Read ntt 4 1 2 with max.threads 22 Continue with num.threads = 4:1:2 max_threads = 4 Version.......[8489ea6] PRELOAD.......[/home/abdulfe/.cache/R/INLA/stiles-binary/latest/lib/libmimalloc.so] MAX_THREADS...[4] Report issues/bugs to cwd[/home/abdulfe/Documents/ideas/sabil/bugs-tests/fix-bugs/bug1] Process file/directory[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/Model.ini] model[0/1/] threads[4] max.threads[22] blas_threads_force[0] nested[4:1] Run with model[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/Model.ini] inla_build... number of sections=[13] parse section=[12] name=[INLA.numa] type=[NUMA] inla_parse_numa... section[INLA.numa] enable[0] parse section=[10] name=[INLA.stiles] type=[STILES] inla_parse_stiles... section[INLA.stiles] verbose[0] tile.size[-1] block.size[-1] tile.type[-1] (default) sTiles Global Parameters ────────────────────────────────────────────────────────────────── [ 0] CORRECTION_MODE = 0 (STILES_CORRECTION_NONE) [ 1] TILE_SIZE = -1 (STILES_TILE_AUTO) [ 2] ORDERING_MODE = 0 [ 3] TILE_TYPE = 3 (STILES_TILE_AUTO_SELECT) [ 4] TILE_ORDERING_MODE = 14569 [ 5] TILE_ORDERING_SIZE = -1 (auto: tile_size / 2) [ 6] FORCE_ND = 0 (STILES_ND_AUTO) [ 7] INVERSE_STORAGE = 1 (STILES_INV_SEPARATE) [ 8] USE_OMP = 0 (STILES_THREAD_PTHREADS) [ 9] SEMISPARSE_IMPL = 2 (STILES_SEMI_VECTORIZED) [10] GPU_COMPARE = 0 (STILES_GPU_ONLY) [11] GPU_ENABLE = 1 (STILES_GPU_ENABLED) [12] MEMORY_ESTIMATE = 0 (STILES_MEMEST_SKIP) [13] RESERVED_13 = 0 [14] SERIAL_MODE = 0 (STILES_SERIAL_AUTO) [15] BW_MODE = 0 (STILES_BW_CONSERVATIVE) [16] RESERVED_16 = 0 [17] RESERVED_17 = 0 [18] RESERVED_18 = 0 [19] RESERVED_19 = 0 [20] FORCE_SCOTCH_ORDERING = 0 [21] SCOTCH_PADDING = 0 [22] PATH2_DEPTH = 0 [23] RESERVED_23 = 0 [24] RESERVED_24 = 0 [25] TREE_PATH_ENABLE = 1 [26] TREE_PATH_FORCE = 0 [27] BIND_MODE = 2 (STILES_BIND_ALWAYS) [28] FACTOR_VARIANT = -1 (STILES_VARIANT_CALLER) ────────────────────────────────────────────────────────────────── parse section=[0] name=[INLA.libR] type=[LIBR] inla_parse_libR... section[INLA.libR] R_HOME=[/usr/lib/R] parse section=[7] name=[INLA.Expert] type=[EXPERT] inla_parse_expert... section[INLA.Expert] disable.gaussian.check=[0] Optimise linear solve = [No] Optimise storage = [No] Optimise num.threads = [Yes] Memory.alignment.enabled = [Yes] Memory.alignment = [32] bytes Memory.alignment.check = [FAIL] cpo.manual=[0] jp.file=[(null)] jp.model=[(null)] blas.num.threads=[0 (adaptive)] parse section=[1] name=[INLA.Model] type=[PROBLEM] inla_parse_problem... name=[INLA.Model] R-INLA version = [26.08.27.9000] R-INLA build date = [20697] Build tag = [8489ea6] System memory = [62 Gb] L3 cache = [24 Mb] Cores = (Physical= 22, Logical= 22) NUMA not available 'char' is signed 'short int' is 2 bytes 'int' is 4 bytes 'size_t' is 8 bytes 'long int' is 8 bytes 'long long' is 8 bytes 'float' is 4 bytes 'double' is 8 bytes 'long double' is 16 bytes BUFSIZ is 8192 bytes CACHE_LINE_SIZE is 64 bytes MEM_ALIGN is 32 bytes L1 Data Cache: 32768 bytes L1 Instr Cache: 65536 bytes L2 Cache: 2 Mbytes L3 Cache: 24 Mbytes GCC/Compiler version[16.0.1 20260315 (experimental) [trunk r16-8100-g3aca3bae8ee]] Compiler symbol defined [__AVX__] Compiler symbol defined [__AVX2__] Compiler symbol defined [__MMX_WITH_SSE__] Compiler symbol defined [__SSE__] Compiler symbol defined [__SSE2__] Compiler symbol defined [__SSE2_MATH__] Compiler symbol defined [__SSE3__] Compiler symbol defined [__SSE4_1__] Compiler symbol defined [__SSE4_2__] Compiler symbol defined [__SSE_MATH__] Compiler symbol defined [__SSSE3__] Compiled with -DINLA_WITH_STILES Compiled with -DINLA_WITH_LIBR Compiled with -DINLA_WITH_MUPARSER Compiled with -DINLA_WITH_MKL Compiled with -DINLA_WITH_DEVEL Compiled with -DINLA_WITH_NUMA Compiled with -DINLA_WITH_SIMDE CPU feature SSE availble? YES CPU feature SSE2 availble? YES CPU feature SSE3 availble? YES CPU feature SSE4.1 availble? YES CPU feature SSE4.2 availble? YES CPU feature AVX availble? YES CPU feature AVX2 availble? YES CPU feature AVX512F availble? NO openmp.strategy=[default] smtp = [stiles] strategy = [default] store results in directory=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/results.files-0000000000] output: gcpo=[0] type.cv=[single] num.level.sets=[-1] size.max=[32] strategy=[Posterior] correct.hyperpar=[1] epsilon=[0.005] prior.diagonal=[0.0001] keep=[] remove.fixed=[1] remove=[] cpo=[0] po=[0] dic=[0] kld=[1] mlik=[1] q=[0] graph=[0] hyperparameters=[1] config=[0] config.lite=[0] internal.opt=[1] save.memory=[0] summary=[1] return.marginals=[1] return.marginals.predictor=[0] nquantiles=[3] [ 0.025 0.5 0.975 ] ncdf=[0] [ ] parse section=[3] name=[Predictor] type=[PREDICTOR] inla_parse_predictor ... section=[Predictor] dir=[predictor] PRIOR->name=[loggamma] hyperid=[53001|Predictor] PRIOR->from_theta=[function (x) <>exp(x)] PRIOR->to_theta = [function (x) <>log(x)] PRIOR->PARAMETERS=[1, 1e-05] initialise log_precision[13.8155] fixed=[1] user.scale=[1] n=[1000] m=[0] ndata=[1000] compute=[1] read offsets from file=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa2038421be7] read n=[2000] entries from file=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa2038421be7] file=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa2038421be7] 0/1000 (idx,y) = (0, 0) file=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa2038421be7] 1/1000 (idx,y) = (1, 0) file=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa2038421be7] 2/1000 (idx,y) = (2, 0) file=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa2038421be7] 3/1000 (idx,y) = (3, 0) file=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa2038421be7] 4/1000 (idx,y) = (4, 0) file=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa2038421be7] 5/1000 (idx,y) = (5, 0) file=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa2038421be7] 6/1000 (idx,y) = (6, 0) file=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa2038421be7] 7/1000 (idx,y) = (7, 0) file=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa2038421be7] 8/1000 (idx,y) = (8, 0) file=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa2038421be7] 9/1000 (idx,y) = (9, 0) A=[(null)] Aext=[(null)] AextPrecision=[1e+08] output: summary=[1] return.marginals=[1] return.marginals.predictor=[0] nquantiles=[3] [ 0.025 0.5 0.975 ] ncdf=[0] [ ] parse section=[2] name=[INLA.Data1] type=[DATA] inla_parse_data [section 1]... tag=[INLA.Data1] family=[GAUSSIAN] likelihood=[GAUSSIAN] file->name=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa203a5e959b] file->name=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa2072e1542c] file->name=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa2075765543] file->name=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa2046b25ef4] read n=[3000] entries from file=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa203a5e959b] 0/1000 (idx,a,y,d) = (0, 1, -35.4908, 1) 1/1000 (idx,a,y,d) = (1, 1, -44.6603, 1) 2/1000 (idx,a,y,d) = (2, 1, -31.3817, 1) 3/1000 (idx,a,y,d) = (3, 1, -3.95578, 1) 4/1000 (idx,a,y,d) = (4, 1, -26.2354, 1) 5/1000 (idx,a,y,d) = (5, 1, -4.60561, 1) 6/1000 (idx,a,y,d) = (6, 1, -2.1225, 1) 7/1000 (idx,a,y,d) = (7, 1, -26.1665, 1) 8/1000 (idx,a,y,d) = (8, 1, -27.7682, 1) 9/1000 (idx,a,y,d) = (9, 1, -4.41029, 1) likelihood.variant=[0] initialise log_precision[10] fixed0=[1] PRIOR0->name=[loggamma] hyperid=[65001|INLA.Data1] PRIOR0->from_theta=[function (x) <>exp(x)] PRIOR0->to_theta = [function (x) <>log(x)] PRIOR0->PARAMETERS0=[1, 5e-05] initialise log_precision offset[72.0873] fixed1=[1] PRIOR1->name=[none] hyperid=[65002|INLA.Data1] PRIOR1->from_theta=[function (x) <>exp(x)] PRIOR1->to_theta = [function (x) <>log(x)] PRIOR1->PARAMETERS1=[] Link model [IDENTITY] Link order [-1] Link variant [-1] Link a [1] Link ntheta [0] mix.use[0] parse section=[5] name=[st] type=[FFIELD] inla_parse_ffield... section=[st] dir=[random.effect0000000001] model=[cgeneric] vb.correct n[1] -1 correct=[-1] constr=[0] diagonal=[0] id.names= compute=[1] nrep=[1] ngroup=[1] Alocal=[no] read covariates from file=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa201f2cb66c] read n=[2000] entries from file=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa201f2cb66c] file=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa201f2cb66c] 0/1000 (idx,y) = (0, -1) file=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa201f2cb66c] 1/1000 (idx,y) = (1, -1) file=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa201f2cb66c] 2/1000 (idx,y) = (2, -1) file=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa201f2cb66c] 3/1000 (idx,y) = (3, -1) file=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa201f2cb66c] 4/1000 (idx,y) = (4, -1) file=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa201f2cb66c] 5/1000 (idx,y) = (5, -1) file=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa201f2cb66c] 6/1000 (idx,y) = (6, -1) file=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa201f2cb66c] 7/1000 (idx,y) = (7, -1) file=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa201f2cb66c] 8/1000 (idx,y) = (8, -1) file=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa201f2cb66c] 9/1000 (idx,y) = (9, -1) file for locations=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa2016043c4a] nlocations=[43583] locations[0]=[1] locations[1]=[2] locations[2]=[3] locations[3]=[4] locations[4]=[5] locations[5]=[6] locations[6]=[7] locations[7]=[8] locations[8]=[9] locations[9]=[10] cyclic=[0] cgeneric.shlib [/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa20534d3fa0.so] cgeneric.model [inla_cgeneric_sstspde] cgeneric.data [/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa203b6fe855] cgeneric.n [43583] cgeneric.q [0] cgeneric.debug [0] Model [inla_cgeneric_sstspde] is built-in, ignore [/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa20534d3fa0.so] ntheta = [2] initial[0] = 1 initial[1] = 0 initialise theta[0]=[1] fixed[0]=[0] initialise theta[1]=[0] fixed[1]=[0] computed/guessed rank-deficiency = [0] output: summary=[1] return.marginals=[1] return.marginals.predictor=[0] nquantiles=[3] [ 0.025 0.5 0.975 ] ncdf=[0] [ ] section=[4] name=[(Intercept)] type=[LINEAR] inla_parse_linear... section[(Intercept)] dir=[fixed.effect0000000001] file for covariates=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa203f4ae615] read n=[2000] entries from file=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa203f4ae615] file=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa203f4ae615] 0/1000 (idx,y) = (0, 1) file=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa203f4ae615] 1/1000 (idx,y) = (1, 1) file=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa203f4ae615] 2/1000 (idx,y) = (2, 1) file=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa203f4ae615] 3/1000 (idx,y) = (3, 1) file=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa203f4ae615] 4/1000 (idx,y) = (4, 1) file=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa203f4ae615] 5/1000 (idx,y) = (5, 1) file=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa203f4ae615] 6/1000 (idx,y) = (6, 1) file=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa203f4ae615] 7/1000 (idx,y) = (7, 1) file=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa203f4ae615] 8/1000 (idx,y) = (8, 1) file=[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/data.files/fileeaa203f4ae615] 9/1000 (idx,y) = (9, 1) prior mean=[0] prior precision=[0] compute=[1] output: summary=[1] return.marginals=[1] return.marginals.predictor=[0] nquantiles=[3] [ 0.025 0.5 0.975 ] ncdf=[0] [ ] parse section=[9] name=[INLA.pardiso] type=[PARDISO] inla_parse_pardiso... section[INLA.pardiso] verbose[0] debug[0] parallel.reordering[1] nrhs[-1] parse section=[11] name=[INLA.taucs] type=[TAUCS] inla_parse_taucs... section[INLA.taucs] min.block.size[4] block.size[64] parse section=[8] name=[INLA.lp.scale] type=[LP.SCALE] inla_parse_lp_scale... section[INLA.lp.scale] lp_scale variables in use = [ ] Index table: number of entries[3], total length[44584] tag start-index length Predictor 0 1000 st 1000 43583 (Intercept) 44583 1 List of hyperparameters: theta[0] = [Theta1 for st] theta[1] = [Theta2 for st] parse section=[6] name=[INLA.Parameters] type=[INLA] inla_parse_INLA... section[INLA.Parameters] lincomb.derived.correlation.matrix = [No] global_node.factor = 2.000 global_node.degree = 2147483647 reordering = -1 constr.marginal.diagonal = 1.49e-08 Contents of ai_param 0x56c8c900000 Optimiser: DEFAULT METHOD Option for GSL-BFGS2: tol = 0.1 Option for GSL-BFGS2: step_size = 1 Option for GSL-BFGS2: epsx = 0.001 Option for GSL-BFGS2: epsf = 0.002 Option for GSL-BFGS2: epsg = 0.005 Restart: 0 Optimise: try to be smart: Yes Optimise: use directions: Yes Mode restart: Yes Mode fixed: No Mode use_mode: No parallel linesearch [0] Gaussian approximation: tolerance_func = 0.002 tolerance_step = 2.5e-06 optpar_fp = 0 optpar_nr_step_factor = -0.1 Gaussian data: Yes Strategy: Use an adaptive strategy (max=25) Fast mode: On Use linear approximation to log(|Q +c|)? Yes Method: Compute the derivative exact Parameters for improved approximations Number of points evaluate: 9 Step length to compute derivatives numerically: 0.0001 Stencil to compute derivatives numerically: 5 Cutoff value to construct local neigborhood: 0.0001 Log calculations: On Log calculated marginal for the hyperparameters: On Integration strategy: Automatic (GRID for dim(theta)=1 and 2 and otherwise CCD) f0 (CCD only): 1.100 dz (GRID only): 0.750 Adjust weights (GRID only): On Difference in log-density limit (GRID only): 6.000 Skip configurations with (presumed) small density (GRID only): On Gradient is computed using Central difference with step-length 0.005000 Hessian is computed using Central difference with step-length 0.070711 Hessian matrix is forced to be a diagonal matrix? [No] Compute effective number of parameters? [Yes] Perform a Monte Carlo error-test? [No] Interpolator [Auto] CPO required diff in log-density [3] Stupid search mode: Status [On] Max iter [1000] Factor [1.05] Numerical integration of hyperparameters: Maximum number of function evaluations [100000] Relative error ....................... [1e-05] Absolute error ....................... [1e-06] To stabilise the numerical optimisation: Minimum value of the -Hessian [-inf] Strategy for the linear term [Keep] CPO manual calculation[No] VB correction is [Enabled] strategy = [mean] verbose = [Yes] f_enable_limit_mean = [30] f_enable_limit_var = [25] f_enable_limit_mean_max = [1024] f_enable_limit_variance_max = [768] iter_max = [25] emergency = [25.00] hessian_update = [2] hessian_strategy = [full] Misc options: Hessian correct skewness only [1] inla_build: check for unused entries in[/home/abdulfe/Documents/ideas/sabil/bugs-tests/models/inla.model-220-stilesx1/inla.model/Model.ini] inla_INLA_preopt_experimental... Mode....................... [Compact] Setup...................... [1.79s] Sparse-matrix library...... [sTiles] sTiles.version ............ [3.0.0 (built 2026-08-20, 2026-08-20 17:44:30 +0300 [bc1679d]) OpenMP strategy............ [sTiles] num.threads................ [4:1] num.threads (adaptive)..... [2] blas.num.threads........... [adaptive] Density-strategy........... [High] Size of graph.............. [43584] Number of constraints...... [0] Optimise ddot.............. [96] Optimise dscale............ [1024] Optimise daxpy............. [192] Optimise sort2_id.......... [64] Optimise sort2_dd.......... [376] Optimise Qx-strategy....... serial[0.886] parallel [1.000] choose[serial] Optimise pred-strategy..... plain [0.041] data-rich[1.000] choose[plain] Optimise dot-products...... plain[1.000] group[0.447] max[0.002185 ms] ->mix[0.447] (plain[0.0%] group[100.0%]) List of hyperparameters: theta[0] = [Theta1 for st] theta[1] = [Theta2 for st] Compute initial values... Iter[0] RMS(err) = 1.000, update with step-size = 1.001 Iter[1] RMS(err) = 0.856, update with step-size = 0.000 Iter[2] RMS(err) = 1.000, update with step-size = 0.000 Initial values computed in 0.0027 seconds x[0] = 0.0000 x[1] = 0.0000 x[2] = 0.0000 x[3] = 0.0000 x[4] = 0.0000 x[43579] = 0.0000 x[43580] = 0.0000 x[43581] = 0.0000 x[43582] = 0.0000 x[43583] = -11.2450  ╭────────────────────────────────────────────────────────────╮ │ │ │ ******* * │ │ * * │ │ **** * * * ***** **** │ │ * * * * * * │ │ **** * * * ***** **** │ │ * * * * * * │ │ **** * * * **** **** │ │ │ │ High Performance Framework for Tiling Sparse Matrices │ │ │ ╰────────────────────────────────────────────────────────────╯ Version: 3.0.0 | Copyright (c) 2026 KAUST [TIME] 2026-09-01 18:53:26.828 │ ↪ Dense node preprocessing: threshold=490 (5×mean=98) → 0 nodes [TIME] 2026-09-01 18:53:26.832 │ ↪ countActiveTiles (baseline): active=27607, time=0.004 s [TIME] 2026-09-01 18:53:26.832 │ ↪ Ordering candidates (n=43584): SCOTCH, ASCOTCH, FSCOTCH, AMD, METIS, RCM, CAMD [TIME] 2026-09-01 18:53:29.200 │ ↪ Ordering comparison (baseline fill-proxy=27607): [TIME] 2026-09-01 18:53:29.200 │ ND ord= 0.567s total= 0.578s fill-proxy=158928 [TIME] 2026-09-01 18:53:29.200 │ AND ord= 1.119s total= 1.130s fill-proxy=174324 [TIME] 2026-09-01 18:53:29.201 │ FND ord= 0.582s total= 0.592s fill-proxy=154673 [TIME] 2026-09-01 18:53:29.201 │ AMD ord= 0.153s total= 0.165s fill-proxy=333820 [TIME] 2026-09-01 18:53:29.201 │ METIS ord= 2.356s total= 2.367s fill-proxy=194134 [TIME] 2026-09-01 18:53:29.201 │ RCM ord= 0.071s total= 0.089s fill-proxy=80343 <- winner [TIME] 2026-09-01 18:53:29.201 │ CAMD ord= 0.152s total= 0.163s fill-proxy=211453 [TIME] 2026-09-01 18:53:29.201 │ ↪ Symbolic_phase ordering : 2.375000 s [TIME] 2026-09-01 18:53:30.797 │ ↪ SCOTCH separator tree captured: cblknbr=1593 [TIME] 2026-09-01 18:53:30.797 │ ↪ Ordering selected: FND (tiles=71423, nnz(L)=73788503, sep=1593) [TIME] 2026-09-01 18:53:30.826 │ ↪ Symbolic phase (auto-mode resolution) : 3.999680 s [TIME] 2026-09-01 18:53:30.929 │ ↪ Mode auto-select: occ=0.650 fill=34.25 skew=3.1 -> sparse (mode 2) [TIME] 2026-09-01 18:53:30.929 │ ↪ Auto mode: fill=34.2457× → using non-uniform tiles (mode 2) [TIME] 2026-09-01 18:53:30.946 │ ↪ Build sparse pattern (CSC) : 0.016481 s [TIME] 2026-09-01 18:53:31.669 │ ↪ Compute supernodal symbolic : 0.723291 s [TIME] 2026-09-01 18:53:31.671 │ ↪ Allocate L cell store : 0.001780 s [TIME] 2026-09-01 18:53:31.679 │ ↪ Collect numeric tasks : 0.007625 s [TIME] 2026-09-01 18:53:31.698 │ ↪ Dense node preprocessing: threshold=490 (5×mean=98) → 0 nodes [TIME] 2026-09-01 18:53:31.701 │ ↪ countActiveTiles (baseline): active=27607, time=0.003 s [TIME] 2026-09-01 18:53:31.701 │ ↪ Ordering candidates (n=43583): SCOTCH, ASCOTCH, FSCOTCH, AMD, METIS, RCM, CAMD [TIME] 2026-09-01 18:53:34.063 │ ↪ Ordering comparison (baseline fill-proxy=27607): [TIME] 2026-09-01 18:53:34.063 │ ND ord= 0.510s total= 0.523s fill-proxy=151873 [TIME] 2026-09-01 18:53:34.063 │ AND ord= 1.079s total= 1.090s fill-proxy=163823 [TIME] 2026-09-01 18:53:34.063 │ FND ord= 0.511s total= 0.528s fill-proxy=173329 [TIME] 2026-09-01 18:53:34.063 │ AMD ord= 0.139s total= 0.151s fill-proxy=333820 [TIME] 2026-09-01 18:53:34.063 │ METIS ord= 2.351s total= 2.362s fill-proxy=208958 [TIME] 2026-09-01 18:53:34.063 │ RCM ord= 0.062s total= 0.076s fill-proxy=80437 <- winner [TIME] 2026-09-01 18:53:34.064 │ CAMD ord= 0.128s total= 0.139s fill-proxy=211453 [TIME] 2026-09-01 18:53:34.064 │ ↪ Symbolic_phase ordering : 2.368000 s [TIME] 2026-09-01 18:53:35.677 │ ↪ Ordering selected: RCM (tiles=57438, nnz(L)=88425011, sep=0) [TIME] 2026-09-01 18:53:35.707 │ ↪ Symbolic phase (auto-mode resolution) : 4.011110 s [TIME] 2026-09-01 18:53:35.829 │ ↪ Mode auto-select: occ=0.971 fill=41.04 skew=1.2 -> dense (mode 0) [TIME] 2026-09-01 18:53:35.836 │ ↪ Auto mode: fill=41.0386× → using semisparse tiles (mode 1) [TIME] 2026-09-01 18:53:35.840 │ ↪ Construct mapper : 0.003440 s [TIME] 2026-09-01 18:53:35.840 │ ↪ Bind active predicates : 8.780e-07 s [TIME] 2026-09-01 18:53:35.840 │ ↪ Corner-probe skipped: num_cores=1 < 2 (not enough parallelism) [TIME] 2026-09-01 18:53:35.840 │ ↪ Corner probe : 0.000010 s [TIME] 2026-09-01 18:53:35.840 │ ↪ Count tree leaves : 2.950e-07 s [TIME] 2026-09-01 18:53:35.840 │ ↪ Build elimination tree : 2.998e-08 s [TIME] 2026-09-01 18:53:35.856 │ ↪ Dense tile lookup : 0.016172 s [TIME] 2026-09-01 18:53:35.856 │ ↪ Semisparse tile lookup : 3.900e-07 s [TIME] 2026-09-01 18:53:36.356 │ ↪ Collect numeric tasks : 0.499586 s [TIME] 2026-09-01 18:53:36.356 │ ↪ Allocate semisparse tiles : 2.899e-08 s [TIME] 2026-09-01 18:53:36.356 │ ↪ Build scatter/gather info : 6.011e-07 s [TIME] 2026-09-01 18:53:36.444 │ ↪ Allocate dense tiles (sparse) : 0.088042 s [TIME] 2026-09-01 18:53:37.924 │ ↪ Symbolic factorization (user permutation): tiles(L)=57438, time=1.043 s [TIME] 2026-09-01 18:53:37.924 │ ↪ Symbolic phase (auto-mode resolution) : 1.043520 s [TIME] 2026-09-01 18:53:38.048 │ ↪ Mode auto-select: occ=0.971 fill=41.04 skew=1.2 -> dense (mode 0) [TIME] 2026-09-01 18:53:38.054 │ ↪ Auto mode: fill=41.0386× → using semisparse tiles (mode 1) [TIME] 2026-09-01 18:53:38.060 │ ↪ Construct mapper : 0.005465 s [TIME] 2026-09-01 18:53:38.060 │ ↪ Bind active predicates : 0.000001 s [TIME] 2026-09-01 18:53:38.063 │ ↪ Corner-probe: N=1090 mode=0 probe_sep=32 slots=528 nonzero=381 peak=90 mean=9.9 heavy=326/2 heavy_sum=5069 chosen_sep=32 agg_thr=32 -> off (chosen_sep > backend cap) [TIME] 2026-09-01 18:53:38.063 │ ↪ Corner probe : 0.003415 s [TIME] 2026-09-01 18:53:38.063 │ ↪ Count tree leaves : 3.030e-07 s [TIME] 2026-09-01 18:53:38.063 │ ↪ Build elimination tree : 6.903e-08 s [TIME] 2026-09-01 18:53:38.080 │ ↪ Dense tile lookup : 0.016382 s [TIME] 2026-09-01 18:53:38.080 │ ↪ Semisparse tile lookup : 2.310e-07 s [TIME] 2026-09-01 18:53:38.564 │ ↪ Collect numeric tasks : 0.484160 s [TIME] 2026-09-01 18:53:38.564 │ ↪ Allocate semisparse tiles : 2.899e-08 s [TIME] 2026-09-01 18:53:38.564 │ ↪ Build scatter/gather info : 6.050e-07 s [TIME] 2026-09-01 18:53:38.680 │ ↪ Allocate dense tiles (sparse) : 0.115608 s content of 'store' (computed in 11.908s): ngroup[5] verbose[0] ng[2] ng2[4] ngt[5] nt_outer[4] nt_inner[1] nt_special[2] block.size[40] group[0]: n[43584] nnz[4222188] n_within_group[4] n_cores_group[4] rescaled_group[no] nrhs = [ 1 31 40 ] perm[0][0] = 4809 iperm[0][0] = 3283 perm[0][1] = 888 iperm[0][1] = 3315 perm[0][2] = 1082 iperm[0][2] = 3348 perm[0][3] = 3140 iperm[0][3] = 3732 perm[0][4] = 2316 iperm[0][4] = 3733 perm[0][5] = 6549 iperm[0][5] = 3766 perm[0][6] = 7468 iperm[0][6] = 4381 perm[0][7] = 5049 iperm[0][7] = 4414 Qinv_done: 0:0 1:0 2:0 3:0 bind_done: 0:0 1:0 2:0 3:0 chol_done: 0:0 1:0 2:0 3:0 group[1]: n[43583] nnz[4222188] n_within_group[4] n_cores_group[4] rescaled_group[no] nrhs = [ 1 31 40 ] perm[1][0] = 124 iperm[1][0] = 7 perm[1][1] = 4026 iperm[1][1] = 491 perm[1][2] = 4346 iperm[1][2] = 75 perm[1][3] = 12159 iperm[1][3] = 42 perm[1][4] = 10033 iperm[1][4] = 492 perm[1][5] = 2473 iperm[1][5] = 43 perm[1][6] = 2370 iperm[1][6] = 493 perm[1][7] = 0 iperm[1][7] = 458 Qinv_done: 0:0 1:0 2:0 3:0 bind_done: 0:0 1:0 2:0 3:0 chol_done: 0:0 1:0 2:0 3:0 group[2]: n[43584] nnz[4222188] n_within_group[1] n_cores_group[2] rescaled_group[no] nrhs = [ 1 31 40 ] perm[2][0] = 4809 iperm[2][0] = 3283 perm[2][1] = 888 iperm[2][1] = 3315 perm[2][2] = 1082 iperm[2][2] = 3348 perm[2][3] = 3140 iperm[2][3] = 3732 perm[2][4] = 2316 iperm[2][4] = 3733 perm[2][5] = 6549 iperm[2][5] = 3766 perm[2][6] = 7468 iperm[2][6] = 4381 perm[2][7] = 5049 iperm[2][7] = 4414 Qinv_done: 0:0 bind_done: 0:0 chol_done: 0:0 group[3]: n[43583] nnz[4222188] n_within_group[1] n_cores_group[2] rescaled_group[no] nrhs = [ 1 31 40 ] perm[3][0] = 124 iperm[3][0] = 7 perm[3][1] = 4026 iperm[3][1] = 491 perm[3][2] = 4346 iperm[3][2] = 75 perm[3][3] = 12159 iperm[3][3] = 42 perm[3][4] = 10033 iperm[3][4] = 492 perm[3][5] = 2473 iperm[3][5] = 43 perm[3][6] = 2370 iperm[3][6] = 493 perm[3][7] = 0 iperm[3][7] = 458 Qinv_done: 0:0 bind_done: 0:0 chol_done: 0:0 group[4]: n[43584] nnz[4222188] n_within_group[4] n_cores_group[4] rescaled_group[yes] nrhs = [ 1 31 40 ] perm[4][0] = 4809 iperm[4][0] = 3283 perm[4][1] = 888 iperm[4][1] = 3315 perm[4][2] = 1082 iperm[4][2] = 3348 perm[4][3] = 3140 iperm[4][3] = 3732 perm[4][4] = 2316 iperm[4][4] = 3733 perm[4][5] = 6549 iperm[4][5] = 3766 perm[4][6] = 7468 iperm[4][6] = 4381 perm[4][7] = 5049 iperm[4][7] = 4414 Qinv_done: 0:0 1:0 2:0 3:0 bind_done: 0:0 1:0 2:0 3:0 chol_done: 0:0 1:0 2:0 3:0 Optimise using DEFAULT METHOD Smart optimise part I: estimate gradient using forward differences maxld= -3823553517.8594 fn= 1 theta= 1.0000 0.0000 [11.25, 13.848] New directions for numerical gradient dir01 dir02 1.000 . . 1.000 Iter=1 |grad|=0.606 |dx|=0(pass) |best.dx|=0(pass) |df|=850 |best.df|=0(pass) New directions for numerical gradient dir01 dir02 0.562 0.827 0.827 -0.562 Iter=2 |grad|=0.111 |dx|=0.398 |best.dx|=0(pass) |df|=0.162 |best.df|=0(pass) Smart optimise part II: estimate gradient using central differences Smart optimise part II: restart optimiser New directions for numerical gradient dir01 dir02 0.855 0.518 -0.518 0.855 Iter=1 |grad| = 0.00115(pass) |dx|=0.0847 |best.dx|=0(pass) |df|=0.00696 |best.df|=0(pass) New directions for numerical gradient dir01 dir02 0.715 -0.699 0.699 0.715 Iter=2 |grad| = 0.000257(pass) |dx|=0.409 |best.dx|=0(pass) |df|=1.19e-05(pass) |best.df|=0(pass) Optim: Number of function evaluations = 42 Compute the Hessian using central differences and step_size[0.0707107]. Matrix-type [dense] Smart optimise part IV: estimate Hessian using central differences Enable early_stop ff < f0: 3823554367.540713 < 3823554367.546479 (diff 0.00576591) Early stop. Mode not found sufficiently accurate f0=[3823554367.546479] f_best=[3823553517.859385] local.value=[3823554367.540713] Reset 'best' as we are unable to reproduce it: [3823553517.859385] --> [3823554367.540713] Mode not sufficient accurate; switch to a stupid local search strategy. Enable early_stop ff < f0: 3823559687.718598 < 3823559687.719082 (diff 0.000483513) Early stop. Mode not found sufficiently accurate f0=[3823559687.719082] f_best=[3823554367.540713] local.value=[3823559687.718598] Reset 'best' as we are unable to reproduce it: [3823554367.540713] --> [3823559687.718598] maxld= -3823554701.5530 fn= 53 theta= 1.1013 -0.1037 [11.25, 13.121] maxld= -3823554701.5161 fn= 56 theta= 1.0482 -0.1556 [11.25, 13.194] Enable early_stop ff < f0: 3823554701.516083 < 3823554701.552990 (diff 0.0369067) maxld= -3823549287.5092 fn= 57 theta= 1.1544 -0.0518 [11.25, 13.170] Early stop. Mode not found sufficiently accurate f0=[3823554701.552990] f_best=[3823549287.509211] local.value=[3823554701.516083] Reset 'best' as we are unable to reproduce it: [3823549287.509211] --> [3823554701.516083] maxld= -3823549287.4301 fn= 58 theta= 1.0482 -0.1556 [11.25, 13.188] maxld= -3823549287.3984 fn= 60 theta= 0.9951 -0.2075 [11.25, 13.230] Enable early_stop ff < f0: 3823549287.398399 < 3823549287.430076 (diff 0.0316768) Early stop. Mode not found sufficiently accurate f0=[3823549287.430076] f_best=[3823549287.398399] local.value=[3823549287.398399] Enable early_stop ff < f0: 3823554701.457881 < 3823554701.484406 (diff 0.0265245) Early stop. Mode not found sufficiently accurate f0=[3823554701.484406] f_best=[3823549287.398399] local.value=[3823554701.457881] Reset 'best' as we are unable to reproduce it: [3823549287.398399] --> [3823554701.457881] maxld= -3823549287.3719 fn= 68 theta= 0.9420 -0.2593 [11.25, 13.292] maxld= -3823549287.3504 fn= 70 theta= 0.8888 -0.3112 [11.25, 13.327] Enable early_stop ff < f0: 3823549287.350438 < 3823549287.371875 (diff 0.0214367) Early stop. Mode not found sufficiently accurate f0=[3823549287.371875] f_best=[3823549287.350438] local.value=[3823549287.350438] Enable early_stop ff < f0: 3823554701.430656 < 3823554701.436445 (diff 0.0057888) Early stop. Mode not found sufficiently accurate f0=[3823554701.436445] f_best=[3823549287.350438] local.value=[3823554701.430656] Reset 'best' as we are unable to reproduce it: [3823549287.350438] --> [3823554701.430656] maxld= -3823549287.3446 fn= 78 theta= 0.8370 -0.2581 [11.25, 13.370] maxld= -3823549287.3274 fn= 80 theta= 0.7839 -0.3100 [11.25, 13.397] Enable early_stop ff < f0: 3823549287.344221 < 3823549287.344650 (diff 0.000429153) Early stop. Mode not found sufficiently accurate f0=[3823549287.344650] f_best=[3823549287.327373] local.value=[3823549287.344221] Reset 'best' as we are unable to reproduce it: [3823549287.327373] --> [3823549287.344221] Enable early_stop ff < f0: 3823554701.412021 < 3823554701.430227 (diff 0.0182066) Early stop. Mode not found sufficiently accurate f0=[3823554701.430227] f_best=[3823549287.344221] local.value=[3823554701.412021] Reset 'best' as we are unable to reproduce it: [3823549287.344221] --> [3823554701.412021] maxld= -3823549287.3260 fn= 88 theta= 0.7320 -0.2569 [11.25, 13.430] maxld= -3823549287.3134 fn= 91 theta= 0.6789 -0.3088 [11.25, 13.465] Enable early_stop ff < f0: 3823549287.313365 < 3823549287.326014 (diff 0.0126491) Early stop. Mode not found sufficiently accurate f0=[3823549287.326014] f_best=[3823549287.313365] local.value=[3823549287.313365] Enable early_stop ff < f0: 3823554701.392257 < 3823554701.399372 (diff 0.00711441) Early stop. Mode not found sufficiently accurate f0=[3823554701.399372] f_best=[3823549287.313365] local.value=[3823554701.392257] Reset 'best' as we are unable to reproduce it: [3823549287.313365] --> [3823554701.392257] maxld= -3823549287.3063 fn= 98 theta= 0.6258 -0.3606 [11.25, 13.481] maxld= -3823549287.3047 fn=100 theta= 0.5726 -0.4125 [11.25, 13.500] Enable early_stop ff < f0: 3823549287.304660 < 3823549287.306251 (diff 0.00159073) Early stop. Mode not found sufficiently accurate f0=[3823549287.306251] f_best=[3823549287.304660] local.value=[3823549287.304660] GMRFLib_opt_estimate_hessian: ensure spd for Hessian: mode seems fine GMRFLib_opt_estimate_hessian: set tol=[0.9781]. number of negative eigenvalues=[0] 1.014 0.000 0.000 0.992 Eigenvectors of the Hessian 1.000 -0.002 0.002 1.000 Eigenvalues of the Hessian 1.014 0.992 StDev/Correlation matrix (scaled inverse Hessian) 0.993 -0.000 1.004 Compute corrected stdev for theta[0]: negative 0.874 positive 1.144 Compute corrected stdev for theta[1]: negative 1.106 positive 0.904 config 0/45=[ -0.680 -3.871 ] ldens= 5410.873, [1] accept, compute, 28.93s config 1/45=[ 2.224 -2.151 ] ldens= 5411.497, [3] accept, compute, 29.01s config 2/45=[ -3.060 -2.151 ] ldens= 5395.760, [0] accept, compute, 29.01s config 3/45=[ -0.000 0.703 ] ldens= 5413.775, [2] accept, compute, 29.29s config 4/45=[ 2.224 -0.860 ] ldens= -1.595, [3] accept, compute, 28.81s config 5/45=[ -3.060 -0.860 ] ldens= -17.382, [0] accept, compute, 28.83s (interupt) config 6/45=[ -0.680 -2.151 ] ldens= -1.582, [1] accept, compute, 29.23s config 7/45=[ -0.000 1.758 ] ldens= -3.037, [2] accept, compute, 28.98s config 8/45=[ -3.060 0.000 ] ldens= 5396.965, [0] accept, compute, 28.84s config 9/45=[ 2.224 -0.000 ] ldens= 5412.780, [3] accept, compute, 28.87s config 10/45=[ 0.000 3.164 ] ldens= 5394.471, [2] accept, compute, 29.09s config 11/45=[ -0.680 -0.860 ] ldens= 5413.494, [1] accept, compute, 29.24s config 12/45=[ -3.060 0.703 ] ldens= -17.454, [0] accept, compute, 28.98s (interupt) config 13/45=[ 2.224 0.703 ] ldens= -1.621, [3] accept, compute, 29.03s config 14/45=[ 0.890 -3.871 ] ldens= -3.208, [2] accept, compute, 29.21s config 15/45=[ -0.680 -0.000 ] ldens= -0.303, [1] accept, compute, 29.38s config 16/45=[ -3.060 1.758 ] ldens= 5393.887, [0] accept, compute, 28.99s config 17/45=[ 2.224 1.758 ] ldens= 5409.726, [3] accept, compute, 28.97s config 18/45=[ 0.890 -2.151 ] ldens= 5412.514, [2] accept, compute, 29.15s config 19/45=[ -0.680 0.703 ] ldens= 5413.472, [1] accept, compute, 29.26s config 20/45=[ -1.700 -3.871 ] ldens= -5.669, [0] accept, compute, 29.78s config 21/45=[ 2.224 3.164 ] ldens= -21.012, [3] accept, compute, 29.84s (interupt) config 22/45=[ 0.890 -0.860 ] ldens= -0.579, [2] accept, compute, 29.64s config 23/45=[ -0.680 1.758 ] ldens= -3.337, [1] accept, compute, 29.73s config 24/45=[ -1.700 -2.151 ] ldens= 5410.036, [0] accept, compute, 29.12s config 25/45=[ 4.003 -2.151 ] ldens= 5410.139, [3] accept, compute, 29.10s config 26/45=[ 0.890 -0.000 ] ldens= 5413.797, [2] accept, compute, 29.20s config 27/45=[ -0.680 3.164 ] ldens= 5394.192, [1] accept, compute, 29.27s config 28/45=[ 4.003 -0.860 ] ldens= -4.319, [3] accept, compute, 28.84s config 29/45=[ -1.700 -0.860 ] ldens= -3.068, [0] accept, compute, 28.95s config 30/45=[ 0.890 0.703 ] ldens= -0.601, [2] accept, compute, 29.04s config 31/45=[ -0.000 -3.871 ] ldens= -2.916, [1] accept, compute, 29.13s config 32/45=[ 4.003 0.000 ] ldens= 5411.098, [3] accept, compute, 28.83s config 33/45=[ -1.700 0.000 ] ldens= 5411.303, [0] accept, compute, 28.83s config 34/45=[ 0.890 1.758 ] ldens= 5410.754, [2] accept, compute, 29.00s config 35/45=[ -0.000 -2.151 ] ldens= 5412.804, [1] accept, compute, 29.11s config 36/45=[ -1.700 0.703 ] ldens= -3.097, [0] accept, compute, 28.77s config 37/45=[ 0.890 3.164 ] ldens= -19.939, [2] accept, compute, 28.92s (interupt) config 38/45=[ -0.000 -0.860 ] ldens= -0.290, [1] accept, compute, 29.10s config 39/45=[ -1.700 1.758 ] ldens= 5408.266, [0] accept, compute, 28.72s config 40/45=[ 2.224 -3.871 ] ldens= 5409.861, [2] accept, compute, 28.90s config 41/45=[ 0.000 0.000 ] ldens= 5414.086, [1] accept, compute, 29.13s config 42/45=[ -1.700 3.164 ] ldens= -22.352, [0] accept, compute, 25.22s (interupt)