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Use a symmetric coloring for the computation of sparse Hessians #271

Merged
merged 6 commits into from
Jul 18, 2024
Merged

Use a symmetric coloring for the computation of sparse Hessians #271

merged 6 commits into from
Jul 18, 2024

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@amontoison amontoison commented Jul 12, 2024 •
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close #258
Related to #246

@amontoison amontoison force-pushed the symmetric_coloring branch from e356cc7 to 55de379 Compare July 12, 2024 18:50
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github-actions bot commented Jul 12, 2024 •
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Package name latest stable
CaNNOLeS.jl
DCISolver.jl
DerivativeFreeSolvers.jl
JSOSolvers.jl
NLPModelsIpopt.jl
OptimalControl.jl
OptimizationProblems.jl
Percival.jl
QuadraticModels.jl
SolverBenchmark.jl
SolverTools.jl

@amontoison amontoison force-pushed the symmetric_coloring branch 3 times, most recently from 5004c7e to 63e4833 Compare July 12, 2024 19:25
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Benchmark result

Judge result

Benchmark Report for /home/runner/work/ADNLPModels.jl/ADNLPModels.jl

Job Properties

  • Time of benchmarks:
    • Target: 12 Jul 2024 - 21:45
    • Baseline: 12 Jul 2024 - 22:48
  • Package commits:
    • Target: 2ed491
    • Baseline: 093eb8
  • Julia commits:
    • Target: 48d4fd
    • Baseline: 48d4fd
  • Julia command flags:
    • Target: None
    • Baseline: None
  • Environment variables:
    • Target: None
    • Baseline: None

Results

A ratio greater than 1.0 denotes a possible regression (marked with ❌), while a ratio less
than 1.0 denotes a possible improvement (marked with ✅). Only significant results - results
that indicate possible regressions or improvements - are shown below (thus, an empty table means that all
benchmark results remained invariant between builds).

ID time ratio memory ratio
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "channel"] 0.75 (5%) ✅ 0.76 (1%) ✅
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "hovercraft1d"] 0.77 (5%) ✅ 1.00 (1%)
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "marine"] 1.07 (5%) ❌ 1.03 (1%) ❌
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "polygon1"] 0.55 (5%) ✅ 0.59 (1%) ✅
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "polygon3"] 0.75 (5%) ✅ 0.76 (1%) ✅
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "robotarm"] 0.01 (5%) ✅ 0.01 (1%) ✅
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "structural"] 0.00 (5%) ✅ 0.03 (1%) ✅
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "arglina"] 0.00 (5%) ✅ 0.01 (1%) ✅
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "arglinb"] 0.00 (5%) ✅ 0.01 (1%) ✅
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "brybnd"] 1.29 (5%) ❌ 1.00 (1%)
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "dixon3dq"] 0.00 (5%) ✅ 0.01 (1%) ✅
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "hovercraft1d"] 0.00 (5%) ✅ 0.02 (1%) ✅
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "integreq"] 0.88 (5%) ✅ 1.00 (1%)
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "spmsrtls"] 0.83 (5%) ✅ 0.84 (1%) ✅
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "camshape"] 1.11 (5%) ❌ 1.00 (1%)
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "catenary"] 1.14 (5%) ❌ 1.01 (1%) ❌
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "channel"] 1.07 (5%) ❌ 1.01 (1%)
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "controlinvestment"] 1.06 (5%) ❌ 1.00 (1%)
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "elec"] 2.44 (5%) ❌ 1.01 (1%)
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "marine"] 1.21 (5%) ❌ 1.02 (1%) ❌
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "polygon1"] 1.15 (5%) ❌ 1.01 (1%)
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "polygon3"] 1.05 (5%) ❌ 1.00 (1%)
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "hovercraft1d"] 0.29 (5%) ✅ 1.00 (1%)
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "polygon1"] 1.06 (5%) ❌ 1.01 (1%) ❌
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "polygon3"] 1.14 (5%) ❌ 1.00 (1%)
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "structural"] 0.94 (5%) ✅ 1.00 (1%)

Benchmark Group List

Here's a list of all the benchmark groups executed by this job:

  • ["hess_coord", "optimized", "Float64", "scalable_cons", "sparse"]
  • ["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse"]
  • ["hessian_backend", "optimized", "Float64", "scalable", "sparse"]
  • ["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics"]

Julia versioninfo

Target

Julia Version 1.10.4
Commit 48d4fd48430 (2024-06-04 10:41 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
      Ubuntu 22.04.4 LTS
  uname: Linux 6.5.0-1023-azure #24~22.04.1-Ubuntu SMP Wed Jun 12 19:55:26 UTC 2024 x86_64 x86_64
  CPU: AMD EPYC 7763 64-Core Processor: 
              speed         user         nice          sys         idle          irq
       #1  2445 MHz      22770 s          0 s        923 s      60873 s          0 s
       #2  2595 MHz      25387 s          0 s        998 s      58205 s          0 s
       #3  2445 MHz      24787 s          0 s       1007 s      58785 s          0 s
       #4  3244 MHz      21639 s          2 s       1070 s      61879 s          0 s
  Memory: 15.606491088867188 GB (12096.6484375 MB free)
  Uptime: 8473.98 sec
  Load Avg:  1.06  1.04  1.0
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)
Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)

Baseline

Julia Version 1.10.4
Commit 48d4fd48430 (2024-06-04 10:41 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
      Ubuntu 22.04.4 LTS
  uname: Linux 6.5.0-1023-azure #24~22.04.1-Ubuntu SMP Wed Jun 12 19:55:26 UTC 2024 x86_64 x86_64
  CPU: AMD EPYC 7763 64-Core Processor: 
              speed         user         nice          sys         idle          irq
       #1  2445 MHz      32056 s          0 s       1293 s      89131 s          0 s
       #2  2445 MHz      35724 s          0 s       1410 s      85376 s          0 s
       #3  3243 MHz      34656 s          0 s       1382 s      86462 s          0 s
       #4  3216 MHz      30145 s          2 s       1532 s      90832 s          0 s
  Memory: 15.606491088867188 GB (12811.75390625 MB free)
  Uptime: 12272.15 sec
  Load Avg:  1.02  1.03  1.0
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)
Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)

Target result

Benchmark Report for /home/runner/work/ADNLPModels.jl/ADNLPModels.jl

Job Properties

  • Time of benchmark: 12 Jul 2024 - 21:45
  • Package commit: 2ed491
  • Julia commit: 48d4fd
  • Julia command flags: None
  • Environment variables: None

Results

Below is a table of this job's results, obtained by running the benchmarks.
The values listed in the ID column have the structure [parent_group, child_group, ..., key], and can be used to
index into the BaseBenchmarks suite to retrieve the corresponding benchmarks.
The percentages accompanying time and memory values in the below table are noise tolerances. The "true"
time/memory value for a given benchmark is expected to fall within this percentage of the reported value.
An empty cell means that the value was zero.

ID time GC time memory allocations
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "camshape"] 406.312 ms (5%) 1.63 MiB (1%) 12350
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "catenary"] 11.156 ms (5%) 369.45 KiB (1%) 2390
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "chain"] 21.582 ms (5%) 283.58 KiB (1%) 1931
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "channel"] 392.143 ms (5%) 864.81 KiB (1%) 5784
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "clnlbeam"] 13.349 ms (5%) 124.95 KiB (1%) 800
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "controlinvestment"] 18.668 ms (5%) 145.08 KiB (1%) 968
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "elec"] 917.587 s (5%) 23.783 ms 232.44 MiB (1%) 1905099
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "hovercraft1d"] 619.478 μs (5%) 5.57 MiB (1%) 386
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "marine"] 429.203 ms (5%) 13.434 ms 199.66 MiB (1%) 1061157
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "polygon1"] 15.406 ms (5%) 197.33 KiB (1%) 1071
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "polygon3"] 72.969 ms (5%) 626.70 KiB (1%) 4406
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "robotarm"] 75.877 ms (5%) 827.45 KiB (1%) 5897
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "structural"] 1.829 μs (5%) 2.58 KiB (1%) 5
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "arglina"] 1.849 μs (5%) 2.50 KiB (1%) 2
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "arglinb"] 1.873 μs (5%) 2.50 KiB (1%) 2
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "bdqrtic"] 73.296 ms (5%) 339.52 KiB (1%) 2459
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "brownal"] 16.336 s (5%) 20.634 ms 201.67 MiB (1%) 1465536
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "broyden3d"] 24.200 ms (5%) 213.19 KiB (1%) 1469
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "brybnd"] 83.375 ms (5%) 213.19 KiB (1%) 1469
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "dixon3dq"] 1.365 μs (5%) 2.50 KiB (1%) 2
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "errinros_mod"] 64.024 ms (5%) 340.28 KiB (1%) 2465
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "freuroth"] 74.553 ms (5%) 340.28 KiB (1%) 2465
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "hovercraft1d"] 920.817 ns (5%) 2.50 KiB (1%) 2
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "integreq"] 17.045 s (5%) 213.19 KiB (1%) 1469
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "morebv"] 35.929 ms (5%) 213.19 KiB (1%) 1469
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "penalty1"] 18.660 ms (5%) 213.19 KiB (1%) 1469
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "power"] 2.776 ms (5%) 85.33 KiB (1%) 467
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "sbrybnd"] 71.211 ms (5%) 213.19 KiB (1%) 1469
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "spmsrtls"] 365.378 ms (5%) 1.84 MiB (1%) 13968
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "tquartic"] 11.662 ms (5%) 212.42 KiB (1%) 1463
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "camshape"] 46.493 ms (5%) 2.110 ms 88.01 MiB (1%) 111238
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "catenary"] 13.252 ms (5%) 22.76 MiB (1%) 32114
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "chain"] 7.653 ms (5%) 18.23 MiB (1%) 45610
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "channel"] 109.155 ms (5%) 3.191 ms 143.56 MiB (1%) 140851
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "clnlbeam"] 5.545 ms (5%) 13.33 MiB (1%) 42891
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "controlinvestment"] 12.938 ms (5%) 26.63 MiB (1%) 60845
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "elec"] 30.519 s (5%) 276.713 ms 15.04 GiB (1%) 5393469
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "hovercraft1d"] 1.712 ms (5%) 4.07 MiB (1%) 13105
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "marine"] 5.547 ms (5%) 10.16 MiB (1%) 35034
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "polygon1"] 34.763 ms (5%) 51.26 MiB (1%) 35422
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "polygon3"] 37.424 ms (5%) 73.41 MiB (1%) 71003
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "robotarm"] 27.240 ms (5%) 53.72 MiB (1%) 47405
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "structural"] 4.057 ms (5%) 15.03 MiB (1%) 73038
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "camshape"] 568.121 ms (5%) 41.688 ms 323.29 MiB (1%) 1610492
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "catenary"] 113.656 ms (5%) 57.70 MiB (1%) 680133
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "chain"] 140.295 ms (5%) 67.94 MiB (1%) 920524
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "channel"] 741.787 ms (5%) 95.821 ms 284.62 MiB (1%) 3961190
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "clnlbeam"] 138.653 ms (5%) 68.43 MiB (1%) 792422
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "controlinvestment"] 124.757 ms (5%) 71.36 MiB (1%) 807944
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "elec"] 743.082 s (5%) 3.188 s 36.64 GiB (1%) 104920002
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "hovercraft1d"] 11.861 ms (5%) 11.17 MiB (1%) 103117
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "marine"] 92.836 ms (5%) 45.68 MiB (1%) 498242
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "polygon1"] 57.322 ms (5%) 33.28 MiB (1%) 357361
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "polygon3"] 250.308 ms (5%) 17.967 ms 145.26 MiB (1%) 970123
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "robotarm"] 180.207 ms (5%) 7.930 ms 101.54 MiB (1%) 840142
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "structural"] 141.682 ms (5%) 2.973 ms 151.56 MiB (1%) 442523

Benchmark Group List

Here's a list of all the benchmark groups executed by this job:

  • ["hess_coord", "optimized", "Float64", "scalable_cons", "sparse"]
  • ["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse"]
  • ["hessian_backend", "optimized", "Float64", "scalable", "sparse"]
  • ["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics"]

Julia versioninfo

Julia Version 1.10.4
Commit 48d4fd48430 (2024-06-04 10:41 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
      Ubuntu 22.04.4 LTS
  uname: Linux 6.5.0-1023-azure #24~22.04.1-Ubuntu SMP Wed Jun 12 19:55:26 UTC 2024 x86_64 x86_64
  CPU: AMD EPYC 7763 64-Core Processor: 
              speed         user         nice          sys         idle          irq
       #1  2445 MHz      22770 s          0 s        923 s      60873 s          0 s
       #2  2595 MHz      25387 s          0 s        998 s      58205 s          0 s
       #3  2445 MHz      24787 s          0 s       1007 s      58785 s          0 s
       #4  3244 MHz      21639 s          2 s       1070 s      61879 s          0 s
  Memory: 15.606491088867188 GB (12096.6484375 MB free)
  Uptime: 8473.98 sec
  Load Avg:  1.06  1.04  1.0
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)
Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)

Baseline result

Benchmark Report for /home/runner/work/ADNLPModels.jl/ADNLPModels.jl

Job Properties

  • Time of benchmark: 12 Jul 2024 - 22:48
  • Package commit: 093eb8
  • Julia commit: 48d4fd
  • Julia command flags: None
  • Environment variables: None

Results

Below is a table of this job's results, obtained by running the benchmarks.
The values listed in the ID column have the structure [parent_group, child_group, ..., key], and can be used to
index into the BaseBenchmarks suite to retrieve the corresponding benchmarks.
The percentages accompanying time and memory values in the below table are noise tolerances. The "true"
time/memory value for a given benchmark is expected to fall within this percentage of the reported value.
An empty cell means that the value was zero.

ID time GC time memory allocations
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "camshape"] 410.102 ms (5%) 1.63 MiB (1%) 12350
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "catenary"] 11.488 ms (5%) 369.45 KiB (1%) 2390
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "chain"] 21.655 ms (5%) 283.58 KiB (1%) 1931
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "channel"] 525.892 ms (5%) 1.12 MiB (1%) 7710
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "clnlbeam"] 13.903 ms (5%) 124.95 KiB (1%) 800
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "controlinvestment"] 18.511 ms (5%) 145.08 KiB (1%) 968
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "elec"] 913.565 s (5%) 18.045 ms 232.44 MiB (1%) 1905099
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "hovercraft1d"] 805.074 μs (5%) 5.57 MiB (1%) 386
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "marine"] 400.245 ms (5%) 9.843 ms 193.42 MiB (1%) 1027996
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "polygon1"] 27.955 ms (5%) 334.39 KiB (1%) 1923
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "polygon3"] 96.917 ms (5%) 829.52 KiB (1%) 5873
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "robotarm"] 13.062 s (5%) 394.488 ms 132.42 MiB (1%) 981023
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "structural"] 14.056 ms (5%) 96.47 KiB (1%) 854
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "arglina"] 49.110 ms (5%) 332.41 KiB (1%) 2465
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "arglinb"] 41.422 ms (5%) 332.41 KiB (1%) 2465
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "bdqrtic"] 73.654 ms (5%) 339.52 KiB (1%) 2459
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "brownal"] 16.354 s (5%) 16.089 ms 201.87 MiB (1%) 1467003
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "broyden3d"] 24.206 ms (5%) 213.19 KiB (1%) 1469
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "brybnd"] 64.746 ms (5%) 213.19 KiB (1%) 1469
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "dixon3dq"] 8.835 ms (5%) 205.31 KiB (1%) 1469
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "errinros_mod"] 63.895 ms (5%) 340.28 KiB (1%) 2465
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "freuroth"] 74.845 ms (5%) 340.28 KiB (1%) 2465
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "hovercraft1d"] 1.698 ms (5%) 119.56 KiB (1%) 797
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "integreq"] 19.381 s (5%) 213.19 KiB (1%) 1469
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "morebv"] 35.936 ms (5%) 213.19 KiB (1%) 1469
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "penalty1"] 18.635 ms (5%) 213.19 KiB (1%) 1469
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "power"] 2.812 ms (5%) 85.33 KiB (1%) 467
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "sbrybnd"] 70.892 ms (5%) 213.19 KiB (1%) 1469
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "spmsrtls"] 440.380 ms (5%) 2.20 MiB (1%) 16761
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "tquartic"] 11.678 ms (5%) 212.42 KiB (1%) 1463
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "camshape"] 41.949 ms (5%) 87.82 MiB (1%) 111211
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "catenary"] 11.591 ms (5%) 22.47 MiB (1%) 32075
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "chain"] 7.371 ms (5%) 18.17 MiB (1%) 45581
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "channel"] 101.615 ms (5%) 2.991 ms 142.72 MiB (1%) 140804
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "clnlbeam"] 5.456 ms (5%) 13.29 MiB (1%) 42875
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "controlinvestment"] 12.196 ms (5%) 26.58 MiB (1%) 60823
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "elec"] 12.529 s (5%) 206.290 ms 14.97 GiB (1%) 5390328
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "hovercraft1d"] 1.644 ms (5%) 4.05 MiB (1%) 13089
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "marine"] 4.570 ms (5%) 9.94 MiB (1%) 34967
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "polygon1"] 30.105 ms (5%) 50.91 MiB (1%) 35388
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "polygon3"] 35.503 ms (5%) 73.13 MiB (1%) 70969
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "robotarm"] 26.633 ms (5%) 53.71 MiB (1%) 48697
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "structural"] 4.040 ms (5%) 15.03 MiB (1%) 73032
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "camshape"] 579.535 ms (5%) 52.750 ms 323.10 MiB (1%) 1610465
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "catenary"] 115.316 ms (5%) 57.41 MiB (1%) 680094
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "chain"] 145.250 ms (5%) 67.79 MiB (1%) 919001
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "channel"] 720.435 ms (5%) 283.78 MiB (1%) 3961143
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "clnlbeam"] 141.092 ms (5%) 68.33 MiB (1%) 791410
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "controlinvestment"] 127.814 ms (5%) 71.31 MiB (1%) 807922
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "elec"] 709.052 s (5%) 4.009 s 36.56 GiB (1%) 104916861
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "hovercraft1d"] 40.862 ms (5%) 26.325 ms 11.15 MiB (1%) 103101
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "marine"] 90.737 ms (5%) 45.46 MiB (1%) 498175
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "polygon1"] 53.904 ms (5%) 32.84 MiB (1%) 355827
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "polygon3"] 218.815 ms (5%) 144.98 MiB (1%) 970089
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "robotarm"] 175.973 ms (5%) 8.787 ms 101.53 MiB (1%) 841434
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "structural"] 150.957 ms (5%) 3.469 ms 151.56 MiB (1%) 442517

Benchmark Group List

Here's a list of all the benchmark groups executed by this job:

  • ["hess_coord", "optimized", "Float64", "scalable_cons", "sparse"]
  • ["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse"]
  • ["hessian_backend", "optimized", "Float64", "scalable", "sparse"]
  • ["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics"]

Julia versioninfo

Julia Version 1.10.4
Commit 48d4fd48430 (2024-06-04 10:41 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
      Ubuntu 22.04.4 LTS
  uname: Linux 6.5.0-1023-azure #24~22.04.1-Ubuntu SMP Wed Jun 12 19:55:26 UTC 2024 x86_64 x86_64
  CPU: AMD EPYC 7763 64-Core Processor: 
              speed         user         nice          sys         idle          irq
       #1  2445 MHz      32056 s          0 s       1293 s      89131 s          0 s
       #2  2445 MHz      35724 s          0 s       1410 s      85376 s          0 s
       #3  3243 MHz      34656 s          0 s       1382 s      86462 s          0 s
       #4  3216 MHz      30145 s          2 s       1532 s      90832 s          0 s
  Memory: 15.606491088867188 GB (12811.75390625 MB free)
  Uptime: 12272.15 sec
  Load Avg:  1.02  1.03  1.0
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)
Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)

Runtime information

Runtime Info
BLAS #threads 2
BLAS.vendor() lbt
Sys.CPU_THREADS 4

lscpu output:

Architecture:                       x86_64
CPU op-mode(s):                     32-bit, 64-bit
Address sizes:                      48 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             4
On-line CPU(s) list:                0-3
Vendor ID:                          AuthenticAMD
Model name:                         AMD EPYC 7763 64-Core Processor
CPU family:                         25
Model:                              1
Thread(s) per core:                 2
Core(s) per socket:                 2
Socket(s):                          1
Stepping:                           1
BogoMIPS:                           4890.86
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl tsc_reliable nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy svm cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext invpcid_single vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves clzero xsaveerptr rdpru arat npt nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload umip vaes vpclmulqdq rdpid fsrm
Virtualization:                     AMD-V
Hypervisor vendor:                  Microsoft
Virtualization type:                full
L1d cache:                          64 KiB (2 instances)
L1i cache:                          64 KiB (2 instances)
L2 cache:                           1 MiB (2 instances)
L3 cache:                           32 MiB (1 instance)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0-3
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Not affected
Vulnerability Retbleed:             Not affected
Vulnerability Spec rstack overflow: Vulnerable: Safe RET, no microcode
Vulnerability Spec store bypass:    Vulnerable
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Retpolines; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected
Cpu Property Value
Brand AMD EPYC 7763 64-Core Processor
Vendor :AMD
Architecture :Unknown
Model Family: 0xaf, Model: 0x01, Stepping: 0x01, Type: 0x00
Cores 16 physical cores, 16 logical cores (on executing CPU)
No Hyperthreading hardware capability detected
Clock Frequencies Not supported by CPU
Data Cache Level 1:3 : (32, 512, 32768) kbytes
64 byte cache line size
Address Size 48 bits virtual, 48 bits physical
SIMD 256 bit = 32 byte max. SIMD vector size
Time Stamp Counter TSC is accessible via rdtsc
TSC runs at constant rate (invariant from clock frequency)
Perf. Monitoring Performance Monitoring Counters (PMC) are not supported
Hypervisor Yes, Microsoft

@gdalle
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gdalle commented Jul 17, 2024 •
edited
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My recommendation would be to use the decompression functions provided with SparseMatrixColorings:

The latter is more efficient when you provide an additional argument, namely the set of stars resulting from the star coloring. This object can be accessed by replacing ADTypes.symmetric_coloring with SparseMatrixColorings.symmetric_coloring_detailed.

In the API of the decompression functions:

  • A is the matrix you want to reconstruct
  • S is the boolean sparsity pattern
  • B is the compressed matrix, whose columns or rows contain the matrix-vector products (directional derivatives)

You can take a look at the following files in DI to see how these functions are used:

@amontoison amontoison force-pushed the symmetric_coloring branch from 22e0e49 to 3ad77de Compare July 18, 2024 01:45
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Benchmark result

Judge result

Benchmark Report for /home/runner/work/ADNLPModels.jl/ADNLPModels.jl

Job Properties

  • Time of benchmarks:
    • Target: 18 Jul 2024 - 03:44
    • Baseline: 18 Jul 2024 - 04:38
  • Package commits:
    • Target: cc1a74
    • Baseline: 88a9b7
  • Julia commits:
    • Target: 48d4fd
    • Baseline: 48d4fd
  • Julia command flags:
    • Target: None
    • Baseline: None
  • Environment variables:
    • Target: None
    • Baseline: None

Results

A ratio greater than 1.0 denotes a possible regression (marked with ❌), while a ratio less
than 1.0 denotes a possible improvement (marked with ✅). Only significant results - results
that indicate possible regressions or improvements - are shown below (thus, an empty table means that all
benchmark results remained invariant between builds).

ID time ratio memory ratio
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "channel"] 0.75 (5%) ✅ 0.76 (1%) ✅
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "hovercraft1d"] 0.64 (5%) ✅ 1.00 (1%)
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "marine"] 1.08 (5%) ❌ 1.03 (1%) ❌
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "polygon1"] 0.55 (5%) ✅ 0.59 (1%) ✅
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "polygon3"] 0.74 (5%) ✅ 0.76 (1%) ✅
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "robotarm"] 0.01 (5%) ✅ 0.01 (1%) ✅
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "structural"] 0.00 (5%) ✅ 0.03 (1%) ✅
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "arglina"] 0.00 (5%) ✅ 0.01 (1%) ✅
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "arglinb"] 0.00 (5%) ✅ 0.01 (1%) ✅
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "dixon3dq"] 0.00 (5%) ✅ 0.01 (1%) ✅
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "hovercraft1d"] 0.00 (5%) ✅ 0.02 (1%) ✅
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "morebv"] 1.06 (5%) ❌ 1.00 (1%)
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "spmsrtls"] 0.85 (5%) ✅ 0.84 (1%) ✅
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "catenary"] 1.04 (5%) 1.01 (1%) ❌
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "elec"] 1.65 (5%) ❌ 1.01 (1%)
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "hovercraft1d"] 1.07 (5%) ❌ 1.01 (1%)
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "marine"] 1.06 (5%) ❌ 1.02 (1%) ❌
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "hovercraft1d"] 0.32 (5%) ✅ 1.00 (1%)
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "polygon1"] 1.02 (5%) 1.01 (1%) ❌
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "polygon3"] 1.07 (5%) ❌ 1.00 (1%)

Benchmark Group List

Here's a list of all the benchmark groups executed by this job:

  • ["hess_coord", "optimized", "Float64", "scalable_cons", "sparse"]
  • ["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse"]
  • ["hessian_backend", "optimized", "Float64", "scalable", "sparse"]
  • ["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics"]

Julia versioninfo

Target

Julia Version 1.10.4
Commit 48d4fd48430 (2024-06-04 10:41 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
      Ubuntu 22.04.4 LTS
  uname: Linux 6.5.0-1023-azure #24~22.04.1-Ubuntu SMP Wed Jun 12 19:55:26 UTC 2024 x86_64 x86_64
  CPU: AMD EPYC 7763 64-Core Processor: 
              speed         user         nice          sys         idle          irq
       #1  3262 MHz      18495 s          0 s        732 s      53413 s          0 s
       #2  3242 MHz      25228 s          0 s        752 s      46681 s          0 s
       #3  2445 MHz      20707 s          0 s        839 s      51099 s          0 s
       #4  2445 MHz      17156 s          0 s        884 s      54607 s          0 s
  Memory: 15.606491088867188 GB (12079.69921875 MB free)
  Uptime: 7277.08 sec
  Load Avg:  1.03  1.02  1.0
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)
Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)

Baseline

Julia Version 1.10.4
Commit 48d4fd48430 (2024-06-04 10:41 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
      Ubuntu 22.04.4 LTS
  uname: Linux 6.5.0-1023-azure #24~22.04.1-Ubuntu SMP Wed Jun 12 19:55:26 UTC 2024 x86_64 x86_64
  CPU: AMD EPYC 7763 64-Core Processor: 
              speed         user         nice          sys         idle          irq
       #1  2445 MHz      26797 s          0 s       1013 s      77377 s          0 s
       #2  3242 MHz      33922 s          0 s       1074 s      70214 s          0 s
       #3  2445 MHz      28959 s          0 s       1136 s      75103 s          0 s
       #4  2599 MHz      24498 s          0 s       1239 s      79457 s          0 s
  Memory: 15.606491088867188 GB (12735.20703125 MB free)
  Uptime: 10536.65 sec
  Load Avg:  1.06  1.02  1.0
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)
Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)

Target result

Benchmark Report for /home/runner/work/ADNLPModels.jl/ADNLPModels.jl

Job Properties

  • Time of benchmark: 18 Jul 2024 - 3:44
  • Package commit: cc1a74
  • Julia commit: 48d4fd
  • Julia command flags: None
  • Environment variables: None

Results

Below is a table of this job's results, obtained by running the benchmarks.
The values listed in the ID column have the structure [parent_group, child_group, ..., key], and can be used to
index into the BaseBenchmarks suite to retrieve the corresponding benchmarks.
The percentages accompanying time and memory values in the below table are noise tolerances. The "true"
time/memory value for a given benchmark is expected to fall within this percentage of the reported value.
An empty cell means that the value was zero.

ID time GC time memory allocations
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "camshape"] 419.801 ms (5%) 1.63 MiB (1%) 12350
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "catenary"] 11.130 ms (5%) 369.45 KiB (1%) 2390
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "chain"] 21.483 ms (5%) 283.58 KiB (1%) 1931
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "channel"] 391.898 ms (5%) 864.81 KiB (1%) 5784
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "clnlbeam"] 13.390 ms (5%) 124.95 KiB (1%) 800
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "controlinvestment"] 17.920 ms (5%) 145.08 KiB (1%) 968
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "elec"] 911.716 s (5%) 16.484 ms 232.44 MiB (1%) 1905099
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "hovercraft1d"] 575.949 μs (5%) 5.57 MiB (1%) 386
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "marine"] 426.910 ms (5%) 13.157 ms 199.66 MiB (1%) 1061157
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "polygon1"] 15.473 ms (5%) 197.33 KiB (1%) 1071
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "polygon3"] 72.517 ms (5%) 626.70 KiB (1%) 4406
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "robotarm"] 75.382 ms (5%) 827.45 KiB (1%) 5897
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "structural"] 1.766 μs (5%) 2.58 KiB (1%) 5
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "arglina"] 1.799 μs (5%) 2.50 KiB (1%) 2
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "arglinb"] 1.801 μs (5%) 2.50 KiB (1%) 2
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "bdqrtic"] 73.358 ms (5%) 339.52 KiB (1%) 2459
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "brownal"] 16.168 s (5%) 5.670 ms 201.67 MiB (1%) 1465536
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "broyden3d"] 24.264 ms (5%) 213.19 KiB (1%) 1469
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "brybnd"] 64.697 ms (5%) 213.19 KiB (1%) 1469
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "dixon3dq"] 1.265 μs (5%) 2.50 KiB (1%) 2
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "errinros_mod"] 64.612 ms (5%) 340.28 KiB (1%) 2465
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "freuroth"] 74.201 ms (5%) 340.28 KiB (1%) 2465
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "hovercraft1d"] 952.889 ns (5%) 2.50 KiB (1%) 2
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "integreq"] 17.035 s (5%) 213.19 KiB (1%) 1469
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "morebv"] 38.023 ms (5%) 213.19 KiB (1%) 1469
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "penalty1"] 18.719 ms (5%) 213.19 KiB (1%) 1469
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "power"] 2.655 ms (5%) 85.33 KiB (1%) 467
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "sbrybnd"] 70.862 ms (5%) 213.19 KiB (1%) 1469
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "spmsrtls"] 370.307 ms (5%) 1.84 MiB (1%) 13968
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "tquartic"] 11.673 ms (5%) 212.42 KiB (1%) 1463
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "camshape"] 41.546 ms (5%) 88.01 MiB (1%) 111238
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "catenary"] 11.706 ms (5%) 22.79 MiB (1%) 32115
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "chain"] 7.225 ms (5%) 18.23 MiB (1%) 45610
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "channel"] 100.142 ms (5%) 2.924 ms 143.58 MiB (1%) 140847
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "clnlbeam"] 5.475 ms (5%) 13.35 MiB (1%) 42892
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "controlinvestment"] 11.887 ms (5%) 26.63 MiB (1%) 60845
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "elec"] 21.725 s (5%) 242.107 ms 15.04 GiB (1%) 5393473
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "hovercraft1d"] 1.692 ms (5%) 4.07 MiB (1%) 13105
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "marine"] 4.752 ms (5%) 10.16 MiB (1%) 35034
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "polygon1"] 30.329 ms (5%) 51.29 MiB (1%) 35423
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "polygon3"] 34.930 ms (5%) 73.44 MiB (1%) 71004
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "robotarm"] 25.583 ms (5%) 53.73 MiB (1%) 47406
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "structural"] 3.834 ms (5%) 15.03 MiB (1%) 73038
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "camshape"] 544.988 ms (5%) 37.389 ms 323.29 MiB (1%) 1610492
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "catenary"] 110.245 ms (5%) 57.73 MiB (1%) 680134
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "chain"] 138.059 ms (5%) 67.94 MiB (1%) 920524
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "channel"] 696.808 ms (5%) 81.019 ms 284.64 MiB (1%) 3961186
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "clnlbeam"] 136.875 ms (5%) 68.46 MiB (1%) 792423
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "controlinvestment"] 121.802 ms (5%) 71.36 MiB (1%) 807944
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "elec"] 465.803 s (5%) 2.786 s 36.64 GiB (1%) 104920006
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "hovercraft1d"] 11.740 ms (5%) 11.17 MiB (1%) 103117
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "marine"] 89.696 ms (5%) 45.68 MiB (1%) 498242
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "polygon1"] 51.977 ms (5%) 33.31 MiB (1%) 357362
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "polygon3"] 227.976 ms (5%) 8.170 ms 145.28 MiB (1%) 970124
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "robotarm"] 172.038 ms (5%) 8.290 ms 101.55 MiB (1%) 840143
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "structural"] 138.036 ms (5%) 2.704 ms 151.56 MiB (1%) 442523

Benchmark Group List

Here's a list of all the benchmark groups executed by this job:

  • ["hess_coord", "optimized", "Float64", "scalable_cons", "sparse"]
  • ["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse"]
  • ["hessian_backend", "optimized", "Float64", "scalable", "sparse"]
  • ["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics"]

Julia versioninfo

Julia Version 1.10.4
Commit 48d4fd48430 (2024-06-04 10:41 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
      Ubuntu 22.04.4 LTS
  uname: Linux 6.5.0-1023-azure #24~22.04.1-Ubuntu SMP Wed Jun 12 19:55:26 UTC 2024 x86_64 x86_64
  CPU: AMD EPYC 7763 64-Core Processor: 
              speed         user         nice          sys         idle          irq
       #1  3262 MHz      18495 s          0 s        732 s      53413 s          0 s
       #2  3242 MHz      25228 s          0 s        752 s      46681 s          0 s
       #3  2445 MHz      20707 s          0 s        839 s      51099 s          0 s
       #4  2445 MHz      17156 s          0 s        884 s      54607 s          0 s
  Memory: 15.606491088867188 GB (12079.69921875 MB free)
  Uptime: 7277.08 sec
  Load Avg:  1.03  1.02  1.0
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)
Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)

Baseline result

Benchmark Report for /home/runner/work/ADNLPModels.jl/ADNLPModels.jl

Job Properties

  • Time of benchmark: 18 Jul 2024 - 4:38
  • Package commit: 88a9b7
  • Julia commit: 48d4fd
  • Julia command flags: None
  • Environment variables: None

Results

Below is a table of this job's results, obtained by running the benchmarks.
The values listed in the ID column have the structure [parent_group, child_group, ..., key], and can be used to
index into the BaseBenchmarks suite to retrieve the corresponding benchmarks.
The percentages accompanying time and memory values in the below table are noise tolerances. The "true"
time/memory value for a given benchmark is expected to fall within this percentage of the reported value.
An empty cell means that the value was zero.

ID time GC time memory allocations
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "camshape"] 406.770 ms (5%) 1.63 MiB (1%) 12350
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "catenary"] 11.201 ms (5%) 369.45 KiB (1%) 2390
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "chain"] 21.575 ms (5%) 283.58 KiB (1%) 1931
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "channel"] 521.937 ms (5%) 1.12 MiB (1%) 7710
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "clnlbeam"] 13.471 ms (5%) 124.95 KiB (1%) 800
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "controlinvestment"] 17.881 ms (5%) 145.08 KiB (1%) 968
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "elec"] 915.634 s (5%) 12.902 ms 232.44 MiB (1%) 1905099
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "hovercraft1d"] 897.501 μs (5%) 5.57 MiB (1%) 386
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "marine"] 395.931 ms (5%) 9.392 ms 193.42 MiB (1%) 1027996
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "polygon1"] 27.903 ms (5%) 334.39 KiB (1%) 1923
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "polygon3"] 97.803 ms (5%) 829.52 KiB (1%) 5873
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "robotarm"] 12.676 s (5%) 4.583 ms 132.42 MiB (1%) 981023
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "structural"] 13.942 ms (5%) 96.47 KiB (1%) 854
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "arglina"] 48.992 ms (5%) 332.41 KiB (1%) 2465
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "arglinb"] 41.488 ms (5%) 332.41 KiB (1%) 2465
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "bdqrtic"] 73.748 ms (5%) 339.52 KiB (1%) 2459
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "brownal"] 16.114 s (5%) 15.103 ms 201.87 MiB (1%) 1467003
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "broyden3d"] 24.105 ms (5%) 213.19 KiB (1%) 1469
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "brybnd"] 64.760 ms (5%) 213.19 KiB (1%) 1469
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "dixon3dq"] 8.945 ms (5%) 205.31 KiB (1%) 1469
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "errinros_mod"] 63.220 ms (5%) 340.28 KiB (1%) 2465
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "freuroth"] 74.788 ms (5%) 340.28 KiB (1%) 2465
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "hovercraft1d"] 1.732 ms (5%) 119.56 KiB (1%) 797
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "integreq"] 17.002 s (5%) 213.19 KiB (1%) 1469
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "morebv"] 35.873 ms (5%) 213.19 KiB (1%) 1469
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "penalty1"] 18.593 ms (5%) 213.19 KiB (1%) 1469
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "power"] 2.753 ms (5%) 85.33 KiB (1%) 467
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "sbrybnd"] 71.082 ms (5%) 213.19 KiB (1%) 1469
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "spmsrtls"] 437.342 ms (5%) 2.20 MiB (1%) 16761
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "tquartic"] 11.659 ms (5%) 212.42 KiB (1%) 1463
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "camshape"] 40.346 ms (5%) 87.82 MiB (1%) 111211
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "catenary"] 11.283 ms (5%) 22.47 MiB (1%) 32075
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "chain"] 6.957 ms (5%) 18.17 MiB (1%) 45581
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "channel"] 97.485 ms (5%) 142.72 MiB (1%) 140799
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "clnlbeam"] 5.245 ms (5%) 13.29 MiB (1%) 42875
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "controlinvestment"] 11.785 ms (5%) 26.58 MiB (1%) 60823
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "elec"] 13.165 s (5%) 583.710 ms 14.97 GiB (1%) 5390328
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "hovercraft1d"] 1.578 ms (5%) 4.05 MiB (1%) 13089
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "marine"] 4.472 ms (5%) 9.94 MiB (1%) 34967
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "polygon1"] 29.655 ms (5%) 50.91 MiB (1%) 35388
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "polygon3"] 34.343 ms (5%) 73.13 MiB (1%) 70969
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "robotarm"] 25.691 ms (5%) 53.71 MiB (1%) 48697
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "structural"] 3.788 ms (5%) 15.03 MiB (1%) 73032
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "camshape"] 545.268 ms (5%) 41.062 ms 323.10 MiB (1%) 1610465
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "catenary"] 111.859 ms (5%) 57.41 MiB (1%) 680094
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "chain"] 138.691 ms (5%) 67.88 MiB (1%) 920495
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "channel"] 698.120 ms (5%) 79.822 ms 283.78 MiB (1%) 3961138
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "clnlbeam"] 139.296 ms (5%) 68.33 MiB (1%) 791410
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "controlinvestment"] 121.094 ms (5%) 71.31 MiB (1%) 807922
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "elec"] 457.669 s (5%) 3.658 s 36.56 GiB (1%) 104916861
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "hovercraft1d"] 37.151 ms (5%) 22.970 ms 11.15 MiB (1%) 103101
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "marine"] 89.310 ms (5%) 45.46 MiB (1%) 498175
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "polygon1"] 51.108 ms (5%) 32.84 MiB (1%) 355827
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "polygon3"] 212.892 ms (5%) 144.98 MiB (1%) 970089
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "robotarm"] 173.708 ms (5%) 6.397 ms 101.53 MiB (1%) 841434
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "structural"] 138.520 ms (5%) 151.56 MiB (1%) 442517

Benchmark Group List

Here's a list of all the benchmark groups executed by this job:

  • ["hess_coord", "optimized", "Float64", "scalable_cons", "sparse"]
  • ["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse"]
  • ["hessian_backend", "optimized", "Float64", "scalable", "sparse"]
  • ["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics"]

Julia versioninfo

Julia Version 1.10.4
Commit 48d4fd48430 (2024-06-04 10:41 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
      Ubuntu 22.04.4 LTS
  uname: Linux 6.5.0-1023-azure #24~22.04.1-Ubuntu SMP Wed Jun 12 19:55:26 UTC 2024 x86_64 x86_64
  CPU: AMD EPYC 7763 64-Core Processor: 
              speed         user         nice          sys         idle          irq
       #1  2445 MHz      26797 s          0 s       1013 s      77377 s          0 s
       #2  3242 MHz      33922 s          0 s       1074 s      70214 s          0 s
       #3  2445 MHz      28959 s          0 s       1136 s      75103 s          0 s
       #4  2599 MHz      24498 s          0 s       1239 s      79457 s          0 s
  Memory: 15.606491088867188 GB (12735.20703125 MB free)
  Uptime: 10536.65 sec
  Load Avg:  1.06  1.02  1.0
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)
Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)

Runtime information

Runtime Info
BLAS #threads 2
BLAS.vendor() lbt
Sys.CPU_THREADS 4

lscpu output:

Architecture:                       x86_64
CPU op-mode(s):                     32-bit, 64-bit
Address sizes:                      48 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             4
On-line CPU(s) list:                0-3
Vendor ID:                          AuthenticAMD
Model name:                         AMD EPYC 7763 64-Core Processor
CPU family:                         25
Model:                              1
Thread(s) per core:                 2
Core(s) per socket:                 2
Socket(s):                          1
Stepping:                           1
BogoMIPS:                           4890.84
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl tsc_reliable nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy svm cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext invpcid_single vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves clzero xsaveerptr rdpru arat npt nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload umip vaes vpclmulqdq rdpid fsrm
Virtualization:                     AMD-V
Hypervisor vendor:                  Microsoft
Virtualization type:                full
L1d cache:                          64 KiB (2 instances)
L1i cache:                          64 KiB (2 instances)
L2 cache:                           1 MiB (2 instances)
L3 cache:                           32 MiB (1 instance)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0-3
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Not affected
Vulnerability Retbleed:             Not affected
Vulnerability Spec rstack overflow: Vulnerable: Safe RET, no microcode
Vulnerability Spec store bypass:    Vulnerable
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Retpolines; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected
Cpu Property Value
Brand AMD EPYC 7763 64-Core Processor
Vendor :AMD
Architecture :Unknown
Model Family: 0xaf, Model: 0x01, Stepping: 0x01, Type: 0x00
Cores 16 physical cores, 16 logical cores (on executing CPU)
No Hyperthreading hardware capability detected
Clock Frequencies Not supported by CPU
Data Cache Level 1:3 : (32, 512, 32768) kbytes
64 byte cache line size
Address Size 48 bits virtual, 48 bits physical
SIMD 256 bit = 32 byte max. SIMD vector size
Time Stamp Counter TSC is accessible via rdtsc
TSC runs at constant rate (invariant from clock frequency)
Perf. Monitoring Performance Monitoring Counters (PMC) are not supported
Hypervisor Yes, Microsoft

@gdalle gdalle mentioned this pull request Jul 18, 2024
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@gdalle
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gdalle commented Jul 18, 2024

You have the wrong version of SparseMatrixColorings in the benchmark manifest
https://github.com/JuliaSmoothOptimizers/ADNLPModels.jl/actions/runs/9994717737/job/27625170927?pr=271#step:6:127

@amontoison
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I need to do like yesterday and bump benchmark/Manifest.toml.

@amontoison amontoison force-pushed the symmetric_coloring branch from 70d67e3 to 99ebc68 Compare July 18, 2024 17:15
@amontoison
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If we have the same results after the benchmarks, I merge the PR and do a new release.

@gdalle
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gdalle commented Jul 18, 2024

Hell yeah!

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codecov bot commented Jul 18, 2024

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 95.53%. Comparing base (1d114e0) to head (99ebc68).
Report is 9 commits behind head on main.

Additional details and impacted files 
@@            Coverage Diff             @@
##             main     #271      +/-   ##
==========================================
+ Coverage   95.22%   95.53%   +0.31%     
==========================================
  Files          13       13              
  Lines        1445     1546     +101     
==========================================
+ Hits         1376     1477     +101     
  Misses         69       69

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Benchmark result

Judge result

Benchmark Report for /home/runner/work/ADNLPModels.jl/ADNLPModels.jl

Job Properties

  • Time of benchmarks:
    • Target: 18 Jul 2024 - 19:21
    • Baseline: 18 Jul 2024 - 20:18
  • Package commits:
    • Target: 13eb96
    • Baseline: 1650fc
  • Julia commits:
    • Target: 48d4fd
    • Baseline: 48d4fd
  • Julia command flags:
    • Target: None
    • Baseline: None
  • Environment variables:
    • Target: None
    • Baseline: None

Results

A ratio greater than 1.0 denotes a possible regression (marked with ❌), while a ratio less
than 1.0 denotes a possible improvement (marked with ✅). Only significant results - results
that indicate possible regressions or improvements - are shown below (thus, an empty table means that all
benchmark results remained invariant between builds).

ID time ratio memory ratio
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "chain"] 0.50 (5%) ✅ 0.51 (1%) ✅
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "channel"] 0.88 (5%) ✅ 0.88 (1%) ✅
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "hovercraft1d"] 0.49 (5%) ✅ 1.00 (1%)
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "marine"] 1.08 (5%) ❌ 1.03 (1%) ❌
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "polygon1"] 0.67 (5%) ✅ 0.69 (1%) ✅
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "polygon3"] 0.75 (5%) ✅ 0.76 (1%) ✅
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "robotarm"] 0.00 (5%) ✅ 0.00 (1%) ✅
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "structural"] 0.00 (5%) ✅ 0.03 (1%) ✅
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "arglina"] 0.00 (5%) ✅ 0.01 (1%) ✅
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "arglinb"] 0.00 (5%) ✅ 0.01 (1%) ✅
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "brybnd"] 0.83 (5%) ✅ 1.00 (1%)
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "dixon3dq"] 0.00 (5%) ✅ 0.01 (1%) ✅
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "hovercraft1d"] 0.00 (5%) ✅ 0.02 (1%) ✅
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "spmsrtls"] 0.82 (5%) ✅ 0.84 (1%) ✅
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "catenary"] 1.02 (5%) 1.01 (1%) ❌
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "elec"] 1.96 (5%) ❌ 1.01 (1%)
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "hovercraft1d"] 1.05 (5%) ❌ 1.01 (1%)
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "marine"] 1.06 (5%) ❌ 1.02 (1%) ❌
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "hovercraft1d"] 0.32 (5%) ✅ 1.00 (1%)
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "polygon1"] 1.01 (5%) 1.01 (1%) ❌
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "polygon3"] 1.07 (5%) ❌ 1.00 (1%)

Benchmark Group List

Here's a list of all the benchmark groups executed by this job:

  • ["hess_coord", "optimized", "Float64", "scalable_cons", "sparse"]
  • ["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse"]
  • ["hessian_backend", "optimized", "Float64", "scalable", "sparse"]
  • ["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics"]

Julia versioninfo

Target

Julia Version 1.10.4
Commit 48d4fd48430 (2024-06-04 10:41 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
      Ubuntu 22.04.4 LTS
  uname: Linux 6.5.0-1023-azure #24~22.04.1-Ubuntu SMP Wed Jun 12 19:55:26 UTC 2024 x86_64 x86_64
  CPU: AMD EPYC 7763 64-Core Processor: 
              speed         user         nice          sys         idle          irq
       #1  3215 MHz      19338 s          0 s        823 s      56109 s          0 s
       #2  3242 MHz      26127 s          0 s        864 s      49307 s          0 s
       #3  2445 MHz      20487 s          0 s        824 s      54989 s          0 s
       #4  2445 MHz      19943 s          0 s        946 s      55408 s          0 s
  Memory: 15.606491088867188 GB (11977.90625 MB free)
  Uptime: 7642.25 sec
  Load Avg:  1.01  1.05  1.0
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)
Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)

Baseline

Julia Version 1.10.4
Commit 48d4fd48430 (2024-06-04 10:41 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
      Ubuntu 22.04.4 LTS
  uname: Linux 6.5.0-1023-azure #24~22.04.1-Ubuntu SMP Wed Jun 12 19:55:26 UTC 2024 x86_64 x86_64
  CPU: AMD EPYC 7763 64-Core Processor: 
              speed         user         nice          sys         idle          irq
       #1  3242 MHz      28742 s          0 s       1064 s      81005 s          0 s
       #2  3253 MHz      36320 s          0 s       1188 s      73340 s          0 s
       #3  2445 MHz      27619 s          0 s       1110 s      82116 s          0 s
       #4  3239 MHz      27814 s          0 s       1288 s      81740 s          0 s
  Memory: 15.606491088867188 GB (12187.77734375 MB free)
  Uptime: 11102.05 sec
  Load Avg:  1.09  1.05  1.01
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)
Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)

Target result

Benchmark Report for /home/runner/work/ADNLPModels.jl/ADNLPModels.jl

Job Properties

  • Time of benchmark: 18 Jul 2024 - 19:21
  • Package commit: 13eb96
  • Julia commit: 48d4fd
  • Julia command flags: None
  • Environment variables: None

Results

Below is a table of this job's results, obtained by running the benchmarks.
The values listed in the ID column have the structure [parent_group, child_group, ..., key], and can be used to
index into the BaseBenchmarks suite to retrieve the corresponding benchmarks.
The percentages accompanying time and memory values in the below table are noise tolerances. The "true"
time/memory value for a given benchmark is expected to fall within this percentage of the reported value.
An empty cell means that the value was zero.

ID time GC time memory allocations
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "camshape"] 407.192 ms (5%) 1.63 MiB (1%) 12350
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "catenary"] 11.155 ms (5%) 369.45 KiB (1%) 2390
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "chain"] 10.815 ms (5%) 145.08 KiB (1%) 968
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "channel"] 459.023 ms (5%) 1003.31 KiB (1%) 6747
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "clnlbeam"] 13.436 ms (5%) 124.95 KiB (1%) 800
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "controlinvestment"] 18.830 ms (5%) 145.08 KiB (1%) 968
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "elec"] 912.417 s (5%) 20.416 ms 232.44 MiB (1%) 1905099
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "hovercraft1d"] 587.242 μs (5%) 5.57 MiB (1%) 386
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "marine"] 429.881 ms (5%) 14.037 ms 199.66 MiB (1%) 1061157
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "polygon1"] 18.576 ms (5%) 231.59 KiB (1%) 1284
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "polygon3"] 72.596 ms (5%) 626.70 KiB (1%) 4406
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "robotarm"] 56.465 ms (5%) 623.88 KiB (1%) 4424
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "structural"] 1.752 μs (5%) 2.58 KiB (1%) 5
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "arglina"] 1.784 μs (5%) 2.50 KiB (1%) 2
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "arglinb"] 1.766 μs (5%) 2.50 KiB (1%) 2
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "bdqrtic"] 72.998 ms (5%) 339.52 KiB (1%) 2459
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "brownal"] 16.273 s (5%) 5.965 ms 201.67 MiB (1%) 1465536
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "broyden3d"] 24.269 ms (5%) 213.19 KiB (1%) 1469
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "brybnd"] 64.771 ms (5%) 213.19 KiB (1%) 1469
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "dixon3dq"] 1.284 μs (5%) 2.50 KiB (1%) 2
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "errinros_mod"] 63.419 ms (5%) 340.28 KiB (1%) 2465
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "freuroth"] 74.356 ms (5%) 340.28 KiB (1%) 2465
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "hovercraft1d"] 888.932 ns (5%) 2.50 KiB (1%) 2
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "integreq"] 17.012 s (5%) 213.19 KiB (1%) 1469
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "morebv"] 35.969 ms (5%) 213.19 KiB (1%) 1469
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "penalty1"] 18.583 ms (5%) 213.19 KiB (1%) 1469
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "power"] 2.651 ms (5%) 85.33 KiB (1%) 467
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "sbrybnd"] 70.634 ms (5%) 213.19 KiB (1%) 1469
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "spmsrtls"] 362.262 ms (5%) 1.84 MiB (1%) 13968
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "tquartic"] 11.658 ms (5%) 212.42 KiB (1%) 1463
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "camshape"] 40.861 ms (5%) 88.01 MiB (1%) 111238
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "catenary"] 11.552 ms (5%) 22.76 MiB (1%) 32114
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "chain"] 7.167 ms (5%) 18.21 MiB (1%) 45606
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "channel"] 100.551 ms (5%) 2.745 ms 143.56 MiB (1%) 140849
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "clnlbeam"] 5.341 ms (5%) 13.33 MiB (1%) 42891
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "controlinvestment"] 11.850 ms (5%) 26.63 MiB (1%) 60845
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "elec"] 23.761 s (5%) 223.177 ms 15.04 GiB (1%) 5393469
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "hovercraft1d"] 1.669 ms (5%) 4.07 MiB (1%) 13105
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "marine"] 4.708 ms (5%) 10.16 MiB (1%) 35034
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "polygon1"] 30.181 ms (5%) 51.22 MiB (1%) 35424
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "polygon3"] 34.761 ms (5%) 73.41 MiB (1%) 71003
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "robotarm"] 25.698 ms (5%) 53.72 MiB (1%) 47390
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "structural"] 3.774 ms (5%) 15.03 MiB (1%) 73038
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "camshape"] 547.174 ms (5%) 35.551 ms 323.29 MiB (1%) 1610492
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "catenary"] 111.595 ms (5%) 57.70 MiB (1%) 680133
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "chain"] 137.887 ms (5%) 67.93 MiB (1%) 920520
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "channel"] 689.977 ms (5%) 86.710 ms 284.62 MiB (1%) 3961188
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "clnlbeam"] 138.457 ms (5%) 68.43 MiB (1%) 792422
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "controlinvestment"] 120.151 ms (5%) 71.36 MiB (1%) 807944
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "elec"] 548.651 s (5%) 2.775 s 36.64 GiB (1%) 104920002
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "hovercraft1d"] 11.864 ms (5%) 11.17 MiB (1%) 103117
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "marine"] 89.852 ms (5%) 45.67 MiB (1%) 498241
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "polygon1"] 52.327 ms (5%) 33.24 MiB (1%) 357363
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "polygon3"] 226.951 ms (5%) 6.483 ms 145.26 MiB (1%) 970123
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "robotarm"] 172.400 ms (5%) 6.863 ms 101.54 MiB (1%) 840127
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "structural"] 138.300 ms (5%) 2.620 ms 151.56 MiB (1%) 442523

Benchmark Group List

Here's a list of all the benchmark groups executed by this job:

  • ["hess_coord", "optimized", "Float64", "scalable_cons", "sparse"]
  • ["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse"]
  • ["hessian_backend", "optimized", "Float64", "scalable", "sparse"]
  • ["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics"]

Julia versioninfo

Julia Version 1.10.4
Commit 48d4fd48430 (2024-06-04 10:41 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
      Ubuntu 22.04.4 LTS
  uname: Linux 6.5.0-1023-azure #24~22.04.1-Ubuntu SMP Wed Jun 12 19:55:26 UTC 2024 x86_64 x86_64
  CPU: AMD EPYC 7763 64-Core Processor: 
              speed         user         nice          sys         idle          irq
       #1  3215 MHz      19338 s          0 s        823 s      56109 s          0 s
       #2  3242 MHz      26127 s          0 s        864 s      49307 s          0 s
       #3  2445 MHz      20487 s          0 s        824 s      54989 s          0 s
       #4  2445 MHz      19943 s          0 s        946 s      55408 s          0 s
  Memory: 15.606491088867188 GB (11977.90625 MB free)
  Uptime: 7642.25 sec
  Load Avg:  1.01  1.05  1.0
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)
Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)

Baseline result

Benchmark Report for /home/runner/work/ADNLPModels.jl/ADNLPModels.jl

Job Properties

  • Time of benchmark: 18 Jul 2024 - 20:18
  • Package commit: 1650fc
  • Julia commit: 48d4fd
  • Julia command flags: None
  • Environment variables: None

Results

Below is a table of this job's results, obtained by running the benchmarks.
The values listed in the ID column have the structure [parent_group, child_group, ..., key], and can be used to
index into the BaseBenchmarks suite to retrieve the corresponding benchmarks.
The percentages accompanying time and memory values in the below table are noise tolerances. The "true"
time/memory value for a given benchmark is expected to fall within this percentage of the reported value.
An empty cell means that the value was zero.

ID time GC time memory allocations
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "camshape"] 406.798 ms (5%) 1.63 MiB (1%) 12350
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "catenary"] 11.196 ms (5%) 369.45 KiB (1%) 2390
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "chain"] 21.580 ms (5%) 283.58 KiB (1%) 1931
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "channel"] 522.487 ms (5%) 1.12 MiB (1%) 7710
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "clnlbeam"] 13.526 ms (5%) 124.95 KiB (1%) 800
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "controlinvestment"] 18.626 ms (5%) 145.08 KiB (1%) 968
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "elec"] 911.072 s (5%) 12.959 ms 232.44 MiB (1%) 1905099
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "hovercraft1d"] 1.189 ms (5%) 5.57 MiB (1%) 386
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "marine"] 398.335 ms (5%) 9.664 ms 193.42 MiB (1%) 1027996
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "polygon1"] 27.746 ms (5%) 334.39 KiB (1%) 1923
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "polygon3"] 96.869 ms (5%) 829.52 KiB (1%) 5873
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "robotarm"] 12.706 s (5%) 5.162 ms 132.42 MiB (1%) 981023
["hess_coord", "optimized", "Float64", "scalable_cons", "sparse", "structural"] 13.970 ms (5%) 96.47 KiB (1%) 854
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "arglina"] 49.319 ms (5%) 332.41 KiB (1%) 2465
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "arglinb"] 41.487 ms (5%) 332.41 KiB (1%) 2465
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "bdqrtic"] 73.191 ms (5%) 339.52 KiB (1%) 2459
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "brownal"] 16.365 s (5%) 15.377 ms 201.87 MiB (1%) 1467003
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "broyden3d"] 24.473 ms (5%) 213.19 KiB (1%) 1469
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "brybnd"] 77.795 ms (5%) 213.19 KiB (1%) 1469
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "dixon3dq"] 8.816 ms (5%) 205.31 KiB (1%) 1469
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "errinros_mod"] 64.004 ms (5%) 340.28 KiB (1%) 2465
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "freuroth"] 73.873 ms (5%) 340.28 KiB (1%) 2465
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "hovercraft1d"] 1.689 ms (5%) 119.56 KiB (1%) 797
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "integreq"] 17.112 s (5%) 213.19 KiB (1%) 1469
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "morebv"] 35.948 ms (5%) 213.19 KiB (1%) 1469
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "penalty1"] 18.549 ms (5%) 213.19 KiB (1%) 1469
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "power"] 2.663 ms (5%) 85.33 KiB (1%) 467
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "sbrybnd"] 70.702 ms (5%) 213.19 KiB (1%) 1469
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "spmsrtls"] 440.211 ms (5%) 2.20 MiB (1%) 16761
["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse", "tquartic"] 11.655 ms (5%) 212.42 KiB (1%) 1463
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "camshape"] 40.859 ms (5%) 87.82 MiB (1%) 111211
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "catenary"] 11.273 ms (5%) 22.47 MiB (1%) 32075
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "chain"] 6.909 ms (5%) 18.17 MiB (1%) 45581
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "channel"] 99.772 ms (5%) 2.857 ms 142.72 MiB (1%) 140799
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "clnlbeam"] 5.239 ms (5%) 13.29 MiB (1%) 42875
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "controlinvestment"] 11.768 ms (5%) 26.58 MiB (1%) 60823
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "elec"] 12.110 s (5%) 182.396 ms 14.97 GiB (1%) 5390328
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "hovercraft1d"] 1.588 ms (5%) 4.05 MiB (1%) 13089
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "marine"] 4.449 ms (5%) 9.94 MiB (1%) 34967
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "polygon1"] 29.801 ms (5%) 50.91 MiB (1%) 35388
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "polygon3"] 34.798 ms (5%) 73.13 MiB (1%) 70969
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "robotarm"] 26.154 ms (5%) 53.71 MiB (1%) 48685
["hessian_backend", "optimized", "Float64", "scalable", "sparse", "structural"] 3.816 ms (5%) 15.03 MiB (1%) 73032
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "camshape"] 542.165 ms (5%) 34.396 ms 323.10 MiB (1%) 1610465
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "catenary"] 110.457 ms (5%) 57.41 MiB (1%) 680094
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "chain"] 138.332 ms (5%) 67.88 MiB (1%) 920495
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "channel"] 698.728 ms (5%) 283.78 MiB (1%) 3961138
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "clnlbeam"] 137.430 ms (5%) 68.33 MiB (1%) 791410
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "controlinvestment"] 121.339 ms (5%) 71.31 MiB (1%) 807922
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "elec"] 535.000 s (5%) 3.631 s 36.56 GiB (1%) 104916861
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "hovercraft1d"] 36.845 ms (5%) 23.061 ms 11.15 MiB (1%) 103101
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "marine"] 89.773 ms (5%) 45.46 MiB (1%) 498175
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "polygon1"] 52.023 ms (5%) 32.84 MiB (1%) 355827
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "polygon3"] 212.421 ms (5%) 144.98 MiB (1%) 970089
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "robotarm"] 172.096 ms (5%) 6.846 ms 101.53 MiB (1%) 841422
["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics", "structural"] 142.833 ms (5%) 2.827 ms 151.56 MiB (1%) 442517

Benchmark Group List

Here's a list of all the benchmark groups executed by this job:

  • ["hess_coord", "optimized", "Float64", "scalable_cons", "sparse"]
  • ["hess_coord_residual", "optimized", "Float64", "scalable_nls", "sparse"]
  • ["hessian_backend", "optimized", "Float64", "scalable", "sparse"]
  • ["hessian_backend", "optimized", "Float64", "scalable", "sparse_symbolics"]

Julia versioninfo

Julia Version 1.10.4
Commit 48d4fd48430 (2024-06-04 10:41 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
      Ubuntu 22.04.4 LTS
  uname: Linux 6.5.0-1023-azure #24~22.04.1-Ubuntu SMP Wed Jun 12 19:55:26 UTC 2024 x86_64 x86_64
  CPU: AMD EPYC 7763 64-Core Processor: 
              speed         user         nice          sys         idle          irq
       #1  3242 MHz      28742 s          0 s       1064 s      81005 s          0 s
       #2  3253 MHz      36320 s          0 s       1188 s      73340 s          0 s
       #3  2445 MHz      27619 s          0 s       1110 s      82116 s          0 s
       #4  3239 MHz      27814 s          0 s       1288 s      81740 s          0 s
  Memory: 15.606491088867188 GB (12187.77734375 MB free)
  Uptime: 11102.05 sec
  Load Avg:  1.09  1.05  1.01
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)
Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)

Runtime information

Runtime Info
BLAS #threads 2
BLAS.vendor() lbt
Sys.CPU_THREADS 4

lscpu output:

Architecture:                       x86_64
CPU op-mode(s):                     32-bit, 64-bit
Address sizes:                      48 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             4
On-line CPU(s) list:                0-3
Vendor ID:                          AuthenticAMD
Model name:                         AMD EPYC 7763 64-Core Processor
CPU family:                         25
Model:                              1
Thread(s) per core:                 2
Core(s) per socket:                 2
Socket(s):                          1
Stepping:                           1
BogoMIPS:                           4890.85
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl tsc_reliable nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy svm cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext invpcid_single vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves clzero xsaveerptr rdpru arat npt nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload umip vaes vpclmulqdq rdpid fsrm
Virtualization:                     AMD-V
Hypervisor vendor:                  Microsoft
Virtualization type:                full
L1d cache:                          64 KiB (2 instances)
L1i cache:                          64 KiB (2 instances)
L2 cache:                           1 MiB (2 instances)
L3 cache:                           32 MiB (1 instance)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0-3
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Not affected
Vulnerability Retbleed:             Not affected
Vulnerability Spec rstack overflow: Vulnerable: Safe RET, no microcode
Vulnerability Spec store bypass:    Vulnerable
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Retpolines; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected
Cpu Property Value
Brand AMD EPYC 7763 64-Core Processor
Vendor :AMD
Architecture :Unknown
Model Family: 0xaf, Model: 0x01, Stepping: 0x01, Type: 0x00
Cores 16 physical cores, 16 logical cores (on executing CPU)
No Hyperthreading hardware capability detected
Clock Frequencies Not supported by CPU
Data Cache Level 1:3 : (32, 512, 32768) kbytes
64 byte cache line size
Address Size 48 bits virtual, 48 bits physical
SIMD 256 bit = 32 byte max. SIMD vector size
Time Stamp Counter TSC is accessible via rdtsc
TSC runs at constant rate (invariant from clock frequency)
Perf. Monitoring Performance Monitoring Counters (PMC) are not supported
Hypervisor Yes, Microsoft

@amontoison amontoison merged commit 55ec5c9 into main Jul 18, 2024
43 of 45 checks passed
@amontoison amontoison deleted the symmetric_coloring branch July 18, 2024 20:28
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Symmetric coloring for efficient sparse hessian computation
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@amontoison @gdalle