AMD Ryzen 9 3900X 12-Core testing with a MSI X570-A PRO (MS-7C37) v3.0 (H.70 BIOS) and NVIDIA GeForce RTX 3060 on Ubuntu 24.04 via the Phoronix Test Suite. Noble python 3.12 performance vs. python compiled without frame pointers.
Compare your own system(s) to this result file with the
Phoronix Test Suite by running the command:
phoronix-test-suite benchmark 2405056-VPA1-MERGE7223
{
"title": "SCIKIT-leaRn tests",
"last_modified": "2024-05-06 03:17:11",
"description": "AMD Ryzen 9 3900X 12-Core testing with a MSI X570-A PRO (MS-7C37) v3.0 (H.70 BIOS) and NVIDIA GeForce RTX 3060 on Ubuntu 24.04 via the Phoronix Test Suite. Noble python 3.12 performance vs. python compiled without frame pointers.",
"systems": {
"noble": {
"identifier": "noble",
"hardware": {
"Processor": "AMD Ryzen 9 3900X 12-Core @ 3.80GHz (12 Cores \/ 24 Threads)",
"Motherboard": "MSI X570-A PRO (MS-7C37) v3.0 (H.70 BIOS)",
"Chipset": "AMD Starship\/Matisse",
"Memory": "2 x 16GB DDR4-3200MT\/s F4-3200C16-16GVK",
"Disk": "2000GB Seagate ST2000DM006-2DM1 + 2000GB Western Digital WD20EZAZ-00G + 500GB Samsung SSD 860 + 8002GB Seagate ST8000DM004-2CX1 + 1000GB CT1000BX500SSD1 + 512GB TS512GESD310C",
"Graphics": "NVIDIA GeForce RTX 3060",
"Audio": "NVIDIA GA104 HD Audio",
"Monitor": "DELL P2314H + U32J59x",
"Network": "Realtek RTL8111\/8168\/8211\/8411"
},
"software": {
"OS": "Ubuntu 24.04",
"Kernel": "6.8.0-31-generic (x86_64)",
"Compiler": "GCC 13.2.0",
"File-System": "ext4",
"Screen Resolution": "1920x1080"
},
"user": "ubuntu",
"timestamp": "2024-05-02 06:45:06",
"client_version": "10.8.4",
"data": {
"compiler-configuration": "--build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-backtrace --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-defaulted --enable-offload-targets=nvptx-none=\/build\/gcc-13-uJ7kn6\/gcc-13-13.2.0\/debian\/tmp-nvptx\/usr,amdgcn-amdhsa=\/build\/gcc-13-uJ7kn6\/gcc-13-13.2.0\/debian\/tmp-gcn\/usr --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --program-prefix=x86_64-linux-gnu- --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -v",
"cpu-scaling-governor": "acpi-cpufreq schedutil (Boost: Enabled)",
"cpu-microcode": "0x8701013",
"kernel-extra-details": "Transparent Huge Pages: madvise",
"python": "Python 3.12.3",
"security": "gather_data_sampling: Not affected + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + reg_file_data_sampling: Not affected + retbleed: Mitigation of untrained return thunk; SMT enabled with STIBP protection + spec_rstack_overflow: Mitigation of Safe RET + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy\/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Retpolines; IBPB: conditional; STIBP: always-on; RSB filling; PBRSB-eIBRS: Not affected; BHI: Not affected + srbds: Not affected + tsx_async_abort: Not affected"
}
},
"scikit-learn-python-disabled-fp": {
"identifier": "scikit-learn-python-disabled-fp",
"hardware": {
"Processor": "AMD Ryzen 9 3900X 12-Core @ 3.80GHz (12 Cores \/ 24 Threads)",
"Motherboard": "MSI X570-A PRO (MS-7C37) v3.0 (H.70 BIOS)",
"Chipset": "AMD Starship\/Matisse",
"Memory": "2 x 16GB DDR4-3200MT\/s F4-3200C16-16GVK",
"Disk": "2000GB Seagate ST2000DM006-2DM1 + 2000GB Western Digital WD20EZAZ-00G + 500GB Samsung SSD 860 + 8002GB Seagate ST8000DM004-2CX1 + 1000GB CT1000BX500SSD1 + 512GB TS512GESD310C",
"Graphics": "NVIDIA GeForce RTX 3060",
"Audio": "NVIDIA GA104 HD Audio",
"Monitor": "DELL P2314H + U32J59x",
"Network": "Realtek RTL8111\/8168\/8211\/8411"
},
"software": {
"OS": "Ubuntu 24.04",
"Kernel": "6.8.0-31-generic (x86_64)",
"Compiler": "GCC 13.2.0",
"File-System": "ext4",
"Screen Resolution": "1920x1080"
},
"user": "ubuntu",
"timestamp": "2024-05-03 07:42:30",
"client_version": "10.8.4",
"data": {
"compiler-configuration": "--build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-backtrace --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-defaulted --enable-offload-targets=nvptx-none=\/build\/gcc-13-S2PGXz\/gcc-13-13.2.0\/debian\/tmp-nvptx\/usr,amdgcn-amdhsa=\/build\/gcc-13-S2PGXz\/gcc-13-13.2.0\/debian\/tmp-gcn\/usr --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --program-prefix=x86_64-linux-gnu- --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -v",
"cpu-scaling-governor": "acpi-cpufreq schedutil (Boost: Enabled)",
"cpu-microcode": "0x8701013",
"kernel-extra-details": "Transparent Huge Pages: madvise",
"python": "Python 3.12.3",
"security": "gather_data_sampling: Not affected + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + reg_file_data_sampling: Not affected + retbleed: Mitigation of untrained return thunk; SMT enabled with STIBP protection + spec_rstack_overflow: Mitigation of Safe RET + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy\/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Retpolines; IBPB: conditional; STIBP: always-on; RSB filling; PBRSB-eIBRS: Not affected; BHI: Not affected + srbds: Not affected + tsx_async_abort: Not affected"
}
}
},
"results": {
"16c9bda5dcaa26720a451b55ae6dbb85b11db65e": {
"identifier": "pts\/scikit-learn-2.0.0",
"title": "Scikit-Learn",
"app_version": "1.2.2",
"arguments": "tree.py",
"description": "Benchmark: Tree",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"noble": {
"value": 48.3250000000000028421709430404007434844970703125,
"raw_values": [
47.54899999999999948840923025272786617279052734375,
45.96300000000000096633812063373625278472900390625,
50.844999999999998863131622783839702606201171875,
47.08500000000000085265128291212022304534912109375,
48.57300000000000039790393202565610408782958984375,
47.75800000000000267164068645797669887542724609375,
46.03699999999999903366187936626374721527099609375,
54.05199999999999960209606797434389591217041015625,
47.61500000000000198951966012828052043914794921875,
47.56099999999999994315658113919198513031005859375,
47.046999999999997044142219237983226776123046875,
48.99799999999999755573298898525536060333251953125,
47.356999999999999317878973670303821563720703125,
49.332999999999998408384271897375583648681640625,
49.09899999999999664623828721232712268829345703125
],
"test_run_times": [
48.46000000000000085265128291212022304534912109375,
47.5499999999999971578290569595992565155029296875,
45.96000000000000085265128291212022304534912109375,
50.85000000000000142108547152020037174224853515625,
47.090000000000003410605131648480892181396484375,
48.57000000000000028421709430404007434844970703125,
47.75999999999999801048033987171947956085205078125,
46.03999999999999914734871708787977695465087890625,
54.0499999999999971578290569595992565155029296875,
47.6099999999999994315658113919198513031005859375,
47.56000000000000227373675443232059478759765625,
47.0499999999999971578290569595992565155029296875,
49,
47.3599999999999994315658113919198513031005859375,
49.3299999999999982946974341757595539093017578125,
49.10000000000000142108547152020037174224853515625
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
},
"scikit-learn-python-disabled-fp": {
"value": 46.2950000000000017053025658242404460906982421875,
"raw_values": [
46.316000000000002501110429875552654266357421875,
45.1520000000000010231815394945442676544189453125,
47.41799999999999926103555480949580669403076171875
],
"test_run_times": [
44.61999999999999744204615126363933086395263671875,
46.32000000000000028421709430404007434844970703125,
45.14999999999999857891452847979962825775146484375,
47.4200000000000017053025658242404460906982421875
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
}
}
},
"8c35c7c4bdfd26e3a04c4d2164deef38139363f3": {
"identifier": "pts\/scikit-learn-2.0.0",
"title": "Scikit-Learn",
"app_version": "1.2.2",
"arguments": "sample_without_replacement.py",
"description": "Benchmark: Sample Without Replacement",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"noble": {
"value": 180.104999999999989768184605054557323455810546875,
"raw_values": [
182.13200000000000500222085975110530853271484375,
176.17099999999999226929503493010997772216796875,
182.013000000000005229594535194337368011474609375
],
"test_run_times": [
182.789999999999992041921359486877918243408203125,
182.1299999999999954525264911353588104248046875,
176.169999999999987494447850622236728668212890625,
182.009999999999990905052982270717620849609375
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
},
"scikit-learn-python-disabled-fp": {
"value": 172.818999999999988403942552395164966583251953125,
"raw_values": [
172.0620000000000118234311230480670928955078125,
177.22100000000000363797880709171295166015625,
169.174000000000006593836587853729724884033203125
],
"test_run_times": [
177,
172.06000000000000227373675443232059478759765625,
177.219999999999998863131622783839702606201171875,
169.169999999999987494447850622236728668212890625
]
}
}
},
"705767c965e514206b035fd8cdf7a8c852ccd8ad": {
"identifier": "pts\/scikit-learn-2.0.0",
"title": "Scikit-Learn",
"app_version": "1.2.2",
"arguments": "text_vectorizers.py",
"description": "Benchmark: Text Vectorizers",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"noble": {
"value": 65.7399999999999948840923025272786617279052734375,
"raw_values": [
65.75,
65.804000000000002046363078989088535308837890625,
65.66599999999999681676854379475116729736328125
],
"test_run_times": [
85.31000000000000227373675443232059478759765625,
65.75,
65.7999999999999971578290569595992565155029296875,
65.6700000000000017053025658242404460906982421875
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
},
"scikit-learn-python-disabled-fp": {
"value": 63.47999999999999687361196265555918216705322265625,
"raw_values": [
64.0810000000000030695446184836328029632568359375,
63.469999999999998863131622783839702606201171875,
62.8900000000000005684341886080801486968994140625
],
"test_run_times": [
64.0199999999999960209606797434389591217041015625,
64.0799999999999982946974341757595539093017578125,
63.469999999999998863131622783839702606201171875,
62.8900000000000005684341886080801486968994140625
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
}
}
},
"e2cc341c3a96531e49e70b55587177beca99efa7": {
"identifier": "pts\/scikit-learn-2.0.0",
"title": "Scikit-Learn",
"app_version": "1.2.2",
"arguments": "isotonic.py --iterations 100 --log_min_problem_size 1 --log_max_problem_size 10 --dataset perturbed_logarithm",
"description": "Benchmark: Isotonic \/ Perturbed Logarithm",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"noble": {
"value": 1787.711000000000012732925824820995330810546875,
"raw_values": [
1784.93499999999994543031789362430572509765625,
1785.680000000000063664629124104976654052734375,
1792.517000000000052750692702829837799072265625
],
"test_run_times": [
1775.84999999999990905052982270717620849609375,
1784.94000000000005456968210637569427490234375,
1785.680000000000063664629124104976654052734375,
1792.51999999999998181010596454143524169921875
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
},
"scikit-learn-python-disabled-fp": {
"value": 1824.2989999999999781721271574497222900390625,
"raw_values": [
1837.265000000000100044417195022106170654296875,
1835.32799999999997453414835035800933837890625,
1800.303000000000110958353616297245025634765625
],
"test_run_times": [
1787.59999999999990905052982270717620849609375,
1837.26999999999998181010596454143524169921875,
1835.329999999999927240423858165740966796875,
1800.299999999999954525264911353588104248046875
]
}
}
},
"4b3cfb0ef799f37cb8cb63927c2a6bc40ed38103": {
"identifier": "pts\/scikit-learn-2.0.0",
"title": "Scikit-Learn",
"app_version": "1.2.2",
"arguments": "tsne_mnist.py",
"description": "Benchmark: TSNE MNIST Dataset",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"noble": {
"value": 259.75799999999998135535861365497112274169921875,
"raw_values": [
261.2010000000000218278728425502777099609375,
257.384000000000014551915228366851806640625,
260.68900000000002137312549166381359100341796875
],
"test_run_times": [
252.81000000000000227373675443232059478759765625,
261.19999999999998863131622783839702606201171875,
257.3799999999999954525264911353588104248046875,
260.68999999999999772626324556767940521240234375
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
},
"scikit-learn-python-disabled-fp": {
"value": 254.864000000000004320099833421409130096435546875,
"raw_values": [
253.914999999999992041921359486877918243408203125,
254.2160000000000081854523159563541412353515625,
256.461000000000012732925824820995330810546875
],
"test_run_times": [
254.159999999999996589394868351519107818603515625,
253.909999999999996589394868351519107818603515625,
254.219999999999998863131622783839702606201171875,
256.45999999999997953636921010911464691162109375
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
}
}
},
"c4d0b1f8172b8730a5ad8adc48e5b56069013698": {
"identifier": "pts\/scikit-learn-2.0.0",
"title": "Scikit-Learn",
"app_version": "1.2.2",
"arguments": "plot_polynomial_kernel_approximation.py",
"description": "Benchmark: Plot Polynomial Kernel Approximation",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"noble": {
"value": 155.493999999999999772626324556767940521240234375,
"raw_values": [
155.79099999999999681676854379475116729736328125,
153.7549999999999954525264911353588104248046875,
156.93500000000000227373675443232059478759765625
],
"test_run_times": [
153.919999999999987494447850622236728668212890625,
155.789999999999992041921359486877918243408203125,
153.759999999999990905052982270717620849609375,
156.93000000000000682121026329696178436279296875
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
},
"scikit-learn-python-disabled-fp": {
"value": 152.597000000000008412825991399586200714111328125,
"raw_values": [
153.36299999999999954525264911353588104248046875,
152.270000000000010231815394945442676544189453125,
152.1589999999999918145476840436458587646484375
],
"test_run_times": [
153.080000000000012505552149377763271331787109375,
153.3600000000000136424205265939235687255859375,
152.270000000000010231815394945442676544189453125,
152.159999999999996589394868351519107818603515625
]
}
}
},
"90318f2b9e77d5e89dc8096ecff5d801d4cce2ad": {
"identifier": "pts\/scikit-learn-2.0.0",
"title": "Scikit-Learn",
"app_version": "1.2.2",
"arguments": "random_projections.py --n-times 100",
"description": "Benchmark: Sparse Random Projections \/ 100 Iterations",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"noble": {
"value": 557.9850000000000136424205265939235687255859375,
"raw_values": [
561.413999999999987267074175179004669189453125,
552.3260000000000218278728425502777099609375,
560.2150000000000318323145620524883270263671875
],
"test_run_times": [
552.759999999999990905052982270717620849609375,
561.4099999999999681676854379475116729736328125,
552.3300000000000409272615797817707061767578125,
560.220000000000027284841053187847137451171875
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
},
"scikit-learn-python-disabled-fp": {
"value": 566.9510000000000218278728425502777099609375,
"raw_values": [
576.3990000000000009094947017729282379150390625,
552.24800000000004729372449219226837158203125,
572.2060000000000172803993336856365203857421875
],
"test_run_times": [
559.1499999999999772626324556767940521240234375,
576.3999999999999772626324556767940521240234375,
552.25,
572.2100000000000363797880709171295166015625
]
}
}
},
"5132288cf1546a4f77948654d5ae0826dbd35ac3": {
"identifier": "pts\/scikit-learn-2.0.0",
"title": "Scikit-Learn",
"app_version": "1.2.2",
"arguments": "kernel_pca_solvers_time_vs_n_components.py",
"description": "Benchmark: Kernel PCA Solvers \/ Time vs. N Components",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"noble": {
"value": 70.230999999999994543031789362430572509765625,
"raw_values": [
72.03600000000000136424205265939235687255859375,
72.0139999999999957935870043002068996429443359375,
67.9740000000000037516656448133289813995361328125,
68.35800000000000409272615797817707061767578125,
70.7710000000000007958078640513122081756591796875,
69.429000000000002046363078989088535308837890625,
72.1029999999999944293449516408145427703857421875,
69.16100000000000136424205265939235687255859375
],
"test_run_times": [
71.2600000000000051159076974727213382720947265625,
72.0400000000000062527760746888816356658935546875,
72.0100000000000051159076974727213382720947265625,
67.969999999999998863131622783839702606201171875,
68.3599999999999994315658113919198513031005859375,
70.7699999999999960209606797434389591217041015625,
69.43000000000000682121026329696178436279296875,
72.099999999999994315658113919198513031005859375,
69.159999999999996589394868351519107818603515625
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
},
"scikit-learn-python-disabled-fp": {
"value": 69.1590000000000060254023992456495761871337890625,
"raw_values": [
69.118999999999999772626324556767940521240234375,
68.59600000000000363797880709171295166015625,
69.76200000000000045474735088646411895751953125
],
"test_run_times": [
69.93999999999999772626324556767940521240234375,
69.1200000000000045474735088646411895751953125,
68.599999999999994315658113919198513031005859375,
69.7600000000000051159076974727213382720947265625
]
}
}
},
"678e1b1938d0d397b89687152fb474d6f101f050": {
"identifier": "pts\/scikit-learn-2.0.0",
"title": "Scikit-Learn",
"app_version": "1.2.2",
"arguments": "hist_gradient_boosting.py",
"description": "Benchmark: Hist Gradient Boosting",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"noble": {
"value": 117.08899999999999863575794734060764312744140625,
"raw_values": [
117.6820000000000021600499167107045650482177734375,
117.2420000000000044337866711430251598358154296875,
116.3419999999999987494447850622236728668212890625
],
"test_run_times": [
117.5799999999999982946974341757595539093017578125,
117.68000000000000682121026329696178436279296875,
117.2399999999999948840923025272786617279052734375,
116.340000000000003410605131648480892181396484375
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
},
"scikit-learn-python-disabled-fp": {
"value": 115.3900000000000005684341886080801486968994140625,
"raw_values": [
115.2099999999999937472239253111183643341064453125,
115.7699999999999960209606797434389591217041015625,
115.18999999999999772626324556767940521240234375
],
"test_run_times": [
115.75,
115.2099999999999937472239253111183643341064453125,
115.7699999999999960209606797434389591217041015625,
115.18999999999999772626324556767940521240234375
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
}
}
},
"6737564751666cb71e46d7d87975cec3a3916bc2": {
"identifier": "pts\/scikit-learn-2.0.0",
"title": "Scikit-Learn",
"app_version": "1.2.2",
"arguments": "plot_hierarchical.py",
"description": "Benchmark: Plot Hierarchical",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"noble": {
"value": 207.115000000000009094947017729282379150390625,
"raw_values": [
209.77600000000001045918907038867473602294921875,
203.62299999999999045030563138425350189208984375,
207.94499999999999317878973670303821563720703125
],
"test_run_times": [
203.44999999999998863131622783839702606201171875,
209.780000000000001136868377216160297393798828125,
203.6200000000000045474735088646411895751953125,
207.93999999999999772626324556767940521240234375
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
},
"scikit-learn-python-disabled-fp": {
"value": 204.131000000000000227373675443232059478759765625,
"raw_values": [
205.8319999999999936335370875895023345947265625,
200.967000000000012960299500264227390289306640625,
205.594999999999998863131622783839702606201171875
],
"test_run_times": [
206.8700000000000045474735088646411895751953125,
205.830000000000012505552149377763271331787109375,
200.969999999999998863131622783839702606201171875,
205.599999999999994315658113919198513031005859375
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
}
}
},
"c11a1bc7d1139f46a28f339b15bd1556fec01524": {
"identifier": "pts\/scikit-learn-2.0.0",
"title": "Scikit-Learn",
"app_version": "1.2.2",
"arguments": "hist_gradient_boosting_categorical_only.py",
"description": "Benchmark: Hist Gradient Boosting Categorical Only",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"noble": {
"value": 20.053000000000000824229573481716215610504150390625,
"raw_values": [
19.836999999999999744204615126363933086395263671875,
20.2349999999999994315658113919198513031005859375,
20.08800000000000096633812063373625278472900390625
],
"test_run_times": [
20,
19.839999999999999857891452847979962825775146484375,
20.230000000000000426325641456060111522674560546875,
20.089999999999999857891452847979962825775146484375
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
},
"scikit-learn-python-disabled-fp": {
"value": 19.79599999999999937472239253111183643341064453125,
"raw_values": [
19.721000000000000085265128291212022304534912109375,
19.96600000000000108002495835535228252410888671875,
19.70100000000000051159076974727213382720947265625
],
"test_run_times": [
19.760000000000001563194018672220408916473388671875,
19.719999999999998863131622783839702606201171875,
19.969999999999998863131622783839702606201171875,
19.699999999999999289457264239899814128875732421875
]
}
}
},
"fa128afc6938368e38625914c73e86b381abc2cb": {
"identifier": "pts\/scikit-learn-2.0.0",
"title": "Scikit-Learn",
"app_version": "1.2.2",
"arguments": "hist_gradient_boosting_adult.py",
"description": "Benchmark: Hist Gradient Boosting Adult",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"noble": {
"value": 111.8299999999999982946974341757595539093017578125,
"raw_values": [
111.55500000000000682121026329696178436279296875,
112.0079999999999955662133288569748401641845703125,
111.9260000000000019326762412674725055694580078125
],
"test_run_times": [
121.56000000000000227373675443232059478759765625,
111.5499999999999971578290569595992565155029296875,
112.0100000000000051159076974727213382720947265625,
111.93000000000000682121026329696178436279296875
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
},
"scikit-learn-python-disabled-fp": {
"value": 110.3990000000000009094947017729282379150390625,
"raw_values": [
109.6940000000000026147972675971686840057373046875,
111.08400000000000318323145620524883270263671875,
110.4189999999999969304553815163671970367431640625
],
"test_run_times": [
110.469999999999998863131622783839702606201171875,
109.68999999999999772626324556767940521240234375,
111.0799999999999982946974341757595539093017578125,
110.4200000000000017053025658242404460906982421875
]
}
}
},
"25540795787dee5964af5bc291deddcfed0eb726": {
"identifier": "pts\/scikit-learn-2.0.0",
"title": "Scikit-Learn",
"app_version": "1.2.2",
"arguments": "isolation_forest.py",
"description": "Benchmark: Isolation Forest",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"noble": {
"value": 300.66300000000001091393642127513885498046875,
"raw_values": [
297.73799999999999954525264911353588104248046875,
305.25799999999998135535861365497112274169921875,
298.99299999999999499777914024889469146728515625
],
"test_run_times": [
381.06999999999999317878973670303821563720703125,
297.740000000000009094947017729282379150390625,
305.259999999999990905052982270717620849609375,
298.990000000000009094947017729282379150390625
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
},
"scikit-learn-python-disabled-fp": {
"value": 304.15600000000000591171556152403354644775390625,
"raw_values": [
309.7640000000000100044417195022106170654296875,
297.4850000000000136424205265939235687255859375,
305.21899999999999408828443847596645355224609375
],
"test_run_times": [
309.67000000000001591615728102624416351318359375,
309.759999999999990905052982270717620849609375,
297.48000000000001818989403545856475830078125,
305.220000000000027284841053187847137451171875
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
}
}
},
"f5d09824156b88af55d717e9eb483325064ed957": {
"identifier": "pts\/scikit-learn-2.0.0",
"title": "Scikit-Learn",
"app_version": "1.2.2",
"arguments": "saga.py",
"description": "Benchmark: SAGA",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"noble": {
"value": 873.2569999999999481588019989430904388427734375,
"raw_values": [
887.51999999999998181010596454143524169921875,
843.3819999999999481588019989430904388427734375,
884.5629999999999881765688769519329071044921875,
877.5620000000000118234311230480670928955078125
],
"test_run_times": [
1095.829999999999927240423858165740966796875,
887.51999999999998181010596454143524169921875,
843.3799999999999954525264911353588104248046875,
884.55999999999994543031789362430572509765625,
877.55999999999994543031789362430572509765625
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
},
"scikit-learn-python-disabled-fp": {
"value": 863.60500000000001818989403545856475830078125,
"raw_values": [
854.076999999999998181010596454143524169921875,
886.3600000000000136424205265939235687255859375,
850.37699999999995270627550780773162841796875
],
"test_run_times": [
839.6599999999999681676854379475116729736328125,
854.0800000000000409272615797817707061767578125,
886.3600000000000136424205265939235687255859375,
850.3799999999999954525264911353588104248046875
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
}
}
},
"f04e19c1e82d387fcfde8752afae91e6a63032ad": {
"identifier": "pts\/scikit-learn-2.0.0",
"title": "Scikit-Learn",
"app_version": "1.2.2",
"arguments": "glm.py",
"description": "Benchmark: GLM",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"noble": {
"value": 282.67599999999998772182152606546878814697265625,
"raw_values": [
285.03600000000000136424205265939235687255859375,
283.52600000000001045918907038867473602294921875,
268.5810000000000172803993336856365203857421875,
287.80500000000000682121026329696178436279296875,
286.37700000000000954969436861574649810791015625,
276.0249999999999772626324556767940521240234375,
286.77899999999999636202119290828704833984375,
287.278000000000020008883439004421234130859375
],
"test_run_times": [
283.779999999999972715158946812152862548828125,
285.04000000000002046363078989088535308837890625,
283.529999999999972715158946812152862548828125,
268.57999999999998408384271897375583648681640625,
287.80000000000001136868377216160297393798828125,
286.3799999999999954525264911353588104248046875,
276.01999999999998181010596454143524169921875,
286.779999999999972715158946812152862548828125,
287.279999999999972715158946812152862548828125
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
},
"scikit-learn-python-disabled-fp": {
"value": 279.63600000000002410160959698259830474853515625,
"raw_values": [
282.69099999999997407940099947154521942138671875,
271.80599999999998317434801720082759857177734375,
284.41000000000002501110429875552654266357421875
],
"test_run_times": [
279.93999999999999772626324556767940521240234375,
282.68999999999999772626324556767940521240234375,
271.81000000000000227373675443232059478759765625,
284.41000000000002501110429875552654266357421875
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
}
}
},
"4036585c02ca252b6042d1b18fd9d7eb67c3517c": {
"identifier": "pts\/scikit-learn-2.0.0",
"title": "Scikit-Learn",
"app_version": "1.2.2",
"arguments": "isotonic.py --iterations 100 --log_min_problem_size 1 --log_max_problem_size 10 --dataset logistic",
"description": "Benchmark: Isotonic \/ Logistic",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"noble": {
"value": 1435.272999999999910869519226253032684326171875,
"raw_values": [
1439.8620000000000800355337560176849365234375,
1436.720000000000027284841053187847137451171875,
1429.238000000000056388671509921550750732421875
],
"test_run_times": [
1442.359999999999899955582804977893829345703125,
1439.859999999999899955582804977893829345703125,
1436.720000000000027284841053187847137451171875,
1429.240000000000009094947017729282379150390625
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
},
"scikit-learn-python-disabled-fp": {
"value": 1420.39100000000007639755494892597198486328125,
"raw_values": [
1451.691000000000030922819860279560089111328125,
1408.07400000000006912159733474254608154296875,
1401.406999999999925421434454619884490966796875
],
"test_run_times": [
1427.299999999999954525264911353588104248046875,
1451.69000000000005456968210637569427490234375,
1408.069999999999936335370875895023345947265625,
1401.410000000000081854523159563541412353515625
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
}
}
},
"d4099e414c3a1ffd705ea3918d8a5e0c9490d9a3": {
"identifier": "pts\/scikit-learn-2.0.0",
"title": "Scikit-Learn",
"app_version": "1.2.2",
"arguments": "plot_svd.py",
"description": "Benchmark: Plot Singular Value Decomposition",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"noble": {
"value": 96.614000000000004320099833421409130096435546875,
"raw_values": [
96.9920000000000044337866711430251598358154296875,
95.3070000000000021600499167107045650482177734375,
97.542000000000001591615728102624416351318359375
],
"test_run_times": [
96.3700000000000045474735088646411895751953125,
96.9899999999999948840923025272786617279052734375,
95.31000000000000227373675443232059478759765625,
97.5400000000000062527760746888816356658935546875
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
},
"scikit-learn-python-disabled-fp": {
"value": 95.6580000000000012505552149377763271331787109375,
"raw_values": [
96.022999999999996134647517465054988861083984375,
96.3730000000000046611603465862572193145751953125,
94.5789999999999935198502498678863048553466796875
],
"test_run_times": [
96.4200000000000017053025658242404460906982421875,
96.0199999999999960209606797434389591217041015625,
96.3700000000000045474735088646411895751953125,
94.5799999999999982946974341757595539093017578125
]
}
}
},
"73d95696995fe15d266a22c3dd2be8b7d68c6bfe": {
"identifier": "pts\/scikit-learn-2.0.0",
"title": "Scikit-Learn",
"app_version": "1.2.2",
"arguments": "sparsify.py",
"description": "Benchmark: Sparsify",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"noble": {
"value": 130.296999999999997044142219237983226776123046875,
"raw_values": [
127.1880000000000023874235921539366245269775390625,
131.561000000000007048583938740193843841552734375,
132.14199999999999590727384202182292938232421875
],
"test_run_times": [
131.94999999999998863131622783839702606201171875,
127.18999999999999772626324556767940521240234375,
131.56000000000000227373675443232059478759765625,
132.1399999999999863575794734060764312744140625
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
},
"scikit-learn-python-disabled-fp": {
"value": 129.05500000000000682121026329696178436279296875,
"raw_values": [
125.929000000000002046363078989088535308837890625,
126.2930000000000063664629124104976654052734375,
132.919999999999987494447850622236728668212890625,
130.68999999999999772626324556767940521240234375,
125.375,
131.37200000000001409716787748038768768310546875,
130.8029999999999972715158946812152862548828125
],
"test_run_times": [
131.539999999999992041921359486877918243408203125,
125.93000000000000682121026329696178436279296875,
126.2900000000000062527760746888816356658935546875,
132.919999999999987494447850622236728668212890625,
130.68999999999999772626324556767940521240234375,
125.3700000000000045474735088646411895751953125,
131.3700000000000045474735088646411895751953125,
130.80000000000001136868377216160297393798828125
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
}
}
},
"e1a9f2f91a786af5fa10c6ee5dfa8e087e1bbd9e": {
"identifier": "pts\/scikit-learn-2.0.0",
"title": "Scikit-Learn",
"app_version": "1.2.2",
"arguments": "lasso.py",
"description": "Benchmark: Lasso",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"noble": {
"value": 351.0230000000000245563569478690624237060546875,
"raw_values": [
347.67099999999999226929503493010997772216796875,
350.6200000000000045474735088646411895751953125,
354.77899999999999636202119290828704833984375
],
"test_run_times": [
349.6499999999999772626324556767940521240234375,
347.67000000000001591615728102624416351318359375,
350.6200000000000045474735088646411895751953125,
354.779999999999972715158946812152862548828125
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
},
"scikit-learn-python-disabled-fp": {
"value": 347.8029999999999972715158946812152862548828125,
"raw_values": [
347.32900000000000773070496506989002227783203125,
350.2350000000000136424205265939235687255859375,
345.84600000000000363797880709171295166015625
],
"test_run_times": [
348.82999999999998408384271897375583648681640625,
347.32999999999998408384271897375583648681640625,
350.23000000000001818989403545856475830078125,
345.8500000000000227373675443232059478759765625
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
}
}
},
"fd927d4ac6822e0e9cdd1cd21d8d9c39a6b99f64": {
"identifier": "pts\/scikit-learn-2.0.0",
"title": "Scikit-Learn",
"app_version": "1.2.2",
"arguments": "plot_incremental_pca.py",
"description": "Benchmark: Plot Incremental PCA",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"noble": {
"value": 102.78600000000000136424205265939235687255859375,
"raw_values": [
101.9909999999999996589394868351519107818603515625,
102.8220000000000027284841053187847137451171875,
103.5460000000000064801497501321136951446533203125
],
"test_run_times": [
164,
101.9899999999999948840923025272786617279052734375,
102.81999999999999317878973670303821563720703125,
103.5499999999999971578290569595992565155029296875
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
},
"scikit-learn-python-disabled-fp": {
"value": 101.8509999999999990905052982270717620849609375,
"raw_values": [
102.2879999999999967030817060731351375579833984375,
101.480999999999994543031789362430572509765625,
101.784999999999996589394868351519107818603515625
],
"test_run_times": [
103.3700000000000045474735088646411895751953125,
102.2900000000000062527760746888816356658935546875,
101.4800000000000039790393202565610408782958984375,
101.7900000000000062527760746888816356658935546875
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
}
}
},
"b9b7d4c61961692207a0aaf495dc141b76cd6aaf": {
"identifier": "pts\/scikit-learn-2.0.0",
"title": "Scikit-Learn",
"app_version": "1.2.2",
"arguments": "mnist.py",
"description": "Benchmark: MNIST Dataset",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"noble": {
"value": 65.7060000000000030695446184836328029632568359375,
"raw_values": [
65.03100000000000591171556152403354644775390625,
66.4669999999999987494447850622236728668212890625,
65.6200000000000045474735088646411895751953125
],
"test_run_times": [
82.719999999999998863131622783839702606201171875,
65.030000000000001136868377216160297393798828125,
66.469999999999998863131622783839702606201171875,
65.6200000000000045474735088646411895751953125
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
},
"scikit-learn-python-disabled-fp": {
"value": 65.1710000000000064801497501321136951446533203125,
"raw_values": [
64.3419999999999987494447850622236728668212890625,
66.6940000000000026147972675971686840057373046875,
64.4779999999999944293449516408145427703857421875
],
"test_run_times": [
65.0799999999999982946974341757595539093017578125,
64.340000000000003410605131648480892181396484375,
66.68999999999999772626324556767940521240234375,
64.4800000000000039790393202565610408782958984375
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
}
}
},
"63a5adc79717ebebf12163f49f5737edeb8000e7": {
"identifier": "pts\/scikit-learn-2.0.0",
"title": "Scikit-Learn",
"app_version": "1.2.2",
"arguments": "lof.py",
"description": "Benchmark: LocalOutlierFactor",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"noble": {
"value": 53.38799999999999812416717759333550930023193359375,
"raw_values": [
53.3990000000000009094947017729282379150390625,
53.21000000000000085265128291212022304534912109375,
53.55499999999999971578290569595992565155029296875
],
"test_run_times": [
63.61999999999999744204615126363933086395263671875,
53.39999999999999857891452847979962825775146484375,
53.21000000000000085265128291212022304534912109375,
53.5499999999999971578290569595992565155029296875
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
},
"scikit-learn-python-disabled-fp": {
"value": 53.00500000000000255795384873636066913604736328125,
"raw_values": [
52.68900000000000005684341886080801486968994140625,
52.965000000000003410605131648480892181396484375,
53.3599999999999994315658113919198513031005859375
],
"test_run_times": [
52.86999999999999744204615126363933086395263671875,
52.68999999999999772626324556767940521240234375,
52.96000000000000085265128291212022304534912109375,
53.3599999999999994315658113919198513031005859375
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
}
}
},
"7f70f04a8a94a61ecbc9a843bb376b0d877cd158": {
"identifier": "pts\/scikit-learn-2.0.0",
"title": "Scikit-Learn",
"app_version": "1.2.2",
"arguments": "20newsgroups.py -e logistic_regression",
"description": "Benchmark: 20 Newsgroups \/ Logistic Regression",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"noble": {
"value": 37.58800000000000096633812063373625278472900390625,
"raw_values": [
37.26899999999999835154085303656756877899169921875,
37.792000000000001591615728102624416351318359375,
37.701999999999998181010596454143524169921875
],
"test_run_times": [
46.1700000000000017053025658242404460906982421875,
37.27000000000000312638803734444081783294677734375,
37.78999999999999914734871708787977695465087890625,
37.7000000000000028421709430404007434844970703125
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
},
"scikit-learn-python-disabled-fp": {
"value": 37.804000000000002046363078989088535308837890625,
"raw_values": [
38.078000000000002955857780762016773223876953125,
37.81000000000000227373675443232059478759765625,
37.5240000000000009094947017729282379150390625
],
"test_run_times": [
37.74000000000000198951966012828052043914794921875,
38.0799999999999982946974341757595539093017578125,
37.81000000000000227373675443232059478759765625,
37.52000000000000312638803734444081783294677734375
]
}
}
},
"eb5ac7dade492d76b8b46c32c34f7bfc6410752a": {
"identifier": "pts\/scikit-learn-2.0.0",
"title": "Scikit-Learn",
"app_version": "1.2.2",
"arguments": "plot_ward.py",
"description": "Benchmark: Plot Ward",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"noble": {
"value": 53.99000000000000198951966012828052043914794921875,
"raw_values": [
55.5919999999999987494447850622236728668212890625,
52.92399999999999948840923025272786617279052734375,
55.256000000000000227373675443232059478759765625,
52.8719999999999998863131622783839702606201171875,
53.3070000000000021600499167107045650482177734375
],
"test_run_times": [
54.969999999999998863131622783839702606201171875,
55.590000000000003410605131648480892181396484375,
52.9200000000000017053025658242404460906982421875,
55.25999999999999801048033987171947956085205078125,
52.86999999999999744204615126363933086395263671875,
53.31000000000000227373675443232059478759765625
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
},
"scikit-learn-python-disabled-fp": {
"value": 54.22699999999999675992512493394315242767333984375,
"raw_values": [
55.090000000000003410605131648480892181396484375,
52.9249999999999971578290569595992565155029296875,
54.66499999999999914734871708787977695465087890625
],
"test_run_times": [
53.25,
55.090000000000003410605131648480892181396484375,
52.9200000000000017053025658242404460906982421875,
54.6700000000000017053025658242404460906982421875
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
}
}
},
"ba76fae6a1ec6482bac764805a28d53c814e7c38": {
"identifier": "pts\/scikit-learn-2.0.0",
"title": "Scikit-Learn",
"app_version": "1.2.2",
"arguments": "hist_gradient_boosting_threading.py",
"description": "Benchmark: Hist Gradient Boosting Threading",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"noble": {
"value": 111.8430000000000035242919693700969219207763671875,
"raw_values": [
111.4320000000000021600499167107045650482177734375,
111.719999999999998863131622783839702606201171875,
112.376000000000004774847184307873249053955078125
],
"test_run_times": [
112.030000000000001136868377216160297393798828125,
111.43000000000000682121026329696178436279296875,
111.719999999999998863131622783839702606201171875,
112.3799999999999954525264911353588104248046875
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
},
"scikit-learn-python-disabled-fp": {
"value": 111.3769999999999953388396534137427806854248046875,
"raw_values": [
111.3419999999999987494447850622236728668212890625,
111.3479999999999989768184605054557323455810546875,
111.4419999999999930651028989814221858978271484375
],
"test_run_times": [
111.150000000000005684341886080801486968994140625,
111.340000000000003410605131648480892181396484375,
111.349999999999994315658113919198513031005859375,
111.43999999999999772626324556767940521240234375
]
}
}
},
"44dc471833987a45b105bd8372de18bcb6cf17fe": {
"identifier": "pts\/scikit-learn-2.0.0",
"title": "Scikit-Learn",
"app_version": "1.2.2",
"arguments": "feature_expansions.py",
"description": "Benchmark: Feature Expansions",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"noble": {
"value": 135.121000000000009322320693172514438629150390625,
"raw_values": [
136.883000000000009777068044058978557586669921875,
134.056999999999987949195201508700847625732421875,
134.424000000000006593836587853729724884033203125
],
"test_run_times": [
137.69999999999998863131622783839702606201171875,
136.8799999999999954525264911353588104248046875,
134.06000000000000227373675443232059478759765625,
134.419999999999987494447850622236728668212890625
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
},
"scikit-learn-python-disabled-fp": {
"value": 135.640999999999991132426657713949680328369140625,
"raw_values": [
134.573000000000007503331289626657962799072265625,
138.19999999999998863131622783839702606201171875,
134.15100000000001045918907038867473602294921875
],
"test_run_times": [
137.5,
134.56999999999999317878973670303821563720703125,
138.19999999999998863131622783839702606201171875,
134.150000000000005684341886080801486968994140625
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
}
}
},
"933fc21aa2ac9ab11532c009b8dc284d9f6a109e": {
"identifier": "pts\/scikit-learn-2.0.0",
"title": "Scikit-Learn",
"app_version": "1.2.2",
"arguments": "plot_omp_lars.py",
"description": "Benchmark: Plot OMP vs. LARS",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"noble": {
"value": 70.0870000000000032969182939268648624420166015625,
"raw_values": [
70.058999999999997498889570124447345733642578125,
69.72100000000000363797880709171295166015625,
70.481999999999999317878973670303821563720703125
],
"test_run_times": [
69.400000000000005684341886080801486968994140625,
70.06000000000000227373675443232059478759765625,
69.719999999999998863131622783839702606201171875,
70.4800000000000039790393202565610408782958984375
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
},
"scikit-learn-python-disabled-fp": {
"value": 69.8349999999999937472239253111183643341064453125,
"raw_values": [
69.5720000000000027284841053187847137451171875,
70.2240000000000037516656448133289813995361328125,
69.7099999999999937472239253111183643341064453125
],
"test_run_times": [
69.68999999999999772626324556767940521240234375,
69.56999999999999317878973670303821563720703125,
70.219999999999998863131622783839702606201171875,
69.7099999999999937472239253111183643341064453125
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
}
}
},
"b1d0cf09bc92a5cde512966ce6419a397219838c": {
"identifier": "pts\/scikit-learn-2.0.0",
"title": "Scikit-Learn",
"app_version": "1.2.2",
"arguments": "hist_gradient_boosting_higgsboson.py",
"description": "Benchmark: Hist Gradient Boosting Higgs Boson",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"noble": {
"value": 61.36299999999999954525264911353588104248046875,
"raw_values": [
61.3960000000000007958078640513122081756591796875,
61.3659999999999996589394868351519107818603515625,
61.326999999999998181010596454143524169921875
],
"test_run_times": [
922.700000000000045474735088646411895751953125,
61.39999999999999857891452847979962825775146484375,
61.36999999999999744204615126363933086395263671875,
61.3299999999999982946974341757595539093017578125
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
},
"scikit-learn-python-disabled-fp": {
"value": 61.16499999999999914734871708787977695465087890625,
"raw_values": [
60.73899999999999721467247582040727138519287109375,
61.832999999999998408384271897375583648681640625,
60.92399999999999948840923025272786617279052734375
],
"test_run_times": [
63.4200000000000017053025658242404460906982421875,
60.74000000000000198951966012828052043914794921875,
61.8299999999999982946974341757595539093017578125,
60.9200000000000017053025658242404460906982421875
]
}
}
},
"8174ce61c42810468f08ad8d0bde12d251b635fc": {
"identifier": "pts\/scikit-learn-2.0.0",
"title": "Scikit-Learn",
"app_version": "1.2.2",
"arguments": "plot_fastkmeans.py",
"description": "Benchmark: Plot Fast KMeans",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"noble": {
"value": 172.977000000000003865352482534945011138916015625,
"raw_values": [
171.390999999999991132426657713949680328369140625,
174.1920000000000072759576141834259033203125,
173.34899999999998954081092961132526397705078125
],
"test_run_times": [
172.31000000000000227373675443232059478759765625,
171.3899999999999863575794734060764312744140625,
174.18999999999999772626324556767940521240234375,
173.349999999999994315658113919198513031005859375
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
},
"scikit-learn-python-disabled-fp": {
"value": 172.640999999999991132426657713949680328369140625,
"raw_values": [
172.640999999999991132426657713949680328369140625,
173.31999999999999317878973670303821563720703125,
171.962999999999993860910763032734394073486328125
],
"test_run_times": [
173.259999999999990905052982270717620849609375,
172.6399999999999863575794734060764312744140625,
173.31999999999999317878973670303821563720703125,
171.960000000000007958078640513122081756591796875
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
}
}
},
"37c0f1151209d93333f0ea6f8796368758660656": {
"identifier": "pts\/scikit-learn-2.0.0",
"title": "Scikit-Learn",
"app_version": "1.2.2",
"arguments": "covertype.py",
"description": "Benchmark: Covertype Dataset Benchmark",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"noble": {
"value": 375.95400000000000773070496506989002227783203125,
"raw_values": [
379.346999999999979991116560995578765869140625,
369.1480000000000245563569478690624237060546875,
379.36700000000001864464138634502887725830078125
],
"test_run_times": [
366.1299999999999954525264911353588104248046875,
379.3500000000000227373675443232059478759765625,
369.1499999999999772626324556767940521240234375,
379.3700000000000045474735088646411895751953125
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
},
"scikit-learn-python-disabled-fp": {
"value": 375.45999999999997953636921010911464691162109375,
"raw_values": [
365.134000000000014551915228366851806640625,
378.6480000000000245563569478690624237060546875,
382.59899999999998954081092961132526397705078125
],
"test_run_times": [
370.1100000000000136424205265939235687255859375,
365.1299999999999954525264911353588104248046875,
378.6499999999999772626324556767940521240234375,
382.6000000000000227373675443232059478759765625
]
}
}
},
"d9af098cc5457ba3100464862063971a4b7f12b4": {
"identifier": "pts\/scikit-learn-2.0.0",
"title": "Scikit-Learn",
"app_version": "1.2.2",
"arguments": "plot_neighbors.py",
"description": "Benchmark: Plot Neighbors",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"noble": {
"value": 144.443999999999988403942552395164966583251953125,
"raw_values": [
143.508000000000009777068044058978557586669921875,
147.71199999999998908606357872486114501953125,
142.1109999999999899955582804977893829345703125
],
"test_run_times": [
144.3700000000000045474735088646411895751953125,
143.509999999999990905052982270717620849609375,
147.710000000000007958078640513122081756591796875,
142.1100000000000136424205265939235687255859375
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
},
"scikit-learn-python-disabled-fp": {
"value": 144.30099999999998772182152606546878814697265625,
"raw_values": [
146.503999999999990677679306827485561370849609375,
142.44999999999998863131622783839702606201171875,
143.94900000000001227817847393453121185302734375
],
"test_run_times": [
142.259999999999990905052982270717620849609375,
146.5,
142.44999999999998863131622783839702606201171875,
143.94999999999998863131622783839702606201171875
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
}
}
},
"28b1b20731171aac5a2c58f51dec271541d6ac80": {
"identifier": "pts\/scikit-learn-2.0.0",
"title": "Scikit-Learn",
"app_version": "1.2.2",
"arguments": "plot_lasso_path.py",
"description": "Benchmark: Plot Lasso Path",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"noble": {
"value": 232.39699999999999135980033315718173980712890625,
"raw_values": [
232.05000000000001136868377216160297393798828125,
232.58699999999998908606357872486114501953125,
232.5529999999999972715158946812152862548828125
],
"test_run_times": [
230.31999999999999317878973670303821563720703125,
232.05000000000001136868377216160297393798828125,
232.590000000000003410605131648480892181396484375,
232.55000000000001136868377216160297393798828125
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
},
"scikit-learn-python-disabled-fp": {
"value": 232.342999999999989313437254168093204498291015625,
"raw_values": [
232.6299999999999954525264911353588104248046875,
233.759999999999990905052982270717620849609375,
230.638000000000005229594535194337368011474609375
],
"test_run_times": [
236.169999999999987494447850622236728668212890625,
232.6299999999999954525264911353588104248046875,
233.759999999999990905052982270717620849609375,
230.6399999999999863575794734060764312744140625
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
}
}
},
"d55264cdc780c09f2c3abafeb2c4a73ad1d65420": {
"identifier": "pts\/scikit-learn-2.0.0",
"title": "Scikit-Learn",
"app_version": "1.2.2",
"arguments": "kernel_pca_solvers_time_vs_n_samples.py",
"description": "Benchmark: Kernel PCA Solvers \/ Time vs. N Samples",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"noble": {
"value": 266.44999999999998863131622783839702606201171875,
"raw_values": [
268.60300000000000864019966684281826019287109375,
263.35700000000002773958840407431125640869140625,
267.3890000000000100044417195022106170654296875
],
"test_run_times": [
265.30000000000001136868377216160297393798828125,
268.6000000000000227373675443232059478759765625,
263.3600000000000136424205265939235687255859375,
267.3899999999999863575794734060764312744140625
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
},
"scikit-learn-python-disabled-fp": {
"value": 266.46499999999997498889570124447345733642578125,
"raw_values": [
266.58899999999999863575794734060764312744140625,
267.52199999999999135980033315718173980712890625,
265.2839999999999918145476840436458587646484375
],
"test_run_times": [
265.16000000000002501110429875552654266357421875,
266.58999999999997498889570124447345733642578125,
267.51999999999998181010596454143524169921875,
265.279999999999972715158946812152862548828125
]
}
}
},
"5d202841d6aa6bfc6affbd849769b8e3dde1e8ac": {
"identifier": "pts\/scikit-learn-2.0.0",
"title": "Scikit-Learn",
"app_version": "1.2.2",
"arguments": "plot_nmf.py",
"description": "Benchmark: Plot Non-Negative Matrix Factorization",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"noble": {
"test_run_times": [
49.17999999999999971578290569595992565155029296875,
49.21000000000000085265128291212022304534912109375,
49.5799999999999982946974341757595539093017578125
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
},
"error": "The test quit with a non-zero exit status. E: KeyError:
"
}
},
"scikit-learn-python-disabled-fp": {
"test_run_times": [
48.9500000000000028421709430404007434844970703125,
49.530000000000001136868377216160297393798828125,
50.14999999999999857891452847979962825775146484375
],
"details": {
"error": "The test quit with a non-zero exit status. E: KeyError: "
}
}
}
},
"237fe2d8f04238f0508e07dd7b29b682adcc19c4": {
"identifier": "pts\/scikit-learn-2.0.0",
"title": "Scikit-Learn",
"app_version": "1.2.2",
"arguments": "rcv1_logreg_convergence.py",
"description": "Benchmark: RCV1 Logreg Convergencet",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"noble": {
"test_run_times": [
12.550000000000000710542735760100185871124267578125,
12.3900000000000005684341886080801486968994140625,
11.8499999999999996447286321199499070644378662109375
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
},
"error": "The test quit with a non-zero exit status. E: IndexError: list index out of range"
}
},
"scikit-learn-python-disabled-fp": {
"test_run_times": [
12.949999999999999289457264239899814128875732421875,
11.8300000000000000710542735760100185871124267578125,
11.910000000000000142108547152020037174224853515625
],
"details": {
"error": "The test quit with a non-zero exit status. E: IndexError: list index out of range"
}
}
}
},
"3d14ba284d3ab6c21844a4484b038803d2028ea7": {
"identifier": "pts\/scikit-learn-2.0.0",
"title": "Scikit-Learn",
"app_version": "1.2.2",
"arguments": "isotonic.py --iterations 100 --log_min_problem_size 1 --log_max_problem_size 10 --dataset pathological",
"description": "Benchmark: Isotonic \/ Pathological",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"noble": {
"test_run_times": [
498.51999999999998181010596454143524169921875,
494.82999999999998408384271897375583648681640625,
498.42000000000001591615728102624416351318359375
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
},
"error": "The test quit with a non-zero exit status."
}
},
"scikit-learn-python-disabled-fp": {
"test_run_times": [
508.509999999999990905052982270717620849609375,
510.18999999999999772626324556767940521240234375,
499.23000000000001818989403545856475830078125
],
"details": {
"error": "The test quit with a non-zero exit status."
}
}
}
},
"38419f04decfc6bf0c4179598cd321db506acb1c": {
"identifier": "pts\/scikit-learn-2.0.0",
"title": "Scikit-Learn",
"app_version": "1.2.2",
"arguments": "plot_parallel_pairwise.py",
"description": "Benchmark: Plot Parallel Pairwise",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"noble": {
"test_run_times": [
0.92000000000000003996802888650563545525074005126953125,
0.939999999999999946709294817992486059665679931640625,
0.93000000000000004884981308350688777863979339599609375
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
},
"error": "The test quit with a non-zero exit status. E: numpy.core._exceptions._ArrayMemoryError: Unable to allocate 74.5 GiB for an array with shape (100000, 100000) and data type float64"
}
},
"scikit-learn-python-disabled-fp": {
"test_run_times": [
1.79000000000000003552713678800500929355621337890625,
1.0100000000000000088817841970012523233890533447265625,
0.93000000000000004884981308350688777863979339599609375
],
"details": {
"error": "The test quit with a non-zero exit status. E: numpy.core._exceptions._ArrayMemoryError: Unable to allocate 74.5 GiB for an array with shape (100000, 100000) and data type float64"
}
}
}
},
"ed44b3bb5383f263e9d8ae1c7656ef0c8374a497": {
"identifier": "pts\/scikit-learn-2.0.0",
"title": "Scikit-Learn",
"app_version": "1.2.2",
"arguments": "online_ocsvm.py",
"description": "Benchmark: SGDOneClassSVM",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"noble": {
"value": 328.9379999999999881765688769519329071044921875,
"raw_values": [
403.8020000000000209183781407773494720458984375,
309.2450000000000045474735088646411895751953125,
317.3319999999999936335370875895023345947265625,
322.56499999999999772626324556767940521240234375,
324.70999999999997953636921010911464691162109375,
318.1109999999999899955582804977893829345703125,
326.36700000000001864464138634502887725830078125,
328.08899999999999863575794734060764312744140625,
310.22100000000000363797880709171295166015625
],
"test_run_times": [
111.1099999999999994315658113919198513031005859375,
403.80000000000001136868377216160297393798828125,
309.240000000000009094947017729282379150390625,
317.32999999999998408384271897375583648681640625,
322.56000000000000227373675443232059478759765625,
324.70999999999997953636921010911464691162109375,
318.1100000000000136424205265939235687255859375,
326.3700000000000045474735088646411895751953125,
328.08999999999997498889570124447345733642578125,
310.220000000000027284841053187847137451171875
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
},
"scikit-learn-python-disabled-fp": {
"value": 317.2549999999999954525264911353588104248046875,
"raw_values": [
323.06599999999997407940099947154521942138671875,
312.807000000000016370904631912708282470703125,
315.89299999999997226041159592568874359130859375
],
"test_run_times": [
318.66000000000002501110429875552654266357421875,
323.06999999999999317878973670303821563720703125,
312.81000000000000227373675443232059478759765625,
315.8899999999999863575794734060764312744140625
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
}
}
},
"af4f87b4652902abc8862d2606f23eee34b7a679": {
"identifier": "pts\/scikit-learn-2.0.0",
"title": "Scikit-Learn",
"app_version": "1.2.2",
"arguments": "sgd_regression.py",
"description": "Benchmark: SGD Regression",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"noble": {
"value": 83.0450000000000017053025658242404460906982421875,
"raw_values": [
79.68500000000000227373675443232059478759765625,
100.2960000000000064801497501321136951446533203125,
100.61799999999999499777914024889469146728515625,
79.4260000000000019326762412674725055694580078125,
81.518000000000000682121026329696178436279296875,
79.8130000000000023874235921539366245269775390625,
78.626000000000004774847184307873249053955078125,
78.905000000000001136868377216160297393798828125,
81.5400000000000062527760746888816356658935546875,
81.5750000000000028421709430404007434844970703125,
78.98799999999999954525264911353588104248046875,
82.3659999999999996589394868351519107818603515625,
78.5079999999999955662133288569748401641845703125,
82.1569999999999964757080306299030780792236328125,
81.6590000000000060254023992456495761871337890625
],
"test_run_times": [
82.599999999999994315658113919198513031005859375,
79.68000000000000682121026329696178436279296875,
100.2999999999999971578290569595992565155029296875,
100.6200000000000045474735088646411895751953125,
79.43000000000000682121026329696178436279296875,
81.5199999999999960209606797434389591217041015625,
79.81000000000000227373675443232059478759765625,
78.6299999999999954525264911353588104248046875,
78.909999999999996589394868351519107818603515625,
81.5400000000000062527760746888816356658935546875,
81.56999999999999317878973670303821563720703125,
78.9899999999999948840923025272786617279052734375,
82.3700000000000045474735088646411895751953125,
78.5100000000000051159076974727213382720947265625,
82.159999999999996589394868351519107818603515625,
81.659999999999996589394868351519107818603515625
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
},
"scikit-learn-python-disabled-fp": {
"value": 80.1470000000000055706550483591854572296142578125,
"raw_values": [
78.058999999999997498889570124447345733642578125,
80.3409999999999939745976007543504238128662109375,
82.04099999999999681676854379475116729736328125
],
"test_run_times": [
80.9800000000000039790393202565610408782958984375,
78.06000000000000227373675443232059478759765625,
80.340000000000003410605131648480892181396484375,
82.0400000000000062527760746888816356658935546875
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
}
}
},
"77f5fb435b85e7e5ed17fd9f5d749fc642e44906": {
"identifier": "pts\/scikit-learn-2.0.0",
"title": "Scikit-Learn",
"app_version": "1.2.2",
"arguments": "glmnet.py",
"description": "Benchmark: Glmnet",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"noble": {
"test_run_times": [
0.54000000000000003552713678800500929355621337890625,
0.54000000000000003552713678800500929355621337890625,
0.5500000000000000444089209850062616169452667236328125
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
},
"error": "The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'glmnet'"
}
},
"scikit-learn-python-disabled-fp": {
"test_run_times": [
0.5300000000000000266453525910037569701671600341796875,
0.54000000000000003552713678800500929355621337890625,
0.5300000000000000266453525910037569701671600341796875
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
},
"error": "The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'glmnet'"
}
}
}
}
}
}