Package evaluation to test GridapPETSc on Julia 1.14.0-DEV.3081 (21a70e450d*) started at 2026-09-02T07:54:03.080 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Activating project at `~/.julia/environments/v1.14` Set-up completed after 31.9s ################################################################################ # Installation # Installing GridapPETSc... Resolving package versions... Installed GridapPETSc ─ v0.5.8 Updating `~/.julia/environments/v1.14/Project.toml` [bcdc36c2] + GridapPETSc v0.5.8 Updating `~/.julia/environments/v1.14/Manifest.toml` [47edcb42] + ADTypes v1.24.0 [621f4979] + AbstractFFTs v1.5.0 [1520ce14] + AbstractTrees v0.4.5 [79e6a3ab] + Adapt v4.7.0 [dce04be8] + ArgCheck v2.5.0 [ec485272] + ArnoldiMethod v0.4.0 [4fba245c] + ArrayInterface v7.30.1 [4c555306] + ArrayLayouts v1.12.2 [a9b6321e] + Atomix v1.1.3 [15f4f7f2] + AutoHashEquals v2.2.0 [fbb218c0] + BSON v0.3.9 [62783981] + BitTwiddlingConvenienceFunctions v0.1.6 [8e7c35d0] + BlockArrays v1.10.0 [2a0fbf3d] + CPUSummary v0.2.7 [0b6fb165] + ChunkCodecCore v1.0.2 [4c0bbee4] + ChunkCodecLibZlib v1.1.0 [55437552] + ChunkCodecLibZstd v1.0.0 [7a955b69] + CircularArrays v1.5.0 [fb6a15b2] + CloseOpenIntervals v0.1.13 [944b1d66] + CodecZlib v0.7.9 [861a8166] + Combinatorics v1.1.0 [bbf7d656] + CommonSubexpressions v0.3.1 [f70d9fcc] + CommonWorldInvalidations v1.2.0 [34da2185] + Compat v4.18.1 [187b0558] + ConstructionBase v1.6.0 [adafc99b] + CpuId v0.3.1 [864edb3b] + DataStructures v0.19.6 [163ba53b] + DiffResults v1.1.0 [b552c78f] + DiffRules v1.16.0 [a0c0ee7d] + DifferentiationInterface v0.7.21 [b4f34e82] + Distances v0.10.12 [ffbed154] + DocStringExtensions v0.9.5 [7a1cc6ca] + FFTW v1.10.0 [442a2c76] + FastGaussQuadrature v1.3.0 [5789e2e9] + FileIO v1.20.0 [1a297f60] + FillArrays v1.17.0 [6a86dc24] + FiniteDiff v2.33.0 [f6369f11] + ForwardDiff v1.4.5 [a0844989] + Gamma v1.2.0 [86223c79] + Graphs v1.14.0 [56d4f2e9] + Gridap v0.20.9 [f9701e48] + GridapDistributed v0.4.17 [bcdc36c2] + GridapPETSc v0.5.8 [076d061b] + HashArrayMappedTries v0.2.0 [615f187c] + IfElse v0.1.1 [d25df0c9] + Inflate v0.1.5 [92d709cd] + IrrationalConstants v0.2.6 [42fd0dbc] + IterativeSolvers v0.9.4 [033835bb] + JLD2 v0.6.6 [692b3bcd] + JLLWrappers v1.8.0 [682c06a0] + JSON v1.7.1 [10f19ff3] + LayoutPointers v0.1.17 [9c8b4983] + LightXML v0.9.3 ⌃ [d3d80556] + LineSearches v7.5.1 [2ab3a3ac] + LogExpFunctions v1.0.1 [da04e1cc] + MPI v0.20.27 [3da0fdf6] + MPIPreferences v0.1.12 [1914dd2f] + MacroTools v0.5.16 [d125e4d3] + ManualMemory v0.1.8 ⌅ [d41bc354] + NLSolversBase v7.10.0 ⌅ [2774e3e8] + NLsolve v4.5.1 [77ba4419] + NaNMath v1.1.4 [b8a86587] + NearestNeighbors v0.4.29 [6fe1bfb0] + OffsetArrays v1.17.0 [bac558e1] + OrderedCollections v2.0.1 ⌅ [69de0a69] + Parsers v2.8.7 ⌅ [5a9dfac6] + PartitionedArrays v0.3.5 [eebad327] + PkgVersion v0.3.3 [f517fe37] + Polyester v0.7.19 [1d0040c9] + PolyesterWeave v0.2.2 [c74db56a] + PolynomialBases v0.4.28 [aea7be01] + PrecompileTools v1.3.4 [21216c6a] + Preferences v1.5.2 [1fd47b50] + QuadGK v2.11.3 [3cdcf5f2] + RecipesBase v1.3.4 [189a3867] + Reexport v1.2.2 [ae029012] + Requires v1.3.1 [94e857df] + SIMDTypes v0.1.0 [431bcebd] + SciMLPublic v1.3.0 [7e506255] + ScopedValues v1.6.2 [efcf1570] + Setfield v1.1.2 [699a6c99] + SimpleTraits v0.9.6 [ce78b400] + SimpleUnPack v1.1.0 [a0a7dd2c] + SparseMatricesCSR v0.6.12 [276daf66] + SpecialFunctions v2.9.0 [aedffcd0] + Static v1.4.6 [0d7ed370] + StaticArrayInterface v1.10.0 [90137ffa] + StaticArrays v1.9.19 [1e83bf80] + StaticArraysCore v1.4.4 [10745b16] + Statistics v1.11.4 [82ae8749] + StatsAPI v1.8.0 [7792a7ef] + StrideArraysCore v0.5.9 [ec057cc2] + StructUtils v2.8.5 [8290d209] + ThreadingUtilities v0.5.6 [3bb67fe8] + TranscodingStreams v0.11.3 [013be700] + UnsafeAtomics v0.3.2 [4004b06d] + VTKBase v1.0.1 [64499a7a] + WriteVTK v1.22.0 [f5851436] + FFTW_jll v3.3.12+0 [e33a78d0] + Hwloc_jll v2.14.0+0 [1d5cc7b8] + IntelOpenMP_jll v2025.2.0+0 [94ce4f54] + Libiconv_jll v1.18.0+0 [856f044c] + MKL_jll v2025.2.0+0 [b5ada748] + MPIABI_jll v1.0.0+0 [7cb0a576] + MPICH_jll v5.0.1+0 [f1f71cc9] + MPItrampoline_jll v5.5.6+0 [9237b28f] + MicrosoftMPI_jll v10.1.4+3 [656ef2d0] + OpenBLAS32_jll v0.3.34+0 [fe0851c0] + OpenMPI_jll v5.0.11+0 [efe28fd5] + OpenSpecFun_jll v0.5.6+0 [8fa3689e] + PETSc_jll v3.22.2+0 [aabda75e] + SCALAPACK32_jll v2.2.302+0 ⌅ [02c8fc9c] + XML2_jll v2.13.9+0 [a65dc6b1] + Xorg_libpciaccess_jll v0.19.0+0 [9aeb927a] + mpif_jll v1.0.0+0 [1317d2d5] + oneTBB_jll v2022.3.0+0 [0dad84c5] + ArgTools v1.2.0 [56f22d72] + Artifacts v1.11.0 [2a0f44e3] + Base64 v1.11.0 [ade2ca70] + Dates v1.11.0 [8ba89e20] + Distributed v1.12.0 [f43a241f] + Downloads v1.7.0 [7b1f6079] + FileWatching v1.11.0 [9fa8497b] + Future v1.11.0 [b77e0a4c] + InteractiveUtils v1.11.0 [ac6e5ff7] + JuliaSyntaxHighlighting v1.13.0 [4af54fe1] + LazyArtifacts v1.11.0 [b27032c2] + LibCURL v1.0.0 [76f85450] + LibGit2 v1.11.0 [8f399da3] + Libdl v1.11.0 [37e2e46d] + LinearAlgebra v1.14.0 [56ddb016] + Logging v1.11.0 [d6f4376e] + Markdown v1.11.0 [a63ad114] + Mmap v1.11.0 [ca575930] + NetworkOptions v1.3.0 [44cfe95a] + Pkg v1.14.0 [de0858da] + Printf v1.11.0 [9a3f8284] + Random v1.11.0 [ea8e919c] + SHA v1.13.0 [9e88b42a] + Serialization v1.11.0 [6462fe0b] + Sockets v1.11.0 [2f01184e] + SparseArrays v1.13.0 [f489334b] + StyledStrings v1.13.0 [4607b0f0] + SuiteSparse [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 v1.5.7+0 [deac9b47] + LibCURL_jll v8.21.0+0 [e37daf67] + LibGit2_jll v1.9.7+0 [29816b5a] + LibSSH2_jll v1.11.104+0 [14a3606d] + MozillaCACerts_jll v2026.8.13 [4536629a] + OpenBLAS_jll v0.3.34+0 [05823500] + OpenLibm_jll v0.8.7+0 [458c3c95] + OpenSSL_jll v3.5.8+0 [efcefdf7] + PCRE2_jll v10.47.0+0 [bea87d4a] + SuiteSparse_jll v7.10.1+0 [83775a58] + Zlib_jll v1.3.2+0 [3161d3a3] + Zstd_jll v1.5.7+1 [8e850b90] + libblastrampoline_jll v5.15.0+0 [8e850ede] + nghttp2_jll v1.70.0+0 [3f19e933] + p7zip_jll v17.8.2+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` Building GridapPETSc → `~/.julia/scratchspaces/44cfe95a-1eb2-52ea-b672-e2afdf69b78f/35e3b19607cf085487ec5b219a5f20d807bbe4d6/build.log` Installation completed after 27.28s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompiling project... 67.6 s ✓ Gridap 8.2 s ✓ PartitionedArrays 22.8 s ✓ GridapDistributed 21.2 s ✓ GridapPETSc 4 dependencies successfully precompiled in 122 seconds. 179 already precompiled. Precompilation completed after 171.98s ################################################################################ # Testing # Testing GridapPETSc Status `/tmp/jl_qiXIUJ/Project.toml` [56d4f2e9] Gridap v0.20.9 [f9701e48] GridapDistributed v0.4.17 [bcdc36c2] GridapPETSc v0.5.8 [da04e1cc] MPI v0.20.27 ⌅ [5a9dfac6] PartitionedArrays v0.3.5 [a0a7dd2c] SparseMatricesCSR v0.6.12 [8fa3689e] PETSc_jll v3.22.2+0 [8f399da3] Libdl v1.11.0 [37e2e46d] LinearAlgebra v1.14.0 [9a3f8284] Random v1.11.0 [2f01184e] SparseArrays v1.13.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_qiXIUJ/Manifest.toml` [47edcb42] ADTypes v1.24.0 [621f4979] AbstractFFTs v1.5.0 [1520ce14] AbstractTrees v0.4.5 [79e6a3ab] Adapt v4.7.0 [dce04be8] ArgCheck v2.5.0 [ec485272] ArnoldiMethod v0.4.0 [4fba245c] ArrayInterface v7.30.1 [4c555306] ArrayLayouts v1.12.2 [a9b6321e] Atomix v1.1.3 [15f4f7f2] AutoHashEquals v2.2.0 [fbb218c0] BSON v0.3.9 [62783981] BitTwiddlingConvenienceFunctions v0.1.6 [8e7c35d0] BlockArrays v1.10.0 [2a0fbf3d] CPUSummary v0.2.7 [0b6fb165] ChunkCodecCore v1.0.2 [4c0bbee4] ChunkCodecLibZlib v1.1.0 [55437552] ChunkCodecLibZstd v1.0.0 [7a955b69] CircularArrays v1.5.0 [fb6a15b2] CloseOpenIntervals v0.1.13 [944b1d66] CodecZlib v0.7.9 [861a8166] Combinatorics v1.1.0 [bbf7d656] CommonSubexpressions v0.3.1 [f70d9fcc] CommonWorldInvalidations v1.2.0 [34da2185] Compat v4.18.1 [187b0558] ConstructionBase v1.6.0 [adafc99b] CpuId v0.3.1 [864edb3b] DataStructures v0.19.6 [163ba53b] DiffResults v1.1.0 [b552c78f] DiffRules v1.16.0 [a0c0ee7d] DifferentiationInterface v0.7.21 [b4f34e82] Distances v0.10.12 [ffbed154] DocStringExtensions v0.9.5 [7a1cc6ca] FFTW v1.10.0 [442a2c76] FastGaussQuadrature v1.3.0 [5789e2e9] FileIO v1.20.0 [1a297f60] FillArrays v1.17.0 [6a86dc24] FiniteDiff v2.33.0 [f6369f11] ForwardDiff v1.4.5 [a0844989] Gamma v1.2.0 [86223c79] Graphs v1.14.0 [56d4f2e9] Gridap v0.20.9 [f9701e48] GridapDistributed v0.4.17 [bcdc36c2] GridapPETSc v0.5.8 [076d061b] HashArrayMappedTries v0.2.0 [615f187c] IfElse v0.1.1 [d25df0c9] Inflate v0.1.5 [92d709cd] IrrationalConstants v0.2.6 [42fd0dbc] IterativeSolvers v0.9.4 [033835bb] JLD2 v0.6.6 [692b3bcd] JLLWrappers v1.8.0 [682c06a0] JSON v1.7.1 [10f19ff3] LayoutPointers v0.1.17 [9c8b4983] LightXML v0.9.3 ⌃ [d3d80556] LineSearches v7.5.1 [2ab3a3ac] LogExpFunctions v1.0.1 [da04e1cc] MPI v0.20.27 [3da0fdf6] MPIPreferences v0.1.12 [1914dd2f] MacroTools v0.5.16 [d125e4d3] ManualMemory v0.1.8 ⌅ [d41bc354] NLSolversBase v7.10.0 ⌅ [2774e3e8] NLsolve v4.5.1 [77ba4419] NaNMath v1.1.4 [b8a86587] NearestNeighbors v0.4.29 [6fe1bfb0] OffsetArrays v1.17.0 [bac558e1] OrderedCollections v2.0.1 ⌅ [69de0a69] Parsers v2.8.7 ⌅ [5a9dfac6] PartitionedArrays v0.3.5 [eebad327] PkgVersion v0.3.3 [f517fe37] Polyester v0.7.19 [1d0040c9] PolyesterWeave v0.2.2 [c74db56a] PolynomialBases v0.4.28 [aea7be01] PrecompileTools v1.3.4 [21216c6a] Preferences v1.5.2 [1fd47b50] QuadGK v2.11.3 [3cdcf5f2] RecipesBase v1.3.4 [189a3867] Reexport v1.2.2 [ae029012] Requires v1.3.1 [94e857df] SIMDTypes v0.1.0 [431bcebd] SciMLPublic v1.3.0 [7e506255] ScopedValues v1.6.2 [efcf1570] Setfield v1.1.2 [699a6c99] SimpleTraits v0.9.6 [ce78b400] SimpleUnPack v1.1.0 [a0a7dd2c] SparseMatricesCSR v0.6.12 [276daf66] SpecialFunctions v2.9.0 [aedffcd0] Static v1.4.6 [0d7ed370] StaticArrayInterface v1.10.0 [90137ffa] StaticArrays v1.9.19 [1e83bf80] StaticArraysCore v1.4.4 [10745b16] Statistics v1.11.4 [82ae8749] StatsAPI v1.8.0 [7792a7ef] StrideArraysCore v0.5.9 [ec057cc2] StructUtils v2.8.5 [8290d209] ThreadingUtilities v0.5.6 [3bb67fe8] TranscodingStreams v0.11.3 [013be700] UnsafeAtomics v0.3.2 [4004b06d] VTKBase v1.0.1 [64499a7a] WriteVTK v1.22.0 [f5851436] FFTW_jll v3.3.12+0 [e33a78d0] Hwloc_jll v2.14.0+0 [1d5cc7b8] IntelOpenMP_jll v2025.2.0+0 [94ce4f54] Libiconv_jll v1.18.0+0 [856f044c] MKL_jll v2025.2.0+0 [b5ada748] MPIABI_jll v1.0.0+0 [7cb0a576] MPICH_jll v5.0.1+0 [f1f71cc9] MPItrampoline_jll v5.5.6+0 [9237b28f] MicrosoftMPI_jll v10.1.4+3 [656ef2d0] OpenBLAS32_jll v0.3.34+0 [fe0851c0] OpenMPI_jll v5.0.11+0 [efe28fd5] OpenSpecFun_jll v0.5.6+0 [8fa3689e] PETSc_jll v3.22.2+0 [aabda75e] SCALAPACK32_jll v2.2.302+0 ⌅ [02c8fc9c] XML2_jll v2.13.9+0 [a65dc6b1] Xorg_libpciaccess_jll v0.19.0+0 [9aeb927a] mpif_jll v1.0.0+0 [1317d2d5] oneTBB_jll v2022.3.0+0 [0dad84c5] ArgTools v1.2.0 [56f22d72] Artifacts v1.11.0 [2a0f44e3] Base64 v1.11.0 [ade2ca70] Dates v1.11.0 [8ba89e20] Distributed v1.12.0 [f43a241f] Downloads v1.7.0 [7b1f6079] FileWatching v1.11.0 [9fa8497b] Future v1.11.0 [b77e0a4c] InteractiveUtils v1.11.0 [ac6e5ff7] JuliaSyntaxHighlighting v1.13.0 [4af54fe1] LazyArtifacts v1.11.0 [b27032c2] LibCURL v1.0.0 [76f85450] LibGit2 v1.11.0 [8f399da3] Libdl v1.11.0 [37e2e46d] LinearAlgebra v1.14.0 [56ddb016] Logging v1.11.0 [d6f4376e] Markdown v1.11.0 [a63ad114] Mmap v1.11.0 [ca575930] NetworkOptions v1.3.0 [44cfe95a] Pkg v1.14.0 [de0858da] Printf v1.11.0 [9a3f8284] Random v1.11.0 [ea8e919c] SHA v1.13.0 [9e88b42a] Serialization v1.11.0 [6462fe0b] Sockets v1.11.0 [2f01184e] SparseArrays v1.13.0 [f489334b] StyledStrings v1.13.0 [4607b0f0] SuiteSparse [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 v1.5.7+0 [deac9b47] LibCURL_jll v8.21.0+0 [e37daf67] LibGit2_jll v1.9.7+0 [29816b5a] LibSSH2_jll v1.11.104+0 [14a3606d] MozillaCACerts_jll v2026.8.13 [4536629a] OpenBLAS_jll v0.3.34+0 [05823500] OpenLibm_jll v0.8.7+0 [458c3c95] OpenSSL_jll v3.5.8+0 [efcefdf7] PCRE2_jll v10.47.0+0 [bea87d4a] SuiteSparse_jll v7.10.1+0 [83775a58] Zlib_jll v1.3.2+0 [3161d3a3] Zstd_jll v1.5.7+1 [8e850b90] libblastrampoline_jll v5.15.0+0 [8e850ede] nghttp2_jll v1.70.0+0 [3f19e933] p7zip_jll v17.8.2+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. Testing Running tests... [0] PetscDetermineInitialFPTrap(): Floating point trapping is off by default 0 [0] PetscDeviceInitializeTypeFromOptions_Private(): PetscDeviceType host available, initializing [0] PetscDeviceInitializeTypeFromOptions_Private(): PetscDevice host initialized, default device id 0, view FALSE, init type lazy [0] PetscDeviceInitializeTypeFromOptions_Private(): PetscDeviceType cuda not available [0] PetscDeviceInitializeTypeFromOptions_Private(): PetscDeviceType hip not available [0] PetscDeviceInitializeTypeFromOptions_Private(): PetscDeviceType sycl not available [0] PetscInitialize_Common(): PETSc successfully started: number of processors = 1 [0] PetscGetHostName(): Rejecting domainname, likely is NIS GridapPETSc-primary-hvylV5v1.(none) [0] PetscInitialize_Common(): Running on machine: GridapPETSc-primary-hvylV5v1 [0] PetscInitialize_Common(): BLAS: Environment number of OpenBLAS threads 1 given by OPENBLAS_NUM_THREADS [0] PetscBLASSetNumThreads(): Setting number of threads used for OpenBLAS provided BLAS 1 [0] PetscCommDuplicate(): Duplicating a communicator 1140850689 -2080374784 max tags = 1073741823 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscGetHostName(): Rejecting domainname, likely is NIS GridapPETSc-primary-hvylV5v1.(none) Vec Object: 1 MPI process type: seq 1. 2. 4. 1. [0] PetscCommDuplicate(): Duplicating a communicator 1140850688 -2080374783 max tags = 1073741823 [0] PetscGetHostName(): Rejecting domainname, likely is NIS GridapPETSc-primary-hvylV5v1.(none) Vec Object: 1 MPI process type: seq 1. 2. 4. 1. Vec Object: 1 MPI process type: seq 20. 40. 4. 60. [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] MatAssemblyEnd_SeqAIJ(): Matrix size: 4 X 5; storage space: 10 unneeded,10 used [0] MatAssemblyEnd_SeqAIJ(): Number of mallocs during MatSetValues() is 0 [0] MatAssemblyEnd_SeqAIJ(): Maximum nonzeros in any row is 3 [0] MatCheckCompressedRow(): Found the ratio (num_zerorows 0)/(num_localrows 4) < 0.6. Do not use CompressedRow routines. [0] MatSeqAIJCheckInode(): Found 3 nodes of 4. Limit used: 5. Using Inode routines Mat Object: 1 MPI process type: seqaij row 0: (1, 2.) (3, 3.) (4, 1.) row 1: (1, 6.) (3, 11.) (4, 5.) row 2: (1, 4.) (3, 3.) row 3: (3, 4.) (4, 3.) [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] MatAssemblyEnd_SeqAIJ(): Matrix size: 4 X 4; storage space: 0 unneeded,10 used [0] MatAssemblyEnd_SeqAIJ(): Number of mallocs during MatSetValues() is 0 [0] MatAssemblyEnd_SeqAIJ(): Maximum nonzeros in any row is 3 [0] MatCheckCompressedRow(): Found the ratio (num_zerorows 0)/(num_localrows 4) < 0.6. Do not use CompressedRow routines. [0] MatSeqAIJCheckInode(): Found 4 nodes out of 4 rows. Not using Inode routines Mat Object: 1 MPI process type: seqaij row 0: (0, 4.) (1, -2.) row 1: (0, -1.) (1, 6.) (2, -2.) row 2: (1, -1.) (2, 6.) (3, -2.) row 3: (2, -1.) (3, 4.) [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] KSPConvergedDefault(): Linear solver has converged. Residual norm 1.249022930744e-16 is less than relative tolerance 1.000000000000e-05 times initial right-hand side norm 1.145643923739e+00 at iteration 4 [0] KSPConvergedDefault(): Linear solver has converged. Residual norm 1.249022930744e-16 is less than relative tolerance 1.000000000000e-05 times initial right-hand side norm 1.145643923739e+00 at iteration 4 [0] KSPConvergedDefault(): Linear solver has converged. Residual norm 1.291406315399e-16 is less than relative tolerance 1.000000000000e-05 times initial right-hand side norm 1.145643923739e+00 at iteration 4 KSP Object: (p_) 1 MPI process type: gmres restart=30, using Classical (unmodified) Gram-Schmidt Orthogonalization with no iterative refinement happy breakdown tolerance 1e-30 maximum iterations=10000, initial guess is zero tolerances: relative=1e-05, absolute=1e-50, divergence=10000. left preconditioning using PRECONDITIONED norm type for convergence test PC Object: (p_) 1 MPI process type: jacobi type DIAGONAL linear system matrix = precond matrix: Mat Object: 1 MPI process type: seqaij rows=4, cols=4 total: nonzeros=10, allocated nonzeros=0 total number of mallocs used during MatSetValues calls=0 not using I-node routines [0] PetscFinalize(): PetscFinalize() called [0] Petsc_OuterComm_Attr_DeleteFn(): Removing reference to PETSc communicator embedded in a user MPI_Comm -2080374783 [0] Petsc_InnerComm_Attr_DeleteFn(): User MPI_Comm 1140850688 is being unlinked from inner PETSc comm -2080374783 [0] PetscCommDestroy(): Deleting PETSc MPI_Comm -2080374783 [0] Petsc_Counter_Attr_DeleteFn(): Deleting counter data in an MPI_Comm -2080374783 [0] Petsc_OuterComm_Attr_DeleteFn(): Removing reference to PETSc communicator embedded in a user MPI_Comm -2080374784 [0] Petsc_InnerComm_Attr_DeleteFn(): User MPI_Comm 1140850689 is being unlinked from inner PETSc comm -2080374784 [0] PetscCommDestroy(): Deleting PETSc MPI_Comm -2080374784 [0] Petsc_Counter_Attr_DeleteFn(): Deleting counter data in an MPI_Comm -2080374784 26.835383 seconds (8.20 M allocations: 507.826 MiB, 2.88% gc time, 40.97% compilation time: 2% of which was recompilation) [0] PetscDetermineInitialFPTrap(): Floating point trapping is off by default 0 [0] PetscDeviceInitializeTypeFromOptions_Private(): PetscDeviceType host available, initializing [0] PetscDeviceInitializeTypeFromOptions_Private(): PetscDevice host initialized, default device id 0, view FALSE, init type lazy [0] PetscDeviceInitializeTypeFromOptions_Private(): PetscDeviceType cuda not available [0] PetscDeviceInitializeTypeFromOptions_Private(): PetscDeviceType hip not available [0] PetscDeviceInitializeTypeFromOptions_Private(): PetscDeviceType sycl not available [0] PetscInitialize_Common(): PETSc successfully started: number of processors = 1 [0] PetscGetHostName(): Rejecting domainname, likely is NIS GridapPETSc-primary-hvylV5v1.(none) [0] PetscInitialize_Common(): Running on machine: GridapPETSc-primary-hvylV5v1 [0] PetscInitialize_Common(): BLAS: Environment number of OpenBLAS threads 1 given by OPENBLAS_NUM_THREADS [0] PetscBLASSetNumThreads(): Setting number of threads used for OpenBLAS provided BLAS 1 [0] PetscCommDuplicate(): Duplicating a communicator 1140850689 -2080374784 max tags = 1073741823 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] MatAssemblyEnd_SeqAIJ(): Matrix size: 4 X 5; storage space: 20 unneeded,0 used [0] MatAssemblyEnd_SeqAIJ(): Number of mallocs during MatSetValues() is 0 [0] MatAssemblyEnd_SeqAIJ(): Maximum nonzeros in any row is 0 [0] MatCheckCompressedRow(): Found the ratio (num_zerorows 4)/(num_localrows 4) > 0.6. Use CompressedRow routines. [0] MatSeqAIJCheckInode(): Found 1 nodes of 4. Limit used: 5. Using Inode routines [0] MatAssemblyEnd_SeqAIJ(): Matrix size: 4 X 5; storage space: 14 unneeded,1 used [0] MatAssemblyEnd_SeqAIJ(): Number of mallocs during MatSetValues() is 1 [0] MatAssemblyEnd_SeqAIJ(): Maximum nonzeros in any row is 1 [0] MatCheckCompressedRow(): Found the ratio (num_zerorows 3)/(num_localrows 4) > 0.6. Use CompressedRow routines. [0] MatSeqAIJCheckInode(): Found 2 nodes of 4. Limit used: 5. Using Inode routines [0] MatAssemblyEnd_SeqAIJ(): Matrix size: 4 X 5; storage space: 14 unneeded,2 used [0] MatAssemblyEnd_SeqAIJ(): Number of mallocs during MatSetValues() is 1 [0] MatAssemblyEnd_SeqAIJ(): Maximum nonzeros in any row is 1 [0] MatCheckCompressedRow(): Found the ratio (num_zerorows 2)/(num_localrows 4) < 0.6. Do not use CompressedRow routines. [0] MatSeqAIJCheckInode(): Found 4 nodes out of 4 rows. Not using Inode routines 4×5 GridapPETSc.PETScMatrix: 0.0 0.0 5.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7.0 0.0 0.0 0.0 0.0 0.0 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] MatAssemblyEnd_SeqAIJ(): Matrix size: 3 X 2; storage space: 6 unneeded,0 used [0] MatAssemblyEnd_SeqAIJ(): Number of mallocs during MatSetValues() is 0 [0] MatAssemblyEnd_SeqAIJ(): Maximum nonzeros in any row is 0 [0] MatCheckCompressedRow(): Found the ratio (num_zerorows 3)/(num_localrows 3) > 0.6. Use CompressedRow routines. [0] MatSeqAIJCheckInode(): Found 1 nodes of 3. Limit used: 5. Using Inode routines [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] MatAssemblyEnd_SeqAIJ(): Matrix size: 4 X 4; storage space: 0 unneeded,10 used [0] MatAssemblyEnd_SeqAIJ(): Number of mallocs during MatSetValues() is 0 [0] MatAssemblyEnd_SeqAIJ(): Maximum nonzeros in any row is 3 [0] MatCheckCompressedRow(): Found the ratio (num_zerorows 0)/(num_localrows 4) < 0.6. Do not use CompressedRow routines. [0] MatSeqAIJCheckInode(): Found 4 nodes out of 4 rows. Not using Inode routines [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] MatConvert(): Calling duplicate for initial matrix seqaij 0 1 [0] MatConvert(): Calling duplicate for initial matrix seqaij 0 1 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] Petsc_OuterComm_Attr_DeleteFn(): Removing reference to PETSc communicator embedded in a user MPI_Comm -2080374784 [0] Petsc_InnerComm_Attr_DeleteFn(): User MPI_Comm 1140850689 is being unlinked from inner PETSc comm -2080374784 [0] PetscCommDestroy(): Deleting PETSc MPI_Comm -2080374784 [0] Petsc_Counter_Attr_DeleteFn(): Deleting counter data in an MPI_Comm -2080374784 [0] PetscFinalize(): PetscFinalize() called 11.462002 seconds (3.08 M allocations: 195.755 MiB, 2.08% gc time, 97.95% compilation time) 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 KSP Object: 1 MPI process type: gmres restart=30, using Classical (unmodified) Gram-Schmidt Orthogonalization with no iterative refinement happy breakdown tolerance 1e-30 maximum iterations=10000, initial guess is zero tolerances: relative=1e-05, absolute=1e-50, divergence=10000. left preconditioning using DEFAULT norm type for convergence test PC Object: 1 MPI process type: jacobi PC has not been set up so information may be incomplete type DIAGONAL linear system matrix = precond matrix: Mat Object: 1 MPI process type: seqaij rows=4, cols=4 total: nonzeros=10, allocated nonzeros=0 total number of mallocs used during MatSetValues calls=0 not using I-node routines 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 0 KSP Residual norm 2.000000000000e+00 1 KSP Residual norm 4.002966042487e-16 7.470314 seconds (3.71 M allocations: 238.974 MiB, 4.12% gc time, 94.93% compilation time) 0 SNES Function norm 3.605551275464e+00 1 SNES Function norm 4.444444444444e-01 2 SNES Function norm 7.111111111111e-02 3 SNES Function norm 3.936947327951e-03 4 SNES Function norm 1.525925473445e-05 5 SNES Function norm 2.328306437081e-10 6 SNES Function norm 0.000000000000e+00 Nonlinear solve converged due to CONVERGED_FNORM_RELATIVE iterations 6 0 SNES Function norm 0.000000000000e+00 1 SNES Function norm 0.000000000000e+00 Nonlinear solve converged due to CONVERGED_FNORM_RELATIVE iterations 1 0 SNES Function norm 0.000000000000e+00 1 SNES Function norm 0.000000000000e+00 Nonlinear solve converged due to CONVERGED_FNORM_RELATIVE iterations 1 0 SNES Function norm 1.486606874732e-01 1 SNES Function norm 8.402777777778e-03 2 SNES Function norm 6.831067663990e-05 3 SNES Function norm 4.665073682466e-09 4 SNES Function norm 0.000000000000e+00 Nonlinear solve converged due to CONVERGED_FNORM_RELATIVE iterations 4 0 SNES Function norm 0.000000000000e+00 1 SNES Function norm 0.000000000000e+00 Nonlinear solve converged due to CONVERGED_FNORM_RELATIVE iterations 1 0 SNES Function norm 0.000000000000e+00 1 SNES Function norm 0.000000000000e+00 Nonlinear solve converged due to CONVERGED_FNORM_RELATIVE iterations 1 0 SNES Function norm 3.605551275464e+00 1 SNES Function norm 4.444444444444e-01 2 SNES Function norm 7.111111111111e-02 3 SNES Function norm 3.936947327951e-03 4 SNES Function norm 1.525925473445e-05 5 SNES Function norm 2.328306437081e-10 6 SNES Function norm 0.000000000000e+00 Nonlinear solve converged due to CONVERGED_FNORM_RELATIVE iterations 6 2.213329 seconds (1.10 M allocations: 67.179 MiB, 7.41% gc time, 91.83% compilation time) [0] PetscDetermineInitialFPTrap(): Floating point trapping is off by default 0 [0] PetscDeviceInitializeTypeFromOptions_Private(): PetscDeviceType host available, initializing [0] PetscDeviceInitializeTypeFromOptions_Private(): PetscDevice host initialized, default device id 0, view FALSE, init type lazy [0] PetscDeviceInitializeTypeFromOptions_Private(): PetscDeviceType cuda not available [0] PetscDeviceInitializeTypeFromOptions_Private(): PetscDeviceType hip not available [0] PetscDeviceInitializeTypeFromOptions_Private(): PetscDeviceType sycl not available [0] PetscInitialize_Common(): PETSc successfully started: number of processors = 1 [0] PetscGetHostName(): Rejecting domainname, likely is NIS GridapPETSc-primary-hvylV5v1.(none) [0] PetscInitialize_Common(): Running on machine: GridapPETSc-primary-hvylV5v1 [0] PetscInitialize_Common(): BLAS: Environment number of OpenBLAS threads 1 given by OPENBLAS_NUM_THREADS [0] PetscBLASSetNumThreads(): Setting number of threads used for OpenBLAS provided BLAS 1 [0] PetscCommDuplicate(): Duplicating a communicator 1140850689 -2080374784 max tags = 1073741823 [0] MatAssemblyEnd_SeqAIJ(): Matrix size: 4 X 3; storage space: 2 unneeded,1 used [0] MatAssemblyEnd_SeqAIJ(): Number of mallocs during MatSetValues() is 0 [0] MatAssemblyEnd_SeqAIJ(): Maximum nonzeros in any row is 1 [0] MatCheckCompressedRow(): Found the ratio (num_zerorows 3)/(num_localrows 4) > 0.6. Use CompressedRow routines. [0] MatSeqAIJCheckInode(): Found 2 nodes of 4. Limit used: 5. Using Inode routines 4×3 GridapPETSc.PETScMatrix: -4.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] MatAssemblyEnd_SeqAIJ(): Matrix size: 4 X 3; storage space: 11 unneeded,1 used [0] MatAssemblyEnd_SeqAIJ(): Number of mallocs during MatSetValues() is 0 [0] MatAssemblyEnd_SeqAIJ(): Maximum nonzeros in any row is 1 [0] MatCheckCompressedRow(): Found the ratio (num_zerorows 3)/(num_localrows 4) > 0.6. Use CompressedRow routines. [0] MatSeqAIJCheckInode(): Found 2 nodes of 4. Limit used: 5. Using Inode routines 4×3 GridapPETSc.PETScMatrix: -4.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] MatAssemblyEnd_SeqAIJ(): Matrix size: 4 X 3; storage space: 0 unneeded,1 used [0] MatAssemblyEnd_SeqAIJ(): Number of mallocs during MatSetValues() is 0 [0] MatAssemblyEnd_SeqAIJ(): Maximum nonzeros in any row is 1 [0] MatCheckCompressedRow(): Found the ratio (num_zerorows 3)/(num_localrows 4) > 0.6. Use CompressedRow routines. [0] MatSeqAIJCheckInode(): Found 2 nodes of 4. Limit used: 5. Using Inode routines 4×3 GridapPETSc.PETScMatrix: -2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 [0] MatConvert(): Calling duplicate for initial matrix seqaij 0 1 [0] MatConvert(): Calling duplicate for initial matrix seqaij 0 1 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 4-element GridapPETSc.PETScVector: 6.0 0.0 0.0 1.0 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 [0] PetscCommDuplicate(): Using internal PETSc communicator 1140850689 -2080374784 ┌ Warning: 1 objects still not finalized before calling GridapPETSc.Finalize() └ @ GridapPETSc ~/.julia/packages/GridapPETSc/PaJ7d/src/Environment.jl:45 [0] PetscFinalize(): PetscFinalize() called 6.151050 seconds (2.10 M allocations: 131.638 MiB, 3.04% gc time, 96.34% compilation time) 0 KSP Residual norm 0.0467654 1 KSP Residual norm 0.015417 2 KSP Residual norm 0.00327706 3 KSP Residual norm 0.000661172 4 KSP Residual norm 0.000252898 5 KSP Residual norm 0.00014497 6 KSP Residual norm 7.44567e-05 7 KSP Residual norm 5.31437e-05 8 KSP Residual norm 3.10604e-05 9 KSP Residual norm 1.88474e-05 10 KSP Residual norm 1.67308e-05 11 KSP Residual norm 1.30481e-05 12 KSP Residual norm 8.92208e-06 13 KSP Residual norm 6.2216e-06 14 KSP Residual norm 5.78002e-06 15 KSP Residual norm 4.40273e-06 16 KSP Residual norm 4.00261e-06 17 KSP Residual norm 2.70432e-06 18 KSP Residual norm 2.34576e-06 19 KSP Residual norm 1.94042e-06 20 KSP Residual norm 1.75753e-06 21 KSP Residual norm 1.73547e-06 22 KSP Residual norm 1.62163e-06 23 KSP Residual norm 1.25495e-06 24 KSP Residual norm 1.01893e-06 25 KSP Residual norm 8.34647e-07 26 KSP Residual norm 6.48088e-07 27 KSP Residual norm 5.14953e-07 28 KSP Residual norm 4.21736e-07 29 KSP Residual norm 3.82565e-07 30 KSP Residual norm 3.11746e-07 31 KSP Residual norm 2.49033e-07 32 KSP Residual norm 2.33299e-07 33 KSP Residual norm 2.07937e-07 34 KSP Residual norm 1.80063e-07 35 KSP Residual norm 1.38409e-07 36 KSP Residual norm 1.20657e-07 37 KSP Residual norm 9.80194e-08 38 KSP Residual norm 7.25573e-08 39 KSP Residual norm 4.91734e-08 40 KSP Residual norm 3.75952e-08 41 KSP Residual norm 2.78836e-08 42 KSP Residual norm 2.23736e-08 43 KSP Residual norm 1.57428e-08 44 KSP Residual norm 1.24789e-08 45 KSP Residual norm 9.28278e-09 46 KSP Residual norm 6.0542e-09 47 KSP Residual norm 4.92096e-09 48 KSP Residual norm 3.61052e-09 49 KSP Residual norm 3.1137e-09 50 KSP Residual norm 1.99343e-09 51 KSP Residual norm 1.768e-09 52 KSP Residual norm 1.32989e-09 53 KSP Residual norm 9.447e-10 54 KSP Residual norm 6.254e-10 55 KSP Residual norm 4.207e-10 Linear solve converged due to CONVERGED_RTOL iterations 55 WARNING! There are options you set that were not used! WARNING! could be spelling mistake, etc! There is one unused database option. It is: Option left: name:-mg_levels_esteig_ksp_type value: cg source: command line 169.955393 seconds (138.05 M allocations: 8.193 GiB, 3.09% gc time, 99.54% compilation time) 0 KSP Residual norm 7.071067811865e+00 1 KSP Residual norm 4.034745795668e-14 Linear solve converged due to CONVERGED_ITS iterations 1 250.824961 seconds (97.18 M allocations: 5.833 GiB, 1.82% gc time, 98.89% compilation time) 0 SNES Function norm 2.409822369424e+06 Linear solve converged due to CONVERGED_RTOL iterations 54 1 SNES Function norm 8.075649416004e+00 Linear solve converged due to CONVERGED_RTOL iterations 39 2 SNES Function norm 4.662170753064e-01 Linear solve converged due to CONVERGED_RTOL iterations 37 3 SNES Function norm 8.399998666394e-03 Linear solve converged due to CONVERGED_RTOL iterations 37 4 SNES Function norm 4.665302403823e-06 Linear solve converged due to CONVERGED_RTOL iterations 44 5 SNES Function norm 1.574127275597e-11 Nonlinear solve converged due to CONVERGED_FNORM_RELATIVE iterations 5 0 SNES Function norm 2.409822369424e+06 Linear solve converged due to CONVERGED_RTOL iterations 54 1 SNES Function norm 8.075649416004e+00 Linear solve converged due to CONVERGED_RTOL iterations 39 2 SNES Function norm 4.662170753064e-01 Linear solve converged due to CONVERGED_RTOL iterations 37 3 SNES Function norm 8.399998666394e-03 Linear solve converged due to CONVERGED_RTOL iterations 37 4 SNES Function norm 4.665302403823e-06 Linear solve converged due to CONVERGED_RTOL iterations 44 5 SNES Function norm 1.574127275597e-11 Nonlinear solve converged due to CONVERGED_FNORM_RELATIVE iterations 5 47.788631 seconds (26.49 M allocations: 1.366 GiB, 2.61% gc time, 94.17% compilation time) 0 KSP Residual norm 8.550000000000e-03 1 KSP Residual norm 8.968352924103e-04 2 KSP Residual norm 9.188071216687e-05 3 KSP Residual norm 7.483127648168e-06 4 KSP Residual norm 6.216337456339e-07 5 KSP Residual norm 5.262981415328e-08 6 KSP Residual norm 4.447870837457e-09 7 KSP Residual norm 3.660520650081e-10 8 KSP Residual norm 3.000628543014e-11 9 KSP Residual norm 2.545610900010e-12 10 KSP Residual norm 2.084997771778e-13 Linear solve converged due to CONVERGED_RTOL iterations 10 KSP Object: 1 MPI process type: cg maximum iterations=1000, initial guess is zero tolerances: relative=1e-10, absolute=1e-50, divergence=10000. left preconditioning using UNPRECONDITIONED norm type for convergence test PC Object: 1 MPI process type: gamg type is MULTIPLICATIVE, levels=3 cycles=v Cycles per PCApply=1 Using externally compute Galerkin coarse grid matrices GAMG specific options Threshold for dropping small values in graph on each level = -1. -1. -1. Threshold scaling factor for each level not specified = 1. AGG specific options Number of levels of aggressive coarsening 0 MatCoarsen Object: (pc_gamg_) 1 MPI process type: misk Number smoothing steps to construct prolongation 1 Complexity: grid = 1.11385 operator = 1.15878 Per-level complexity: op = operator, int = interpolation #equations | #active PEs | avg nnz/row op | avg nnz/row int 7 1 7 0 76 1 32 4 729 1 22 6 Coarse grid solver -- level 0 ------------------------------- KSP Object: (mg_coarse_) 1 MPI process type: preonly maximum iterations=10000, initial guess is zero tolerances: relative=1e-05, absolute=1e-50, divergence=10000. left preconditioning using NONE norm type for convergence test PC Object: (mg_coarse_) 1 MPI process type: bjacobi number of blocks = 1 Local solver information for first block is in the following KSP and PC objects on rank 0: Use -mg_coarse_ksp_view ::ascii_info_detail to display information for all blocks KSP Object: (mg_coarse_sub_) 1 MPI process type: preonly maximum iterations=1, initial guess is zero tolerances: relative=1e-05, absolute=1e-50, divergence=10000. left preconditioning using NONE norm type for convergence test PC Object: (mg_coarse_sub_) 1 MPI process type: cholesky out-of-place factorization tolerance for zero pivot 2.22045e-14 matrix ordering: nd factor fill ratio given 5., needed 1. Factored matrix follows: Mat Object: (mg_coarse_sub_) 1 MPI process type: seqsbaij rows=7, cols=7 package used to perform factorization: petsc total: nonzeros=28, allocated nonzeros=28 block size is 1 linear system matrix = precond matrix: Mat Object: (mg_coarse_sub_) 1 MPI process type: seqaij rows=7, cols=7 total: nonzeros=49, allocated nonzeros=49 total number of mallocs used during MatSetValues calls=0 using I-node routines: found 2 nodes, limit used is 5 linear system matrix = precond matrix: Mat Object: (mg_coarse_sub_) 1 MPI process type: seqaij rows=7, cols=7 total: nonzeros=49, allocated nonzeros=49 total number of mallocs used during MatSetValues calls=0 using I-node routines: found 2 nodes, limit used is 5 Down solver (pre-smoother) on level 1 ------------------------------- KSP Object: (mg_levels_1_) 1 MPI process type: chebyshev Chebyshev polynomial of first kind eigenvalue targets used: min 0.255929, max 2.81522 eigenvalues provided (min 0.325601, max 2.55929) with transform: [0. 0.1; 0. 1.1] maximum iterations=2, nonzero initial guess tolerances: relative=1e-05, absolute=1e-50, divergence=10000. left preconditioning using NONE norm type for convergence test PC Object: (mg_levels_1_) 1 MPI process type: jacobi type DIAGONAL linear system matrix = precond matrix: Mat Object: 1 MPI process type: seqaij rows=76, cols=76 total: nonzeros=2432, allocated nonzeros=2432 total number of mallocs used during MatSetValues calls=0 not using I-node routines Up solver (post-smoother) same as down solver (pre-smoother) Down solver (pre-smoother) on level 2 ------------------------------- KSP Object: (mg_levels_2_) 1 MPI process type: chebyshev Chebyshev polynomial of first kind eigenvalue targets used: min 0.143003, max 1.57304 eigenvalues provided (min 0.109061, max 1.43003) with transform: [0. 0.1; 0. 1.1] maximum iterations=2, nonzero initial guess tolerances: relative=1e-05, absolute=1e-50, divergence=10000. left preconditioning using NONE norm type for convergence test PC Object: (mg_levels_2_) 1 MPI process type: jacobi type DIAGONAL linear system matrix = precond matrix: Mat Object: 1 MPI process type: seqaij rows=729, cols=729 total: nonzeros=15625, allocated nonzeros=0 total number of mallocs used during MatSetValues calls=0 not using I-node routines Up solver (post-smoother) same as down solver (pre-smoother) linear system matrix = precond matrix: Mat Object: 1 MPI process type: seqaij rows=729, cols=729 total: nonzeros=15625, allocated nonzeros=0 total number of mallocs used during MatSetValues calls=0 not using I-node routines 0 KSP Residual norm 8.550000000000e-03 1 KSP Residual norm 8.968352924103e-04 2 KSP Residual norm 9.188071216687e-05 3 KSP Residual norm 7.483127648168e-06 4 KSP Residual norm 6.216337456339e-07 5 KSP Residual norm 5.262981415328e-08 6 KSP Residual norm 4.447870837457e-09 7 KSP Residual norm 3.660520650081e-10 8 KSP Residual norm 3.000628543014e-11 9 KSP Residual norm 2.545610900010e-12 10 KSP Residual norm 2.084997771778e-13 Linear solve converged due to CONVERGED_RTOL iterations 10 KSP Object: 1 MPI process type: cg maximum iterations=1000, initial guess is zero tolerances: relative=1e-10, absolute=1e-50, divergence=10000. left preconditioning using UNPRECONDITIONED norm type for convergence test PC Object: 1 MPI process type: gamg type is MULTIPLICATIVE, levels=3 cycles=v Cycles per PCApply=1 Using externally compute Galerkin coarse grid matrices GAMG specific options Threshold for dropping small values in graph on each level = -1. -1. -1. Threshold scaling factor for each level not specified = 1. AGG specific options Number of levels of aggressive coarsening 0 MatCoarsen Object: (pc_gamg_) 1 MPI process type: misk Number smoothing steps to construct prolongation 1 Complexity: grid = 1.11385 operator = 1.15878 Per-level complexity: op = operator, int = interpolation #equations | #active PEs | avg nnz/row op | avg nnz/row int 7 1 7 0 76 1 32 4 729 1 22 6 Coarse grid solver -- level 0 ------------------------------- KSP Object: (mg_coarse_) 1 MPI process type: preonly maximum iterations=10000, initial guess is zero tolerances: relative=1e-05, absolute=1e-50, divergence=10000. left preconditioning using NONE norm type for convergence test PC Object: (mg_coarse_) 1 MPI process type: bjacobi number of blocks = 1 Local solver information for first block is in the following KSP and PC objects on rank 0: Use -mg_coarse_ksp_view ::ascii_info_detail to display information for all blocks KSP Object: (mg_coarse_sub_) 1 MPI process type: preonly maximum iterations=1, initial guess is zero tolerances: relative=1e-05, absolute=1e-50, divergence=10000. left preconditioning using NONE norm type for convergence test PC Object: (mg_coarse_sub_) 1 MPI process type: cholesky out-of-place factorization tolerance for zero pivot 2.22045e-14 matrix ordering: nd factor fill ratio given 5., needed 1. Factored matrix follows: Mat Object: (mg_coarse_sub_) 1 MPI process type: seqsbaij rows=7, cols=7 package used to perform factorization: petsc total: nonzeros=28, allocated nonzeros=28 block size is 1 linear system matrix = precond matrix: Mat Object: (mg_coarse_sub_) 1 MPI process type: seqaij rows=7, cols=7 total: nonzeros=49, allocated nonzeros=49 total number of mallocs used during MatSetValues calls=0 using I-node routines: found 2 nodes, limit used is 5 linear system matrix = precond matrix: Mat Object: (mg_coarse_sub_) 1 MPI process type: seqaij rows=7, cols=7 total: nonzeros=49, allocated nonzeros=49 total number of mallocs used during MatSetValues calls=0 using I-node routines: found 2 nodes, limit used is 5 Down solver (pre-smoother) on level 1 ------------------------------- KSP Object: (mg_levels_1_) 1 MPI process type: chebyshev Chebyshev polynomial of first kind eigenvalue targets used: min 0.255929, max 2.81522 eigenvalues provided (min 0.325601, max 2.55929) with transform: [0. 0.1; 0. 1.1] maximum iterations=2, nonzero initial guess tolerances: relative=1e-05, absolute=1e-50, divergence=10000. left preconditioning using NONE norm type for convergence test PC Object: (mg_levels_1_) 1 MPI process type: jacobi type DIAGONAL linear system matrix = precond matrix: Mat Object: 1 MPI process type: seqaij rows=76, cols=76 total: nonzeros=2432, allocated nonzeros=2432 total number of mallocs used during MatSetValues calls=0 not using I-node routines Up solver (post-smoother) same as down solver (pre-smoother) Down solver (pre-smoother) on level 2 ------------------------------- KSP Object: (mg_levels_2_) 1 MPI process type: chebyshev Chebyshev polynomial of first kind eigenvalue targets used: min 0.143003, max 1.57304 eigenvalues provided (min 0.109061, max 1.43003) with transform: [0. 0.1; 0. 1.1] maximum iterations=2, nonzero initial guess tolerances: relative=1e-05, absolute=1e-50, divergence=10000. left preconditioning using NONE norm type for convergence test PC Object: (mg_levels_2_) 1 MPI process type: jacobi type DIAGONAL linear system matrix = precond matrix: Mat Object: 1 MPI process type: seqaij rows=729, cols=729 total: nonzeros=15625, allocated nonzeros=0 total number of mallocs used during MatSetValues calls=0 not using I-node routines Up solver (post-smoother) same as down solver (pre-smoother) linear system matrix = precond matrix: Mat Object: 1 MPI process type: seqaij rows=729, cols=729 total: nonzeros=15625, allocated nonzeros=0 total number of mallocs used during MatSetValues calls=0 not using I-node routines 0 KSP Residual norm 8.550000000000e-03 1 KSP Residual norm 8.968352924103e-04 2 KSP Residual norm 9.188071216687e-05 3 KSP Residual norm 7.483127648168e-06 4 KSP Residual norm 6.216337456339e-07 5 KSP Residual norm 5.262981415328e-08 6 KSP Residual norm 4.447870837457e-09 7 KSP Residual norm 3.660520650081e-10 8 KSP Residual norm 3.000628543014e-11 9 KSP Residual norm 2.545610900010e-12 10 KSP Residual norm 2.084997771778e-13 Linear solve converged due to CONVERGED_RTOL iterations 10 KSP Object: 1 MPI process type: cg maximum iterations=1000, initial guess is zero tolerances: relative=1e-10, absolute=1e-50, divergence=10000. left preconditioning using UNPRECONDITIONED norm type for convergence test PC Object: 1 MPI process type: gamg type is MULTIPLICATIVE, levels=3 cycles=v Cycles per PCApply=1 Using externally compute Galerkin coarse grid matrices GAMG specific options Threshold for dropping small values in graph on each level = -1. -1. -1. Threshold scaling factor for each level not specified = 1. AGG specific options Number of levels of aggressive coarsening 0 MatCoarsen Object: (pc_gamg_) 1 MPI process type: misk Number smoothing steps to construct prolongation 1 Complexity: grid = 1.11385 operator = 1.15878 Per-level complexity: op = operator, int = interpolation #equations | #active PEs | avg nnz/row op | avg nnz/row int 7 1 7 0 76 1 32 4 729 1 22 6 Coarse grid solver -- level 0 ------------------------------- KSP Object: (mg_coarse_) 1 MPI process type: preonly maximum iterations=10000, initial guess is zero tolerances: relative=1e-05, absolute=1e-50, divergence=10000. left preconditioning using NONE norm type for convergence test PC Object: (mg_coarse_) 1 MPI process type: bjacobi number of blocks = 1 Local solver information for first block is in the following KSP and PC objects on rank 0: Use -mg_coarse_ksp_view ::ascii_info_detail to display information for all blocks KSP Object: (mg_coarse_sub_) 1 MPI process type: preonly maximum iterations=1, initial guess is zero tolerances: relative=1e-05, absolute=1e-50, divergence=10000. left preconditioning using NONE norm type for convergence test PC Object: (mg_coarse_sub_) 1 MPI process type: cholesky out-of-place factorization tolerance for zero pivot 2.22045e-14 matrix ordering: nd factor fill ratio given 5., needed 1. Factored matrix follows: Mat Object: (mg_coarse_sub_) 1 MPI process type: seqsbaij rows=7, cols=7 package used to perform factorization: petsc total: nonzeros=28, allocated nonzeros=28 block size is 1 linear system matrix = precond matrix: Mat Object: (mg_coarse_sub_) 1 MPI process type: seqaij rows=7, cols=7 total: nonzeros=49, allocated nonzeros=49 total number of mallocs used during MatSetValues calls=0 using I-node routines: found 2 nodes, limit used is 5 linear system matrix = precond matrix: Mat Object: (mg_coarse_sub_) 1 MPI process type: seqaij rows=7, cols=7 total: nonzeros=49, allocated nonzeros=49 total number of mallocs used during MatSetValues calls=0 using I-node routines: found 2 nodes, limit used is 5 Down solver (pre-smoother) on level 1 ------------------------------- KSP Object: (mg_levels_1_) 1 MPI process type: chebyshev Chebyshev polynomial of first kind eigenvalue targets used: min 0.255929, max 2.81522 eigenvalues provided (min 0.325601, max 2.55929) with transform: [0. 0.1; 0. 1.1] maximum iterations=2, nonzero initial guess tolerances: relative=1e-05, absolute=1e-50, divergence=10000. left preconditioning using NONE norm type for convergence test PC Object: (mg_levels_1_) 1 MPI process type: jacobi type DIAGONAL linear system matrix = precond matrix: Mat Object: 1 MPI process type: seqaij rows=76, cols=76 total: nonzeros=2432, allocated nonzeros=2432 total number of mallocs used during MatSetValues calls=0 not using I-node routines Up solver (post-smoother) same as down solver (pre-smoother) Down solver (pre-smoother) on level 2 ------------------------------- KSP Object: (mg_levels_2_) 1 MPI process type: chebyshev Chebyshev polynomial of first kind eigenvalue targets used: min 0.143003, max 1.57304 eigenvalues provided (min 0.109061, max 1.43003) with transform: [0. 0.1; 0. 1.1] maximum iterations=2, nonzero initial guess tolerances: relative=1e-05, absolute=1e-50, divergence=10000. left preconditioning using NONE norm type for convergence test PC Object: (mg_levels_2_) 1 MPI process type: jacobi type DIAGONAL linear system matrix = precond matrix: Mat Object: 1 MPI process type: seqaij rows=729, cols=729 total: nonzeros=15625, allocated nonzeros=39304 total number of mallocs used during MatSetValues calls=0 not using I-node routines Up solver (post-smoother) same as down solver (pre-smoother) linear system matrix = precond matrix: Mat Object: 1 MPI process type: seqaij rows=729, cols=729 total: nonzeros=15625, allocated nonzeros=39304 total number of mallocs used during MatSetValues calls=0 not using I-node routines 0 KSP Residual norm 8.550000000000e-03 1 KSP Residual norm 8.968352924103e-04 2 KSP Residual norm 9.188071216687e-05 3 KSP Residual norm 7.483127648168e-06 4 KSP Residual norm 6.216337456339e-07 5 KSP Residual norm 5.262981415328e-08 6 KSP Residual norm 4.447870837457e-09 7 KSP Residual norm 3.660520650081e-10 8 KSP Residual norm 3.000628543014e-11 9 KSP Residual norm 2.545610900010e-12 10 KSP Residual norm 2.084997771778e-13 Linear solve converged due to CONVERGED_RTOL iterations 10 KSP Object: 1 MPI process type: cg maximum iterations=1000, initial guess is zero tolerances: relative=1e-10, absolute=1e-50, divergence=10000. left preconditioning using UNPRECONDITIONED norm type for convergence test PC Object: 1 MPI process type: gamg type is MULTIPLICATIVE, levels=3 cycles=v Cycles per PCApply=1 Using externally compute Galerkin coarse grid matrices GAMG specific options Threshold for dropping small values in graph on each level = -1. -1. -1. Threshold scaling factor for each level not specified = 1. AGG specific options Number of levels of aggressive coarsening 0 MatCoarsen Object: (pc_gamg_) 1 MPI process type: misk Number smoothing steps to construct prolongation 1 Complexity: grid = 1.11385 operator = 1.15878 Per-level complexity: op = operator, int = interpolation #equations | #active PEs | avg nnz/row op | avg nnz/row int 7 1 7 0 76 1 32 4 729 1 22 6 Coarse grid solver -- level 0 ------------------------------- KSP Object: (mg_coarse_) 1 MPI process type: preonly maximum iterations=10000, initial guess is zero tolerances: relative=1e-05, absolute=1e-50, divergence=10000. left preconditioning using NONE norm type for convergence test PC Object: (mg_coarse_) 1 MPI process type: bjacobi number of blocks = 1 Local solver information for first block is in the following KSP and PC objects on rank 0: Use -mg_coarse_ksp_view ::ascii_info_detail to display information for all blocks KSP Object: (mg_coarse_sub_) 1 MPI process type: preonly maximum iterations=1, initial guess is zero tolerances: relative=1e-05, absolute=1e-50, divergence=10000. left preconditioning using NONE norm type for convergence test PC Object: (mg_coarse_sub_) 1 MPI process type: cholesky out-of-place factorization tolerance for zero pivot 2.22045e-14 matrix ordering: nd factor fill ratio given 5., needed 1. Factored matrix follows: Mat Object: (mg_coarse_sub_) 1 MPI process type: seqsbaij rows=7, cols=7 package used to perform factorization: petsc total: nonzeros=28, allocated nonzeros=28 block size is 1 linear system matrix = precond matrix: Mat Object: (mg_coarse_sub_) 1 MPI process type: seqaij rows=7, cols=7 total: nonzeros=49, allocated nonzeros=49 total number of mallocs used during MatSetValues calls=0 using I-node routines: found 2 nodes, limit used is 5 linear system matrix = precond matrix: Mat Object: (mg_coarse_sub_) 1 MPI process type: seqaij rows=7, cols=7 total: nonzeros=49, allocated nonzeros=49 total number of mallocs used during MatSetValues calls=0 using I-node routines: found 2 nodes, limit used is 5 Down solver (pre-smoother) on level 1 ------------------------------- KSP Object: (mg_levels_1_) 1 MPI process type: chebyshev Chebyshev polynomial of first kind eigenvalue targets used: min 0.255929, max 2.81522 eigenvalues provided (min 0.325601, max 2.55929) with transform: [0. 0.1; 0. 1.1] maximum iterations=2, nonzero initial guess tolerances: relative=1e-05, absolute=1e-50, divergence=10000. left preconditioning using NONE norm type for convergence test PC Object: (mg_levels_1_) 1 MPI process type: jacobi type DIAGONAL linear system matrix = precond matrix: Mat Object: 1 MPI process type: seqaij rows=76, cols=76 total: nonzeros=2432, allocated nonzeros=2432 total number of mallocs used during MatSetValues calls=0 not using I-node routines Up solver (post-smoother) same as down solver (pre-smoother) Down solver (pre-smoother) on level 2 ------------------------------- KSP Object: (mg_levels_2_) 1 MPI process type: chebyshev Chebyshev polynomial of first kind eigenvalue targets used: min 0.143003, max 1.57304 eigenvalues provided (min 0.109061, max 1.43003) with transform: [0. 0.1; 0. 1.1] maximum iterations=2, nonzero initial guess tolerances: relative=1e-05, absolute=1e-50, divergence=10000. left preconditioning using NONE norm type for convergence test PC Object: (mg_levels_2_) 1 MPI process type: jacobi type DIAGONAL linear system matrix = precond matrix: Mat Object: 1 MPI process type: seqaij rows=729, cols=729 total: nonzeros=15625, allocated nonzeros=39304 total number of mallocs used during MatSetValues calls=0 not using I-node routines Up solver (post-smoother) same as down solver (pre-smoother) linear system matrix = precond matrix: Mat Object: 1 MPI process type: seqaij rows=729, cols=729 total: nonzeros=15625, allocated nonzeros=39304 total number of mallocs used during MatSetValues calls=0 not using I-node routines WARNING! There are options you set that were not used! WARNING! could be spelling mistake, etc! There is one unused database option. It is: Option left: name:-mg_levels_esteig_ksp_type value: cg source: command line 24.817072 seconds (7.19 M allocations: 434.707 MiB, 4.22% gc time, 94.78% compilation time) Test Summary: | Pass Total Time SERIAL | 132 132 9m12.4s 553.058035 seconds (287.81 M allocations: 16.976 GiB, 2.49% gc time, 95.53% compilation time: <1% of which was recompilation) [1, 2, 3, 5, 6] [4, 5, 7, 3, 6] [6, 7, 4] Vec Object: 1 MPI process type: seq 10. 20. 30. 40. 50. 60. 70. 3-element PartitionedArrays.DebugArray{SparseArrays.SparseMatrixCSC{Float64, Int64}, 1}: [1] = sparse([1, 2, 3, 1, 3], [1, 2, 3, 4, 5], [9.0, 9.0, 9.0, 1.0, 1.0], 5, 5) [2] = sparse([1, 2, 2, 2, 1], [1, 2, 3, 4, 5], [9.0, 9.0, 1.0, 9.0, 1.0], 5, 5) [3] = sparse([1, 2, 1], [1, 2, 3], [9.0, 9.0, 1.0], 3, 3) 3-element PartitionedArrays.DebugArray{SparseArrays.SparseMatrixCSC{Float64, Int64}, 1}: [1] = sparse([1, 2, 3, 5, 4, 6, 1, 5, 3, 4, 6, 5, 7], [1, 2, 3, 3, 4, 4, 5, 5, 6, 6, 6, 7, 7], [9.0, 9.0, 9.0, 9.0, 9.0, 1.0, 1.0, 9.0, 1.0, 1.0, 9.0, 1.0, 9.0], 7, 7) [2] = sparse(Int64[], Int64[], Float64[], 2, 2) [3] = sparse(Int64[], Int64[], Float64[], 2, 2) Mat Object: 1 MPI process type: seqaij row 0: (0, 9.) (4, 1.) row 1: (1, 9.) row 2: (2, 9.) (5, 1.) row 3: (3, 9.) (5, 1.) row 4: (2, 9.) (4, 9.) (6, 1.) row 5: (3, 1.) (5, 9.) row 6: (6, 9.) Linear solve converged due to CONVERGED_RTOL iterations 5 Vec Object: 1 MPI process type: seq 10. 20. 30. 40. 50. 60. 70. Vec Object: 1 MPI process type: seq 140. 180. 330. 420. 790. 580. 630. Linear solve converged due to CONVERGED_RTOL iterations 5 Vec Object: 1 MPI process type: seq 140. 180. 330. 420. 790. 580. 630. Vec Object: 1 MPI process type: seq 10. 20. 30. 40. 50. 60. 70. Vec Object: 1 MPI process type: seq 10. 20. 30. 40. 50. 60. 70. Linear solve converged due to CONVERGED_RTOL iterations 5 Vec Object: 1 MPI process type: seq 10. 20. 30. 40. 50. 60. 70. Vec Object: 1 MPI process type: seq 140. 180. 330. 420. 790. 580. 630. Linear solve converged due to CONVERGED_RTOL iterations 5 Vec Object: 1 MPI process type: seq 140. 180. 330. 420. 790. 580. 630. Vec Object: 1 MPI process type: seq 10. 20. 30. 40. 50. 60. 70. Vec Object: 1 MPI process type: seq 10. 20. 30. 40. 50. 60. 70. 97.853234 seconds (37.57 M allocations: 2.293 GiB, 3.03% gc time, 75.64% compilation time) PoissonTests: Error During Test at /home/pkgeval/.julia/packages/GridapPETSc/PaJ7d/test/sequential/runtests.jl:12 Got exception outside of a @test LoadError: promotion of types Float64, Gridap.TensorValues.VectorValue{2, Float64} and Gridap.TensorValues.VectorValue{2, Float64} failed to change any arguments Stacktrace: [1] error(::String, ::String, ::String) @ Base error.jl:56 [2] sametype_error(input::Tuple{Float64, Gridap.TensorValues.VectorValue{2, Float64}, Gridap.TensorValues.VectorValue{2, Float64}}) @ Base promotion.jl:449 [3] not_sametype(x::Tuple{Float64, Gridap.TensorValues.VectorValue{2, Float64}, Gridap.TensorValues.VectorValue{2, Float64}}, y::Tuple{Float64, Gridap.TensorValues.VectorValue{2, Float64}, Gridap.TensorValues.VectorValue{2, Float64}}) @ Base promotion.jl:443 [4] promote(x::Float64, y::Gridap.TensorValues.VectorValue{2, Float64}, z::Gridap.TensorValues.VectorValue{2, Float64}) @ Base promotion.jl:432 [inlined] [5] muladd(a::Float64, b::Gridap.TensorValues.VectorValue{2, Float64}, c::Gridap.TensorValues.VectorValue{2, Float64}) @ Base promotion.jl:511 [6] __generic_matvecmul!(::typeof(identity), C::Vector{Gridap.TensorValues.VectorValue{2, Float64}}, A::Matrix{Float64}, B::Vector{Gridap.TensorValues.VectorValue{2, Float64}}, alpha::Bool, beta::Bool) @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/matmul.jl:1121 [7] _generic_matvecmul!(C::Vector{Gridap.TensorValues.VectorValue{2, Float64}}, tA::Char, A::Matrix{Float64}, B::Vector{Gridap.TensorValues.VectorValue{2, Float64}}, alpha::Bool, beta::Bool) @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/matmul.jl:1140 [inlined] [8] generic_matvecmul!(C::Vector{Gridap.TensorValues.VectorValue{2, Float64}}, tA::Char, A::Matrix{Float64}, B::Vector{Gridap.TensorValues.VectorValue{2, Float64}}, alpha::Bool, beta::Bool) @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/matmul.jl:1074 [inlined] [9] mul!(C::Vector{Gridap.TensorValues.VectorValue{2, Float64}}, tA::Char, A::Matrix{Float64}, B::Vector{Gridap.TensorValues.VectorValue{2, Float64}}, alpha::Bool, beta::Bool) @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/matmul.jl:1066 [inlined] [10] _mul!(y::Vector{Gridap.TensorValues.VectorValue{2, Float64}}, A::Matrix{Float64}, x::Vector{Gridap.TensorValues.VectorValue{2, Float64}}, alpha::Bool, beta::Bool) @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/matmul.jl:74 [inlined] [11] mul!(y::Vector{Gridap.TensorValues.VectorValue{2, Float64}}, A::Matrix{Float64}, x::Vector{Gridap.TensorValues.VectorValue{2, Float64}}, alpha::Bool, beta::Bool) @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/matmul.jl:71 [inlined] [12] mul!(C::Vector{Gridap.TensorValues.VectorValue{2, Float64}}, A::Matrix{Float64}, B::Vector{Gridap.TensorValues.VectorValue{2, Float64}}) @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/matmul.jl:281 [inlined] [13] *(A::Matrix{Float64}, x::Vector{Gridap.TensorValues.VectorValue{2, Float64}}) @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/matmul.jl:61 [inlined] [14] _compute_high_order_nodes_dim_d!(nodes::Vector{Gridap.TensorValues.VectorValue{2, Float64}}, facenodes::Vector{Vector{Int64}}, p::Gridap.ReferenceFEs.ExtrusionPolytope{2}, orders::Tuple{Int64, Int64}, ::Val{1}) @ Gridap.ReferenceFEs ~/.julia/packages/Gridap/FDYkr/src/ReferenceFEs/CLagrangianRefFEs.jl:528 [15] _compute_high_order_nodes(p::Gridap.ReferenceFEs.ExtrusionPolytope{2}, orders::Tuple{Int64, Int64}) @ Gridap.ReferenceFEs ~/.julia/packages/Gridap/FDYkr/src/ReferenceFEs/CLagrangianRefFEs.jl:498 [16] _compute_nodes(p::Gridap.ReferenceFEs.ExtrusionPolytope{2}, orders::Tuple{Int64, Int64}) @ Gridap.ReferenceFEs ~/.julia/packages/Gridap/FDYkr/src/ReferenceFEs/CLagrangianRefFEs.jl:472 [17] compute_nodes(p::Gridap.ReferenceFEs.ExtrusionPolytope{2}, orders::Tuple{Int64, Int64}) @ Gridap.ReferenceFEs ~/.julia/packages/Gridap/FDYkr/src/ReferenceFEs/CLagrangianRefFEs.jl:663 [18] _lagrangian_ref_fe(::Core.TypeEgal{Float64}, p::Gridap.ReferenceFEs.ExtrusionPolytope{2}, orders::Tuple{Int64, Int64}, poly_type::Type) @ Gridap.ReferenceFEs ~/.julia/packages/Gridap/FDYkr/src/ReferenceFEs/CLagrangianRefFEs.jl:249 [19] Gridap.ReferenceFEs.LagrangianRefFE(::Core.TypeEgal{Float64}, p::Gridap.ReferenceFEs.ExtrusionPolytope{2}, orders::Tuple{Int64, Int64}; space::Symbol, poly_type::Type) @ Gridap.ReferenceFEs ~/.julia/packages/Gridap/FDYkr/src/ReferenceFEs/CLagrangianRefFEs.jl:222 [20] Gridap.ReferenceFEs.LagrangianRefFE(::Core.TypeEgal{Float64}, p::Gridap.ReferenceFEs.ExtrusionPolytope{2}, order::Int64; space::Symbol, poly_type::Type) @ Gridap.ReferenceFEs ~/.julia/packages/Gridap/FDYkr/src/ReferenceFEs/CLagrangianRefFEs.jl:359 [21] Gridap.ReferenceFEs.LagrangianRefFE(::Core.TypeEgal{Float64}, p::Gridap.ReferenceFEs.ExtrusionPolytope{2}, order::Int64) @ Gridap.ReferenceFEs ~/.julia/packages/Gridap/FDYkr/src/ReferenceFEs/CLagrangianRefFEs.jl:355 [inlined] [22] Gridap.ReferenceFEs.ReferenceFE(polytope::Gridap.ReferenceFEs.ExtrusionPolytope{2}, ::Gridap.ReferenceFEs.Lagrangian, ::Core.TypeEgal{Float64}, orders::Int64; kwargs::@Kwargs{}) @ Gridap.ReferenceFEs ~/.julia/packages/Gridap/FDYkr/src/ReferenceFEs/CLagrangianRefFEs.jl:242 [inlined] [23] Gridap.ReferenceFEs.ReferenceFE(polytope::Gridap.ReferenceFEs.ExtrusionPolytope{2}, ::Gridap.ReferenceFEs.Lagrangian, ::Core.TypeEgal{Float64}, orders::Int64) @ Gridap.ReferenceFEs ~/.julia/packages/Gridap/FDYkr/src/ReferenceFEs/CLagrangianRefFEs.jl:235 [24] (::Gridap.Geometry.var"#8#9"{@Kwargs{}, Tuple{Gridap.ReferenceFEs.Lagrangian, DataType, Int64}})(p::Gridap.ReferenceFEs.ExtrusionPolytope{2}) @ Gridap.Geometry ~/.julia/packages/Gridap/FDYkr/src/Geometry/Grids.jl:273 [25] iterate(::Base.Generator{Vector{Gridap.ReferenceFEs.Polytope{2}}, Gridap.Geometry.var"#8#9"{@Kwargs{}, Tuple{Gridap.ReferenceFEs.Lagrangian, DataType, Int64}}}) @ Base generator.jl:49 [inlined] [26] _collect(c::Vector{Gridap.ReferenceFEs.Polytope{2}}, itr::Base.Generator{Vector{Gridap.ReferenceFEs.Polytope{2}}, Gridap.Geometry.var"#8#9"{@Kwargs{}, Tuple{Gridap.ReferenceFEs.Lagrangian, DataType, Int64}}}, ::Base.EltypeUnknown, isz::Base.HasShape{1}) @ Base array.jl:859 [27] collect_similar(cont::Vector{Gridap.ReferenceFEs.Polytope{2}}, itr::Base.Generator{Vector{Gridap.ReferenceFEs.Polytope{2}}, Gridap.Geometry.var"#8#9"{@Kwargs{}, Tuple{Gridap.ReferenceFEs.Lagrangian, DataType, Int64}}}) @ Base array.jl:774 [inlined] [28] map(f::Gridap.Geometry.var"#8#9"{@Kwargs{}, Tuple{Gridap.ReferenceFEs.Lagrangian, DataType, Int64}}, A::Vector{Gridap.ReferenceFEs.Polytope{2}}) @ Base abstractarray.jl:3491 [inlined] [29] Gridap.ReferenceFEs.ReferenceFE(::Gridap.Geometry.CartesianDiscreteModel{2, Float64, typeof(identity)}, ::Gridap.ReferenceFEs.Lagrangian, ::DataType, ::Int64; kwargs::@Kwargs{}) @ Gridap.Geometry ~/.julia/packages/Gridap/FDYkr/src/Geometry/Grids.jl:273 [30] Gridap.ReferenceFEs.ReferenceFE(::Gridap.Geometry.CartesianDiscreteModel{2, Float64, typeof(identity)}, ::Gridap.ReferenceFEs.Lagrangian, ::DataType, ::Int64) @ Gridap.Geometry ~/.julia/packages/Gridap/FDYkr/src/Geometry/Grids.jl:270 [inlined] [31] Gridap.ReferenceFEs.ReferenceFE(trian::Gridap.Geometry.CartesianDiscreteModel{2, Float64, typeof(identity)}, reffe::Tuple{Gridap.ReferenceFEs.Lagrangian, Tuple{DataType, Int64}, @Kwargs{}}) @ Gridap.Geometry ~/.julia/packages/Gridap/FDYkr/src/Geometry/Grids.jl:288 [inlined] [32] (::GridapDistributed.var"#520#521"{Tuple{Gridap.ReferenceFEs.Lagrangian, Tuple{DataType, Int64}, @Kwargs{}}})(model::Gridap.Geometry.CartesianDiscreteModel{2, Float64, typeof(identity)}) @ GridapDistributed ~/.julia/packages/GridapDistributed/O0Pts/src/FESpaces.jl:593 [inlined] [33] iterate(::Base.Generator{Vector{Gridap.Geometry.CartesianDiscreteModel{2, Float64, typeof(identity)}}, GridapDistributed.var"#520#521"{Tuple{Gridap.ReferenceFEs.Lagrangian, Tuple{DataType, Int64}, @Kwargs{}}}}) @ Base generator.jl:49 [inlined] [34] _collect(c::Vector{Gridap.Geometry.CartesianDiscreteModel{2, Float64, typeof(identity)}}, itr::Base.Generator{Vector{Gridap.Geometry.CartesianDiscreteModel{2, Float64, typeof(identity)}}, GridapDistributed.var"#520#521"{Tuple{Gridap.ReferenceFEs.Lagrangian, Tuple{DataType, Int64}, @Kwargs{}}}}, ::Base.EltypeUnknown, isz::Base.HasShape{1}) @ Base array.jl:859 [35] collect_similar(cont::Vector{Gridap.Geometry.CartesianDiscreteModel{2, Float64, typeof(identity)}}, itr::Base.Generator{Vector{Gridap.Geometry.CartesianDiscreteModel{2, Float64, typeof(identity)}}, GridapDistributed.var"#520#521"{Tuple{Gridap.ReferenceFEs.Lagrangian, Tuple{DataType, Int64}, @Kwargs{}}}}) @ Base array.jl:774 [36] map(f::Function, A::Vector{Gridap.Geometry.CartesianDiscreteModel{2, Float64, typeof(identity)}}) @ Base abstractarray.jl:3491 [37] map(f::Function, args::PartitionedArrays.DebugArray{Gridap.Geometry.CartesianDiscreteModel{2, Float64, typeof(identity)}, 1}) @ PartitionedArrays ~/.julia/packages/PartitionedArrays/MVmxR/src/debug_array.jl:105 [38] GridapDistributed.DistributedSingleFieldFESpace(model::GridapDistributed.GenericDistributedDiscreteModel{2, 2, PartitionedArrays.DebugArray{Gridap.Geometry.CartesianDiscreteModel{2, Float64, typeof(identity)}, 1}, Vector{PartitionedArrays.PRange}, GridapDistributed.DistributedCartesianDescriptor{PartitionedArrays.DebugArray{Int64, 1}, Tuple{Int64, Int64}, Gridap.Geometry.CartesianDescriptor{2, Float64, typeof(identity)}, Tuple{Bool, Bool}}}, trian::GridapDistributed.DistributedTriangulation{2, 2, PartitionedArrays.DebugArray{Gridap.Geometry.BodyFittedTriangulation{2, 2, Gridap.Geometry.CartesianDiscreteModel{2, Float64, typeof(identity)}, Gridap.Geometry.CartesianGrid{2, Float64, typeof(identity)}, Gridap.Arrays.IdentityVector{Int64}}, 1}, GridapDistributed.GenericDistributedDiscreteModel{2, 2, PartitionedArrays.DebugArray{Gridap.Geometry.CartesianDiscreteModel{2, Float64, typeof(identity)}, 1}, Vector{PartitionedArrays.PRange}, GridapDistributed.DistributedCartesianDescriptor{PartitionedArrays.DebugArray{Int64, 1}, Tuple{Int64, Int64}, Gridap.Geometry.CartesianDescriptor{2, Float64, typeof(identity)}, Tuple{Bool, Bool}}}, Nothing}, cell_gids::PartitionedArrays.PRange{PartitionedArrays.DebugArray{PartitionedArrays.PermutedLocalIndices{PartitionedArrays.LocalIndicesWithConstantBlockSize{2}}, 1}}, reffe::Tuple{Gridap.ReferenceFEs.Lagrangian, Tuple{DataType, Int64}, @Kwargs{}}; kwargs::@Kwargs{dirichlet_tags::String}) @ GridapDistributed ~/.julia/packages/GridapDistributed/O0Pts/src/FESpaces.jl:592 [39] Gridap.FESpaces.FESpace(model::GridapDistributed.GenericDistributedDiscreteModel{2, 2, PartitionedArrays.DebugArray{Gridap.Geometry.CartesianDiscreteModel{2, Float64, typeof(identity)}, 1}, Vector{PartitionedArrays.PRange}, GridapDistributed.DistributedCartesianDescriptor{PartitionedArrays.DebugArray{Int64, 1}, Tuple{Int64, Int64}, Gridap.Geometry.CartesianDescriptor{2, Float64, typeof(identity)}, Tuple{Bool, Bool}}}, args::Tuple{Gridap.ReferenceFEs.Lagrangian, Tuple{DataType, Int64}, @Kwargs{}}; kwargs::@Kwargs{dirichlet_tags::String}) @ GridapDistributed ~/.julia/packages/GridapDistributed/O0Pts/src/FESpaces.jl:575 [40] TestFESpace(::GridapDistributed.GenericDistributedDiscreteModel{2, 2, PartitionedArrays.DebugArray{Gridap.Geometry.CartesianDiscreteModel{2, Float64, typeof(identity)}, 1}, Vector{PartitionedArrays.PRange}, GridapDistributed.DistributedCartesianDescriptor{PartitionedArrays.DebugArray{Int64, 1}, Tuple{Int64, Int64}, Gridap.Geometry.CartesianDescriptor{2, Float64, typeof(identity)}, Tuple{Bool, Bool}}}, ::Vararg{Any}; kwargs::@Kwargs{dirichlet_tags::String}) @ Gridap.FESpaces ~/.julia/packages/Gridap/FDYkr/src/FESpaces/FESpaceFactories.jl:5 [41] (::Main.GridapPETScTests.GridapPETScSequentialTests.PoissonTests.var"#main##0#main##1"{Tuple{Int64, Int64}, Symbol, PartitionedArrays.DebugArray{Int64, 1}})() @ Main.GridapPETScTests.GridapPETScSequentialTests.PoissonTests ~/.julia/packages/GridapPETSc/PaJ7d/test/PoissonTests.jl:64 [42] with(f::Main.GridapPETScTests.GridapPETScSequentialTests.PoissonTests.var"#main##0#main##1"{Tuple{Int64, Int64}, Symbol, PartitionedArrays.DebugArray{Int64, 1}}; kwargs::@Kwargs{args::Vector{SubString{String}}}) @ GridapPETSc ~/.julia/packages/GridapPETSc/PaJ7d/src/Environment.jl:68 [inlined] [43] main(distribute::Core.TypeEgal{PartitionedArrays.DebugArray}, nparts::Tuple{Int64, Int64}, solver::Symbol) @ Main.GridapPETScTests.GridapPETScSequentialTests.PoissonTests ~/.julia/packages/GridapPETSc/PaJ7d/test/PoissonTests.jl:45 [44] main(distribute::Type, nparts::Tuple{Int64, Int64}) @ Main.GridapPETScTests.GridapPETScSequentialTests.PoissonTests ~/.julia/packages/GridapPETSc/PaJ7d/test/PoissonTests.jl:29 [45] (::Main.GridapPETScTests.GridapPETScSequentialTests.PoissonTests.var"#5#6")(distribute::Core.TypeEgal{PartitionedArrays.DebugArray}) @ Main.GridapPETScTests.GridapPETScSequentialTests.PoissonTests ~/.julia/packages/GridapPETSc/PaJ7d/test/sequential/PoissonTests.jl:5 [46] with_debug(f::Main.GridapPETScTests.GridapPETScSequentialTests.PoissonTests.var"#5#6") @ PartitionedArrays ~/.julia/packages/PartitionedArrays/MVmxR/src/debug_array.jl:8 [47] top-level scope @ ~/.julia/packages/GridapPETSc/PaJ7d/test/sequential/PoissonTests.jl:4 [48] include(mapexpr::Function, mod::Module, _path::String) @ Base Base.jl:335 [49] IncludeInto @ ./Base.jl:336 [inlined] [50] macro expansion @ ~/.julia/packages/GridapPETSc/PaJ7d/test/sequential/runtests.jl:12 [inlined] [51] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [52] macro expansion @ ~/.julia/packages/GridapPETSc/PaJ7d/test/sequential/runtests.jl:12 [inlined] [53] macro expansion @ ./timing.jl:741 [inlined] [54] top-level scope @ ~/.julia/packages/GridapPETSc/PaJ7d/test/sequential/runtests.jl:397 in expression starting at /home/pkgeval/.julia/packages/GridapPETSc/PaJ7d/test/sequential/PoissonTests.jl:1 52.652832 seconds (19.55 M allocations: 1.135 GiB, 0.55% gc time, 97.00% compilation time: 2% of which was recompilation) WARNING! There are options you set that were not used! WARNING! could be spelling mistake, etc! There are 5 unused database options. They are: Option left: name:-ksp_converged_reason (no value) source: command line Option left: name:-ksp_error_if_not_converged value: true source: command line Option left: name:-ksp_rtol value: 1.0e-12 source: command line Option left: name:-ksp_type value: cg source: command line Option left: name:-pc_type value: jacobi source: command line 0 SNES Function norm 2.409822369424e+06 Linear solve converged due to CONVERGED_RTOL iterations 258 1 SNES Function norm 8.077651079465e+00 Linear solve converged due to CONVERGED_RTOL iterations 225 2 SNES Function norm 4.661493250862e-01 Linear solve converged due to CONVERGED_RTOL iterations 215 3 SNES Function norm 8.397548297115e-03 Linear solve converged due to CONVERGED_RTOL iterations 246 4 SNES Function norm 4.669108164729e-06 Linear solve converged due to CONVERGED_RTOL iterations 313 5 SNES Function norm 4.635797857203e-11 Nonlinear solve converged due to CONVERGED_FNORM_RELATIVE iterations 5 0 SNES Function norm 4.635797857203e-11 Linear solve converged due to CONVERGED_RTOL iterations 287 1 SNES Function norm 5.122905122569e-13 Nonlinear solve converged due to CONVERGED_SNORM_RELATIVE iterations 1 0 SNES Function norm 2.409822369424e+06 Linear solve converged due to CONVERGED_RTOL iterations 258 1 SNES Function norm 8.077651079465e+00 Linear solve converged due to CONVERGED_RTOL iterations 225 2 SNES Function norm 4.661493250862e-01 Linear solve converged due to CONVERGED_RTOL iterations 215 3 SNES Function norm 8.397548297118e-03 Linear solve converged due to CONVERGED_RTOL iterations 246 4 SNES Function norm 4.669108164798e-06 Linear solve converged due to CONVERGED_RTOL iterations 313 5 SNES Function norm 4.635773814233e-11 Nonlinear solve converged due to CONVERGED_FNORM_RELATIVE iterations 5 0 SNES Function norm 4.635773814233e-11 Linear solve converged due to CONVERGED_RTOL iterations 287 1 SNES Function norm 5.073718370359e-13 Nonlinear solve converged due to CONVERGED_SNORM_RELATIVE iterations 1 210.797947 seconds (63.96 M allocations: 3.616 GiB, 1.76% gc time, 85.68% compilation time: <1% of which was recompilation) Test Summary: | Pass Error Total Time SEQUENTIAL | 44 1 45 6m03.1s PartitionedArrays (sequential) | 40 40 1m37.9s PoissonTests | 1 1 52.7s PLaplacianTests | 4 4 3m30.8s RNG of the outermost testset: Random.Xoshiro(0x0f49c4098c7c8878, 0xdadd61c2ab29a60f, 0xcc12849866bd5c25, 0x99181b30d60f68f3, 0xcbe73ea16a182603) ERROR: LoadError: Some tests did not pass: 44 passed, 0 failed, 1 errored, 0 broken. in expression starting at /home/pkgeval/.julia/packages/GridapPETSc/PaJ7d/test/runtests.jl:1 Testing failed after 762.7s ERROR: LoadError: Package GridapPETSc errored during testing Stacktrace: [1] pkgerror(msg::String) @ Pkg.Types /opt/julia/share/julia/stdlib/v1.14/Pkg/src/Types.jl:68 [2] test(ctx::Pkg.Types.Context, pkgs::Vector{PackageSpec}; coverage::Bool, julia_args::Cmd, test_args::Cmd, test_fn::Nothing, force_latest_compatible_version::Bool, allow_earlier_backwards_compatible_versions::Bool, allow_reresolve::Bool) @ Pkg.Operations /opt/julia/share/julia/stdlib/v1.14/Pkg/src/Operations.jl:3283 [3] test(ctx::Pkg.Types.Context, pkgs::Vector{PackageSpec}; coverage::Bool, test_fn::Nothing, julia_args::Cmd, test_args::Cmd, force_latest_compatible_version::Bool, allow_earlier_backwards_compatible_versions::Bool, allow_reresolve::Bool, kwargs::@Kwargs{io::IOContext{IO}}) @ Pkg.API /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:587 [4] test(pkgs::Vector{PackageSpec}; io::IOContext{IO}, kwargs::@Kwargs{julia_args::Cmd}) @ Pkg.API /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:172 [5] test(pkgs::Vector{String}; kwargs::@Kwargs{julia_args::Cmd}) @ Pkg.API /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:160 [6] test(pkg::String; kwargs::@Kwargs{julia_args::Cmd}) @ Pkg.API /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:159 [inlined] [7] top-level scope @ /PkgEval.jl/scripts/evaluate.jl:223 in expression starting at /PkgEval.jl/scripts/evaluate.jl:214 PkgEval failed after 1215.34s: package tests unexpectedly errored