError("no matrix according to list(156) found")
successfully factorized Bai/bfwa62
successfully factorized Bai/bfwb62
successfully factorized Bai/olm100
successfully factorized Bai/tols90
successfully factorized Bai/tub100
DIMACS10/chesapeake failed to factorize.
FieldError(Nothing, :nzval)
successfully factorized FIDAP/ex5
successfully factorized Grund/b1_ss
successfully factorized Grund/d_dyn
successfully factorized Grund/d_dyn1
successfully factorized Grund/d_ss
HB/ash219 failed to factorize.
ErrorException("Skipping too large matrices")
HB/ash85 failed to factorize.
FieldError(Nothing, :nzval)
HB/bcspwr01 failed to factorize.
FieldError(Nothing, :nzval)
HB/bcspwr02 failed to factorize.
FieldError(Nothing, :nzval)
successfully factorized HB/bcsstk01
successfully factorized HB/bcsstk02
HB/bcsstm01 failed to factorize.
FieldError(Nothing, :nzval)
successfully factorized HB/bcsstm02
HB/can_24 failed to factorize.
FieldError(Nothing, :nzval)
HB/can_61 failed to factorize.
FieldError(Nothing, :nzval)
HB/can_62 failed to factorize.
FieldError(Nothing, :nzval)
HB/can_73 failed to factorize.
FieldError(Nothing, :nzval)
HB/can_96 failed to factorize.
FieldError(Nothing, :nzval)
HB/curtis54 failed to factorize.
FieldError(Nothing, :nzval)
HB/dwt_59 failed to factorize.
FieldError(Nothing, :nzval)
HB/dwt_66 failed to factorize.
FieldError(Nothing, :nzval)
HB/dwt_72 failed to factorize.
FieldError(Nothing, :nzval)
HB/dwt_87 failed to factorize.
FieldError(Nothing, :nzval)
HB/ibm32 failed to factorize.
FieldError(Nothing, :nzval)
successfully factorized HB/impcol_b
HB/jgl009 failed to factorize.
FieldError(Nothing, :nzval)
HB/jgl011 failed to factorize.
FieldError(Nothing, :nzval)
HB/lap_25 failed to factorize.
FieldError(Nothing, :nzval)
successfully factorized HB/nos4
successfully factorized HB/pores_1
HB/rgg010 failed to factorize.
FieldError(Nothing, :nzval)
successfully factorized HB/steam3
successfully factorized HB/west0067
HB/will57 failed to factorize.
FieldError(Nothing, :nzval)
successfully factorized Hamrle/Hamrle1
JGD_BIBD/bibd_9_3 failed to factorize.
FieldError(Nothing, :nzval)
JGD_CAG/CAG_mat72 failed to factorize.
FieldError(Nothing, :nzval)
JGD_G5/IG5-6 failed to factorize.
FieldError(Nothing, :nzval)
JGD_GL7d/GL7d10 failed to factorize.
FieldError(Nothing, :nzval)
JGD_GL7d/GL7d11 failed to factorize.
ErrorException("Skipping too large matrices")
JGD_Homology/ch3-3-b1 failed to factorize.
FieldError(Nothing, :nzval)
JGD_Homology/ch3-3-b2 failed to factorize.
FieldError(Nothing, :nzval)
JGD_Homology/ch4-4-b1 failed to factorize.
FieldError(Nothing, :nzval)
JGD_Homology/ch4-4-b2 failed to factorize.
FieldError(Nothing, :nzval)
JGD_Homology/ch4-4-b3 failed to factorize.
FieldError(Nothing, :nzval)
JGD_Homology/ch5-5-b1 failed to factorize.
ErrorException("Skipping too large matrices")
19×8 Matrix{Float64}:
0.000126659 3.786e-5 4.2549e-5 … 0.000127548 0.000418766
9.3269e-5 3.589e-5 4.558e-5 0.000112698 0.0161476
0.000109869 4.085e-5 3.0389e-5 0.000168058 0.000486915
7.9629e-5 4.396e-5 4.5709e-5 0.000513375 0.016074
0.000116869 4.151e-5 3.7769e-5 0.000159679 0.000544415
5.2389e-5 1.997e-5 2.3099e-5 … 4.4859e-5 0.000214188
1.8319e-5 5.29333e-6 1.0055e-5 2.4739e-5 0.000133758
0.000111319 3.6859e-5 4.923e-5 0.000297477 0.000628004
0.000101069 3.645e-5 5.1789e-5 0.000302237 0.000642144
7.0169e-5 2.4469e-5 2.519e-5 0.000117179 0.000473975
0.000104949 4.6669e-5 4.8409e-5 … 0.000101139 0.000568065
0.000382526 0.000282337 0.000214818 0.000461365 0.00888504
1.7579e-5 9.64e-6 1.524e-5 7.2499e-5 6.536e-5
8.8649e-5 4.401e-5 5.9699e-5 0.000181988 0.000398746
0.000229108 7.27e-5 7.5929e-5 0.000232368 0.000771142
5.0059e-5 1.881e-5 2.8039e-5 … 6.4379e-5 0.000218948
0.000103159 4.2379e-5 5.7489e-5 0.000107179 0.000443876
0.000173749 5.2119e-5 6.2459e-5 0.000229747 0.000606074
5.1849e-5 1.659e-5 2.583e-5 7.3739e-5 0.000243107meantimes = vec(mean(times, dims = 1))
p = bar(algnames, meantimes;
ylabel = "Time/s",
yscale = :log10,
title = "Mean factorization time",
fmt = :png,
legend = :outertopright)
p = scatter(percentage_sparsity, times;
ylabel = "Time/s",
yscale = :log10,
xlabel = "Percentage Sparsity",
xscale = :log10,
label = algnames_transpose,
title = "Factorization Time vs Percentage Sparsity",
fmt = :png,
legend = :outertopright)
p = scatter(matrix_size, times;
ylabel = "Time/s",
yscale = :log10,
xlabel = "Matrix Size",
xscale = :log10,
label = algnames_transpose,
title = "Factorization Time vs Matrix Size",
fmt = :png,
legend = :outertopright)
p = scatter(spaced_out_sparsity, times;
ylabel = "Time/s",
yscale = :log10,
xlabel = "Spaced Out Sparsity",
xscale = :log10,
label = algnames_transpose,
title = "Factorization Time vs Spaced Out Sparsity",
fmt = :png,
legend = :outertopright)
p = scatter(bandedness_five, times;
ylabel = "Time/s",
yscale = :log10,
xlabel = "Bandedness",
xscale = :log10,
label = algnames_transpose,
title = "Factorization Time vs Bandedness, Bandwidth=5",
fmt = :png,
legend = :outertopright)
p = scatter(bandedness_ten, times;
ylabel = "Time/s",
yscale = :log10,
xlabel = "Bandedness",
xscale = :log10,
label = algnames_transpose,
title = "Factorization Time vs Bandedness, Bandwidth=10",
fmt = :png,
legend = :outertopright)
p = scatter(bandedness_twenty, times;
ylabel = "Time/s",
yscale = :log10,
xlabel = "Bandedness",
xscale = :log10,
label = algnames_transpose,
title = "Factorization Time vs Bandedness, Bandwidth=20",
fmt = :png,
legend = :outertopright)
Appendix
Appendix
These benchmarks are a part of the SciMLBenchmarks.jl repository, found at: https://github.com/SciML/SciMLBenchmarks.jl. For more information on high-performance scientific machine learning, check out the SciML Open Source Software Organization https://sciml.ai.
To locally run this benchmark, do the following commands:
using SciMLBenchmarks
SciMLBenchmarks.weave_file("benchmarks/LinearSolve","MatrixDepot.jmd")Computer Information:
Julia Version 1.12.7
Commit 6d172b025e4 (2026-08-15 08:05 UTC)
Build Info:
Official https://julialang.org release
Platform Info:
OS: Linux (x86_64-linux-gnu)
CPU: 128 × AMD EPYC 7502 32-Core Processor
WORD_SIZE: 64
LLVM: libLLVM-18.1.7 (ORCJIT, znver2)
GC: Built with stock GC
Threads: 128 default, 1 interactive, 128 GC (on 128 virtual cores)
Environment:
JULIA_NUM_THREADS = auto
Package Information:
Status `/julia/github-runners/amdci1-1/_work/SciMLBenchmarks.jl/SciMLBenchmarks.jl/benchmarks/LinearSolve/Project.toml`
[6e4b80f9] BenchmarkTools v1.8.0
[29a986be] FastLapackInterface v2.1.1
⌃ [7ed4a6bd] LinearSolve v5.5.0
[b51810bb] MatrixDepot v1.1.0
[46dd5b70] Pardiso v1.1.2
⌃ [91a5bcdd] Plots v1.41.6
⌃ [b7e1f0a2] PureUMFPACK v0.1.4
⌃ [f2c3362d] RecursiveFactorization v0.2.26
⌃ [31c91b34] SciMLBenchmarks v0.1.3 [loaded: v0.1.5]
[e56a9233] Sparspak v0.3.15
[10745b16] Statistics v1.11.1
[3d5dd08c] VectorizationBase v0.21.74
[856f044c] MKL_jll v2025.2.0+0
⌃ [9e0b026c] ParU_jll v1.0.0+0
[37e2e46d] LinearAlgebra v1.12.0
[44cfe95a] Pkg v1.12.1
[9a3f8284] Random v1.11.0
[2f01184e] SparseArrays v1.12.0
Info Packages marked with ⌃ have new versions available and may be upgradable.And the full manifest:
Status `/julia/github-runners/amdci1-1/_work/SciMLBenchmarks.jl/SciMLBenchmarks.jl/benchmarks/LinearSolve/Manifest.toml`
⌃ [47edcb42] ADTypes v1.22.3
[14f7f29c] AMD v0.5.3
[7d9f7c33] Accessors v0.1.45
[79e6a3ab] Adapt v4.7.0
[66dad0bd] AliasTables v1.1.3
⌃ [4fba245c] ArrayInterface v7.28.1
[6e4b80f9] BenchmarkTools v1.8.0
[d1d4a3ce] BitFlags v0.1.10
[62783981] BitTwiddlingConvenienceFunctions v0.1.6
[2a0fbf3d] CPUSummary v0.2.7
[79a69506] ChannelBuffers v0.4.2
[0b6fb165] ChunkCodecCore v1.0.1
[4c0bbee4] ChunkCodecLibZlib v1.1.0
[55437552] ChunkCodecLibZstd v1.0.0
[fb6a15b2] CloseOpenIntervals v0.1.13
⌃ [944b1d66] CodecZlib v0.7.8
[35d6a980] ColorSchemes v3.31.0
[3da002f7] ColorTypes v0.12.1
[c3611d14] ColorVectorSpace v0.11.0
[5ae59095] Colors v0.13.1
⌃ [38540f10] CommonSolve v0.2.12
⌃ [f70d9fcc] CommonWorldInvalidations v1.1.1
[34da2185] Compat v4.18.1
[a33af91c] CompositionsBase v0.1.2
⌃ [2569d6c7] ConcreteStructs v0.2.6
⌃ [f0e56b4a] ConcurrentUtilities v2.5.1
[8f4d0f93] Conda v1.10.3
[187b0558] ConstructionBase v1.6.0
[d38c429a] Contour v0.6.3
[adafc99b] CpuId v0.3.1
[a8cc5b0e] Crayons v4.2.0
[9a962f9c] DataAPI v1.16.0
[a93c6f00] DataFrames v1.8.2
[864edb3b] DataStructures v0.19.6
[e2d170a0] DataValueInterfaces v1.0.0
[8bb1440f] DelimitedFiles v1.9.1
[ffbed154] DocStringExtensions v0.9.5
[4e289a0a] EnumX v1.0.7
[460bff9d] ExceptionUnwrapping v0.1.11
[e2ba6199] ExprTools v0.1.11
[c87230d0] FFMPEG v0.4.5
[29a986be] FastLapackInterface v2.1.1
[5789e2e9] FileIO v1.20.0
[64ca27bc] FindFirstFunctions v3.2.1
⌅ [53c48c17] FixedPointNumbers v0.8.6
[1fa38f19] Format v1.3.7
[069b7b12] FunctionWrappers v1.1.3
⌃ [77dc65aa] FunctionWrappersWrappers v1.12.0
[46192b85] GPUArraysCore v0.2.0
[28b8d3ca] GR v0.73.26
[d7ba0133] Git v1.5.0
[42e2da0e] Grisu v1.0.2
[f67ccb44] HDF5 v0.17.3
⌅ [cd3eb016] HTTP v1.11.0
[076d061b] HashArrayMappedTries v0.2.0
⌅ [eafb193a] Highlights v0.5.3
[3e5b6fbb] HostCPUFeatures v0.1.18
[7073ff75] IJulia v1.34.4
[615f187c] IfElse v0.1.1
[842dd82b] InlineStrings v1.4.5
[3587e190] InverseFunctions v0.1.17
[41ab1584] InvertedIndices v1.3.1
[92d709cd] IrrationalConstants v0.2.6
[82899510] IteratorInterfaceExtensions v1.0.0
[033835bb] JLD2 v0.6.5
[1019f520] JLFzf v0.1.11
[692b3bcd] JLLWrappers v1.8.0
⌅ [682c06a0] JSON v0.21.4
⌃ [ba0b0d4f] Krylov v0.10.8
⌃ [b964fa9f] LaTeXStrings v1.4.0
⌃ [23fbe1c1] Latexify v0.16.11
[10f19ff3] LayoutPointers v0.1.17
⌃ [7ed4a6bd] LinearSolve v5.5.0
[2ab3a3ac] LogExpFunctions v1.0.1
[e6f89c97] LoggingExtras v1.2.0
[bdcacae8] LoopVectorization v0.12.174
[23992714] MAT v0.12.1
[3da0fdf6] MPIPreferences v0.1.12
[1914dd2f] MacroTools v0.5.16
[d125e4d3] ManualMemory v0.1.8
[b51810bb] MatrixDepot v1.1.0
[739be429] MbedTLS v1.1.10
[442fdcdd] Measures v0.3.3
[e1d29d7a] Missings v1.2.0
⌃ [46d2c3a1] MuladdMacro v0.2.6
[ffc61752] Mustache v1.0.21
[77ba4419] NaNMath v1.1.4
[6fe1bfb0] OffsetArrays v1.17.0
[4d8831e6] OpenSSL v1.6.1
⌅ [bac558e1] OrderedCollections v1.8.2
[46dd5b70] Pardiso v1.1.2
⌃ [69de0a69] Parsers v2.8.6 [loaded: v2.8.7]
[ccf2f8ad] PlotThemes v3.3.0
[995b91a9] PlotUtils v1.4.4
⌃ [91a5bcdd] Plots v1.41.6
[f517fe37] Polyester v0.7.19
[1d0040c9] PolyesterWeave v0.2.2
[2dfb63ee] PooledArrays v1.4.3
⌃ [d236fae5] PreallocationTools v1.4.0
[aea7be01] PrecompileTools v1.3.4
[21216c6a] Preferences v1.5.2
⌃ [08abe8d2] PrettyTables v3.4.2
[43287f4e] PtrArrays v1.4.0
⌃ [0c0d3e7f] PureKLU v1.1.1
⌃ [b7e1f0a2] PureUMFPACK v0.1.4
[3cdcf5f2] RecipesBase v1.3.4
[01d81517] RecipesPipeline v0.6.12
⌃ [731186ca] RecursiveArrayTools v4.3.5
⌃ [f2c3362d] RecursiveFactorization v0.2.26
[189a3867] Reexport v1.2.2
[05181044] RelocatableFolders v1.0.1
[ae029012] Requires v1.3.1
⌃ [7e49a35a] RuntimeGeneratedFunctions v0.5.22
[94e857df] SIMDTypes v0.1.0
[476501e8] SLEEFPirates v0.6.46
⌃ [0bca4576] SciMLBase v3.39.1
⌃ [31c91b34] SciMLBenchmarks v0.1.3 [loaded: v0.1.5]
⌃ [a6db7da4] SciMLLogging v2.0.3
⌃ [c0aeaf25] SciMLOperators v1.25.0
[431bcebd] SciMLPublic v1.2.4
[53ae85a6] SciMLStructures v1.10.4
[7e506255] ScopedValues v1.6.2
[6c6a2e73] Scratch v1.3.0
[91c51154] SentinelArrays v1.4.10
[efcf1570] Setfield v1.1.2
[992d4aef] Showoff v1.0.3
[777ac1f9] SimpleBufferStream v1.2.0
[a2af1166] SortingAlgorithms v1.2.3
⌃ [bd59d7e1] SparseBandedMatrices v1.3.3
⌃ [a57abbd0] SparseColumnPivotedQR v2.1.4
[e56a9233] Sparspak v0.3.15
[860ef19b] StableRNGs v1.0.4
⌃ [aedffcd0] Static v1.4.4
[0d7ed370] StaticArrayInterface v1.10.0
[1e83bf80] StaticArraysCore v1.4.4
[10745b16] Statistics v1.11.1
[82ae8749] StatsAPI v1.8.0
[2913bbd2] StatsBase v0.34.12
[7792a7ef] StrideArraysCore v0.5.9
[69024149] StringEncodings v0.3.7
⌅ [892a3eda] StringManipulation v0.4.6
⌃ [2efcf032] SymbolicIndexingInterface v0.3.51
[3783bdb8] TableTraits v1.0.1
[bd369af6] Tables v1.13.0
[62fd8b95] TensorCore v0.1.1
[8290d209] ThreadingUtilities v0.5.6
[3bb67fe8] TranscodingStreams v0.11.3
⌃ [d5829a12] TriangularSolve v0.2.1
⌃ [5c2747f8] URIs v1.6.1
[3a884ed6] UnPack v1.0.2
[1cfade01] UnicodeFun v0.4.1
[41fe7b60] Unzip v0.2.0
[3d5dd08c] VectorizationBase v0.21.74
[33b4df10] VectorizedRNG v0.2.26
[81def892] VersionParsing v1.3.0
[44d3d7a6] Weave v0.10.12
[ddb6d928] YAML v0.4.16
[c2297ded] ZMQ v1.5.1
[6e34b625] Bzip2_jll v1.0.9+0
[83423d85] Cairo_jll v1.18.7+0
[ee1fde0b] Dbus_jll v1.16.2+0
[2702e6a9] EpollShim_jll v0.0.20230411+1
⌃ [2e619515] Expat_jll v2.8.2+0
⌅ [b22a6f82] FFMPEG_jll v8.1.2+0
[a3f928ae] Fontconfig_jll v2.17.1+0
[d7e528f0] FreeType2_jll v2.14.3+1
[559328eb] FriBidi_jll v1.0.17+0
[0656b61e] GLFW_jll v3.4.1+1
[d2c73de3] GR_jll v0.73.26+0
⌅ [b0724c58] GettextRuntime_jll v0.22.4+0
[61579ee1] Ghostscript_jll v9.55.1+0
[020c3dae] Git_LFS_jll v3.7.1+0
⌃ [f8c6e375] Git_jll v2.54.0+0
⌃ [7746bdde] Glib_jll v2.86.3+0
[3b182d85] Graphite2_jll v1.3.16+0
⌅ [0234f1f7] HDF5_jll v2.1.2+0
⌅ [2e76f6c2] HarfBuzz_jll v8.5.1+0
[e33a78d0] Hwloc_jll v2.14.0+0
[1d5cc7b8] IntelOpenMP_jll v2025.2.0+0
⌃ [aacddb02] JpegTurbo_jll v3.2.0+0
[c1c5ebd0] LAME_jll v3.100.3+0
[88015f11] LERC_jll v4.1.0+0
[1d63c593] LLVMOpenMP_jll v22.1.7+0
⌅ [e9f186c6] Libffi_jll v3.4.7+0
[7e76a0d4] Libglvnd_jll v1.7.1+1
[94ce4f54] Libiconv_jll v1.18.0+0
[4b2f31a3] Libmount_jll v2.42.0+0
[89763e89] Libtiff_jll v4.7.3+0
[38a345b3] Libuuid_jll v2.42.0+0
[856f044c] MKL_jll v2025.2.0+0
⌅ [b5ada748] MPIABI_jll v0.1.5+0
[7cb0a576] MPICH_jll v5.0.1+0
[f1f71cc9] MPItrampoline_jll v5.5.6+0
[c8ffd9c3] MbedTLS_jll v2.28.1010+0
[9237b28f] MicrosoftMPI_jll v10.1.4+3
[e7412a2a] Ogg_jll v1.3.6+0
[fe0851c0] OpenMPI_jll v5.0.11+0
⌃ [9bd350c2] OpenSSH_jll v10.4.1+0
[91d4177d] Opus_jll v1.6.1+0
⌃ [36c8627f] Pango_jll v1.57.1+0
⌃ [9e0b026c] ParU_jll v1.0.0+0
[30392449] Pixman_jll v0.46.4+0
[c0090381] Qt6Base_jll v6.10.2+2
[629bc702] Qt6Declarative_jll v6.10.2+2
[ce943373] Qt6ShaderTools_jll v6.10.2+1
[6de9746b] Qt6Svg_jll v6.10.2+0
[e99dba38] Qt6Wayland_jll v6.10.2+1
[a44049a8] Vulkan_Loader_jll v1.3.243+0
[a2964d1f] Wayland_jll v1.24.0+0
⌅ [02c8fc9c] XML2_jll v2.13.9+0
[ffd25f8a] XZ_jll v5.8.3+0
[f67eecfb] Xorg_libICE_jll v1.1.2+0
[c834827a] Xorg_libSM_jll v1.2.6+0
[4f6342f7] Xorg_libX11_jll v1.8.13+0
[0c0b7dd1] Xorg_libXau_jll v1.0.13+0
[935fb764] Xorg_libXcursor_jll v1.2.4+0
[a3789734] Xorg_libXdmcp_jll v1.1.6+0
[1082639a] Xorg_libXext_jll v1.3.8+0
[d091e8ba] Xorg_libXfixes_jll v6.0.2+0
⌃ [a51aa0fd] Xorg_libXi_jll v1.8.3+0
[d1454406] Xorg_libXinerama_jll v1.1.7+0
[ec84b674] Xorg_libXrandr_jll v1.5.6+0
[ea2f1a96] Xorg_libXrender_jll v0.9.12+0
[a65dc6b1] Xorg_libpciaccess_jll v0.19.0+0
[c7cfdc94] Xorg_libxcb_jll v1.17.1+0
[cc61e674] Xorg_libxkbfile_jll v1.2.0+0
[e920d4aa] Xorg_xcb_util_cursor_jll v0.1.6+0
[12413925] Xorg_xcb_util_image_jll v0.4.1+0
[2def613f] Xorg_xcb_util_jll v0.4.1+0
[975044d2] Xorg_xcb_util_keysyms_jll v0.4.1+0
[0d47668e] Xorg_xcb_util_renderutil_jll v0.3.10+0
[c22f9ab0] Xorg_xcb_util_wm_jll v0.4.2+0
[35661453] Xorg_xkbcomp_jll v1.4.7+0
[33bec58e] Xorg_xkeyboard_config_jll v2.47.0+2
[c5fb5394] Xorg_xtrans_jll v1.6.0+0
[8f1865be] ZeroMQ_jll v4.3.6+0
[3161d3a3] Zstd_jll v1.5.7+1
⌅ [2b3700d1] aws_c_auth_jll v0.9.6+0
[70f11efc] aws_c_cal_jll v0.9.13+0
[73048d1d] aws_c_common_jll v0.12.6+0
[73a04cd5] aws_c_compression_jll v0.3.2+0
[3254fc65] aws_c_http_jll v0.10.13+0
[13c41daa] aws_c_io_jll v0.26.3+0
⌅ [bd1f34fb] aws_c_s3_jll v0.11.5+0
[1282aa60] aws_c_sdkutils_jll v0.2.4+1
[b2a88e68] aws_checksums_jll v0.2.10+0
[c4b69c83] dlfcn_win32_jll v1.4.2+0
[35ca27e7] eudev_jll v3.2.14+0
⌅ [214eeab7] fzf_jll v0.61.1+0
[477f73a3] libaec_jll v1.1.7+0
⌃ [a4ae2306] libaom_jll v3.13.3+0
[0ac62f75] libass_jll v0.17.4+0
[1183f4f0] libdecor_jll v0.2.2+0
[8e53e030] libdrm_jll v2.4.134+0
[2db6ffa8] libevdev_jll v1.13.4+0
[f638f0a6] libfdk_aac_jll v2.0.4+0
[36db933b] libinput_jll v1.28.1+0
[b53b4c65] libpng_jll v1.6.58+0
[a9144af2] libsodium_jll v1.0.21+0
[9a156e7d] libva_jll v2.23.0+0
[f27f6e37] libvorbis_jll v1.3.8+0
⌅ [9aeb927a] mpif_jll v0.1.7+0
[009596ad] mtdev_jll v1.1.7+0
[1317d2d5] oneTBB_jll v2022.3.0+0
[cddc5d3d] s2n_tls_jll v1.7.3+0
⌅ [1270edf5] x264_jll v10164.0.1+0
[dfaa095f] x265_jll v4.1.0+0
[d8fb68d0] xkbcommon_jll v1.13.0+0
[0dad84c5] ArgTools v1.1.2
[56f22d72] Artifacts v1.11.0
[2a0f44e3] Base64 v1.11.0
[ade2ca70] Dates v1.11.0
[8ba89e20] Distributed v1.11.0
[f43a241f] Downloads v1.7.0
[7b1f6079] FileWatching v1.11.0
[9fa8497b] Future v1.11.0
[b77e0a4c] InteractiveUtils v1.11.0
[ac6e5ff7] JuliaSyntaxHighlighting v1.12.0
[4af54fe1] LazyArtifacts v1.11.0
[b27032c2] LibCURL v0.6.4
[76f85450] LibGit2 v1.11.0
[8f399da3] Libdl v1.11.0
[37e2e46d] LinearAlgebra v1.12.0
[56ddb016] Logging v1.11.0
[d6f4376e] Markdown v1.11.0
[a63ad114] Mmap v1.11.0
[ca575930] NetworkOptions v1.3.0
[44cfe95a] Pkg v1.12.1
[de0858da] Printf v1.11.0
[9abbd945] Profile v1.11.0
[3fa0cd96] REPL v1.11.0
[9a3f8284] Random v1.11.0
[ea8e919c] SHA v0.7.0
[9e88b42a] Serialization v1.11.0
[6462fe0b] Sockets v1.11.0
[2f01184e] SparseArrays v1.12.0
[f489334b] StyledStrings v1.11.0
[fa267f1f] TOML v1.0.3
[a4e569a6] Tar v1.10.0
[8dfed614] Test v1.11.0
[cf7118a7] UUIDs v1.11.0
[4ec0a83e] Unicode v1.11.0
[e66e0078] CompilerSupportLibraries_jll v1.3.0+1
[deac9b47] LibCURL_jll v8.15.0+0
[e37daf67] LibGit2_jll v1.9.0+0
[29816b5a] LibSSH2_jll v1.11.3+1
[14a3606d] MozillaCACerts_jll v2025.11.4
[4536629a] OpenBLAS_jll v0.3.29+0
[05823500] OpenLibm_jll v0.8.7+0
[458c3c95] OpenSSL_jll v3.5.4+0
[efcefdf7] PCRE2_jll v10.44.0+1
[bea87d4a] SuiteSparse_jll v7.8.3+2
[83775a58] Zlib_jll v1.3.1+2
[8e850b90] libblastrampoline_jll v5.15.0+0
[8e850ede] nghttp2_jll v1.64.0+1
[3f19e933] p7zip_jll v17.7.0+0
Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m`