Compare your own system(s) to this result file with the
Phoronix Test Suite by running the command:
phoronix-test-suite benchmark 2406248-NE-C6I4XLARG35
{
"title": "c6i.4xlarge",
"last_modified": "2024-06-25 01:12:32",
"description": "c6i.4xlarge",
"systems": {
"c6i.4xlarge": {
"identifier": "c6i.4xlarge",
"hardware": {
"Processor": "Intel Xeon Platinum 8375C (8 Cores \/ 16 Threads)",
"Motherboard": "Amazon EC2 c6i.4xlarge (1.0 BIOS)",
"Chipset": "Intel 440FX 82441FX PMC",
"Memory": "1 x 32 GB DDR4-3200MT\/s",
"Disk": "215GB Amazon Elastic Block Store",
"Graphics": "EFI VGA",
"Network": "Amazon Elastic"
},
"software": {
"OS": "Ubuntu 22.04",
"Kernel": "6.5.0-1020-aws (x86_64)",
"Vulkan": "1.3.255",
"Compiler": "GCC 11.4.0",
"File-System": "ext4",
"Screen Resolution": "800x600",
"System Layer": "amazon"
},
"user": "root",
"timestamp": "2024-06-21 12:54:15",
"client_version": "10.8.5",
"data": {
"compiler-configuration": "--build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-bootstrap --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,brig,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-link-serialization=2 --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-targets=nvptx-none=\/build\/gcc-11-XeT9lY\/gcc-11-11.4.0\/debian\/tmp-nvptx\/usr,amdgcn-amdhsa=\/build\/gcc-11-XeT9lY\/gcc-11-11.4.0\/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-build-config=bootstrap-lto-lean --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-microcode": "0xd0003d1",
"kernel-extra-details": "Transparent Huge Pages: madvise",
"python": "Python 3.11.9",
"security": "gather_data_sampling: Unknown: Dependent on hypervisor status + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Mitigation of Clear buffers; SMT Host state unknown + retbleed: Not affected + spec_rstack_overflow: Not affected + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy\/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Enhanced \/ Automatic IBRS; IBPB: conditional; RSB filling; PBRSB-eIBRS: SW sequence; BHI: Syscall hardening KVM: SW loop + srbds: Not affected + tsx_async_abort: Not affected"
}
},
"Intel Xeon Platinum 8375C - EFI VGA - Amazon EC2": {
"identifier": "Intel Xeon Platinum 8375C - EFI VGA - Amazon EC2",
"hardware": {
"Processor": "Intel Xeon Platinum 8375C (8 Cores \/ 16 Threads)",
"Motherboard": "Amazon EC2 c6i.4xlarge (1.0 BIOS)",
"Chipset": "Intel 440FX 82441FX PMC",
"Memory": "1 x 32 GB DDR4-3200MT\/s",
"Disk": "215GB Amazon Elastic Block Store",
"Graphics": "EFI VGA",
"Network": "Amazon Elastic"
},
"software": {
"OS": "Ubuntu 22.04",
"Kernel": "6.5.0-1020-aws (x86_64)",
"Vulkan": "1.3.255",
"Compiler": "GCC 11.4.0",
"File-System": "ext4",
"Screen Resolution": "800x600",
"System Layer": "amazon"
},
"user": "root",
"timestamp": "2024-06-24 09:03:00",
"client_version": "10.8.5",
"data": {
"compiler-configuration": "--build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-bootstrap --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,brig,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-link-serialization=2 --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-targets=nvptx-none=\/build\/gcc-11-XeT9lY\/gcc-11-11.4.0\/debian\/tmp-nvptx\/usr,amdgcn-amdhsa=\/build\/gcc-11-XeT9lY\/gcc-11-11.4.0\/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-build-config=bootstrap-lto-lean --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-microcode": "0xd0003d1",
"kernel-extra-details": "Transparent Huge Pages: madvise",
"python": "Python 3.11.9",
"security": "gather_data_sampling: Unknown: Dependent on hypervisor status + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Mitigation of Clear buffers; SMT Host state unknown + retbleed: Not affected + spec_rstack_overflow: Not affected + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy\/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Enhanced \/ Automatic IBRS; IBPB: conditional; RSB filling; PBRSB-eIBRS: SW sequence; BHI: Syscall hardening KVM: SW loop + srbds: Not affected + tsx_async_abort: Not affected"
}
}
},
"results": {
"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": {
"c6i.4xlarge": {
"value": 2252.40999999999985448084771633148193359375,
"raw_values": [
2262.09099999999989449861459434032440185546875,
2257.4250000000001818989403545856475830078125,
2237.71500000000014551915228366851806640625
],
"test_run_times": [
2246.34999999999990905052982270717620849609375,
2262.09000000000014551915228366851806640625,
2257.420000000000072759576141834259033203125,
2237.6999999999998181010596454143524169921875
],
"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": {
"c6i.4xlarge": {
"value": 1885.9510000000000218278728425502777099609375,
"raw_values": [
1882.171000000000049112713895738124847412109375,
1902.145999999999958163243718445301055908203125,
1873.535000000000081854523159563541412353515625
],
"test_run_times": [
1905.4600000000000363797880709171295166015625,
1882.170000000000072759576141834259033203125,
1902.15000000000009094947017729282379150390625,
1873.5399999999999636202119290828704833984375
],
"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": {
"c6i.4xlarge": {
"value": 1191.57099999999991268850862979888916015625,
"raw_values": [
1194.897999999999910869519226253032684326171875,
1191.10500000000001818989403545856475830078125,
1188.709000000000060026650317013263702392578125
],
"test_run_times": [
1311.2100000000000363797880709171295166015625,
1194.90000000000009094947017729282379150390625,
1191.09999999999990905052982270717620849609375,
1188.7100000000000363797880709171295166015625
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
}
}
},
"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": {
"c6i.4xlarge": {
"value": 852.988000000000056388671509921550750732421875,
"raw_values": [
842.62300000000004729372449219226837158203125,
857.5240000000000009094947017729282379150390625,
858.816000000000030922819860279560089111328125
],
"test_run_times": [
860.8200000000000500222085975110530853271484375,
842.6200000000000045474735088646411895751953125,
857.51999999999998181010596454143524169921875,
858.8200000000000500222085975110530853271484375
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
}
}
},
"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": {
"c6i.4xlarge": {
"test_run_times": [
698.779999999999972715158946812152862548828125,
695.3300000000000409272615797817707061767578125,
696.2100000000000363797880709171295166015625
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
},
"error": "The test quit with a non-zero exit status."
}
}
}
},
"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": {
"c6i.4xlarge": {
"value": 483.16699999999997316990629769861698150634765625,
"raw_values": [
482.00700000000000500222085975110530853271484375,
486.605999999999994543031789362430572509765625,
480.88700000000000045474735088646411895751953125
],
"test_run_times": [
477.26999999999998181010596454143524169921875,
482.009999999999990905052982270717620849609375,
486.6100000000000136424205265939235687255859375,
480.8899999999999863575794734060764312744140625
],
"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": {
"c6i.4xlarge": {
"value": 405.47300000000001318767317570745944976806640625,
"raw_values": [
400.9180000000000063664629124104976654052734375,
403.0670000000000072759576141834259033203125,
412.43400000000002592059900052845478057861328125
],
"test_run_times": [
405.490000000000009094947017729282379150390625,
400.92000000000001591615728102624416351318359375,
403.06999999999999317878973670303821563720703125,
412.43000000000000682121026329696178436279296875
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
}
}
},
"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": {
"c6i.4xlarge": {
"value": 349.904999999999972715158946812152862548828125,
"raw_values": [
350.53199999999998226485331542789936065673828125,
348.27899999999999636202119290828704833984375,
350.904999999999972715158946812152862548828125
],
"test_run_times": [
402.8600000000000136424205265939235687255859375,
350.529999999999972715158946812152862548828125,
348.279999999999972715158946812152862548828125,
350.91000000000002501110429875552654266357421875
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
}
}
},
"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": {
"c6i.4xlarge": {
"value": 297.02600000000001045918907038867473602294921875,
"raw_values": [
296.5629999999999881765688769519329071044921875,
297.7730000000000245563569478690624237060546875,
296.740999999999985448084771633148193359375
],
"test_run_times": [
380.18000000000000682121026329696178436279296875,
296.56000000000000227373675443232059478759765625,
297.76999999999998181010596454143524169921875,
296.740000000000009094947017729282379150390625
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
}
}
},
"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": {
"c6i.4xlarge": {
"value": 318.77699999999998681232682429254055023193359375,
"raw_values": [
318.18099999999998317434801720082759857177734375,
318.9420000000000072759576141834259033203125,
319.20900000000000318323145620524883270263671875
],
"test_run_times": [
301.56999999999999317878973670303821563720703125,
318.18000000000000682121026329696178436279296875,
318.93999999999999772626324556767940521240234375,
319.20999999999997953636921010911464691162109375
],
"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": {
"c6i.4xlarge": {
"value": 268.26799999999997226041159592568874359130859375,
"raw_values": [
267.71499999999997498889570124447345733642578125,
269.15699999999998226485331542789936065673828125,
267.9329999999999927240423858165740966796875
],
"test_run_times": [
267.93000000000000682121026329696178436279296875,
267.70999999999997953636921010911464691162109375,
269.16000000000002501110429875552654266357421875,
267.93000000000000682121026329696178436279296875
],
"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": {
"c6i.4xlarge": {
"value": 262.86700000000001864464138634502887725830078125,
"raw_values": [
261.26799999999997226041159592568874359130859375,
263.11200000000002319211489520967006683349609375,
264.220000000000027284841053187847137451171875
],
"test_run_times": [
266.26999999999998181010596454143524169921875,
261.26999999999998181010596454143524169921875,
263.1100000000000136424205265939235687255859375,
264.220000000000027284841053187847137451171875
],
"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": {
"c6i.4xlarge": {
"value": 259.18900000000002137312549166381359100341796875,
"raw_values": [
259.78100000000000591171556152403354644775390625,
256.23200000000002773958840407431125640869140625,
261.5539999999999736246536485850811004638671875
],
"test_run_times": [
261.58999999999997498889570124447345733642578125,
259.779999999999972715158946812152862548828125,
256.23000000000001818989403545856475830078125,
261.55000000000001136868377216160297393798828125
],
"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": {
"c6i.4xlarge": {
"value": 258.3990000000000009094947017729282379150390625,
"raw_values": [
258.38299999999998135535861365497112274169921875,
258.94099999999997407940099947154521942138671875,
257.874000000000023646862246096134185791015625
],
"test_run_times": [
258.68000000000000682121026329696178436279296875,
258.3799999999999954525264911353588104248046875,
258.93999999999999772626324556767940521240234375,
257.8700000000000045474735088646411895751953125
],
"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": {
"c6i.4xlarge": {
"value": 225.03800000000001091393642127513885498046875,
"raw_values": [
223.657999999999987039700499735772609710693359375,
221.741000000000013869794202037155628204345703125,
229.7160000000000081854523159563541412353515625
],
"test_run_times": [
228.210000000000007958078640513122081756591796875,
223.659999999999996589394868351519107818603515625,
221.740000000000009094947017729282379150390625,
229.719999999999998863131622783839702606201171875
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
}
}
},
"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": {
"c6i.4xlarge": {
"value": 222.47800000000000864019966684281826019287109375,
"raw_values": [
222.623999999999995225152815692126750946044921875,
222.465000000000003410605131648480892181396484375,
222.344999999999998863131622783839702606201171875
],
"test_run_times": [
221.3600000000000136424205265939235687255859375,
222.6200000000000045474735088646411895751953125,
222.460000000000007958078640513122081756591796875,
222.340000000000003410605131648480892181396484375
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
}
}
},
"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": {
"c6i.4xlarge": {
"value": 204.78600000000000136424205265939235687255859375,
"raw_values": [
206.354000000000013415046851150691509246826171875,
203.294000000000011141310096718370914459228515625,
204.70900000000000318323145620524883270263671875
],
"test_run_times": [
200.159999999999996589394868351519107818603515625,
206.349999999999994315658113919198513031005859375,
203.289999999999992041921359486877918243408203125,
204.710000000000007958078640513122081756591796875
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
}
}
},
"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": {
"c6i.4xlarge": {
"value": 199.025000000000005684341886080801486968994140625,
"raw_values": [
199.2740000000000009094947017729282379150390625,
200.609000000000008867573342286050319671630859375,
197.1920000000000072759576141834259033203125
],
"test_run_times": [
198.280000000000001136868377216160297393798828125,
199.270000000000010231815394945442676544189453125,
200.6100000000000136424205265939235687255859375,
197.18999999999999772626324556767940521240234375
],
"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": {
"c6i.4xlarge": {
"value": 153.210000000000007958078640513122081756591796875,
"raw_values": [
151.78800000000001091393642127513885498046875,
154.330999999999988858689903281629085540771484375,
153.509999999999990905052982270717620849609375
],
"test_run_times": [
153.490000000000009094947017729282379150390625,
151.789999999999992041921359486877918243408203125,
154.330000000000012505552149377763271331787109375,
153.509999999999990905052982270717620849609375
],
"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": {
"c6i.4xlarge": {
"value": 54.5439999999999969304553815163671970367431640625,
"raw_values": [
54.3509999999999990905052982270717620849609375,
54.8059999999999973852027324028313159942626953125,
54.47399999999999664623828721232712268829345703125
],
"test_run_times": [
432.1299999999999954525264911353588104248046875,
54.35000000000000142108547152020037174224853515625,
54.81000000000000227373675443232059478759765625,
54.469999999999998863131622783839702606201171875
],
"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": {
"c6i.4xlarge": {
"value": 125.2540000000000048885340220294892787933349609375,
"raw_values": [
124.7600000000000051159076974727213382720947265625,
126.150000000000005684341886080801486968994140625,
124.852000000000003865352482534945011138916015625
],
"test_run_times": [
124.7900000000000062527760746888816356658935546875,
124.7600000000000051159076974727213382720947265625,
126.150000000000005684341886080801486968994140625,
124.849999999999994315658113919198513031005859375
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
}
}
},
"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": {
"c6i.4xlarge": {
"value": 106.328000000000002955857780762016773223876953125,
"raw_values": [
106.45900000000000318323145620524883270263671875,
106.3239999999999980673237587325274944305419921875,
106.2000000000000028421709430404007434844970703125
],
"test_run_times": [
106.2600000000000051159076974727213382720947265625,
106.4599999999999937472239253111183643341064453125,
106.31999999999999317878973670303821563720703125,
106.2000000000000028421709430404007434844970703125
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
}
}
},
"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": {
"c6i.4xlarge": {
"value": 104.03100000000000591171556152403354644775390625,
"raw_values": [
104.162000000000006139089236967265605926513671875,
104.316000000000002501110429875552654266357421875,
103.614000000000004320099833421409130096435546875
],
"test_run_times": [
103.5199999999999960209606797434389591217041015625,
104.159999999999996589394868351519107818603515625,
104.31999999999999317878973670303821563720703125,
103.6099999999999994315658113919198513031005859375
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
}
}
},
"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": {
"c6i.4xlarge": {
"value": 90.72100000000000363797880709171295166015625,
"raw_values": [
90.63200000000000500222085975110530853271484375,
90.8900000000000005684341886080801486968994140625,
90.6410000000000053432813729159533977508544921875
],
"test_run_times": [
136.30000000000001136868377216160297393798828125,
90.6299999999999954525264911353588104248046875,
90.8900000000000005684341886080801486968994140625,
90.6400000000000005684341886080801486968994140625
],
"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": {
"c6i.4xlarge": {
"value": 85.56000000000000227373675443232059478759765625,
"raw_values": [
85.5750000000000028421709430404007434844970703125,
85.4309999999999973852027324028313159942626953125,
85.6749999999999971578290569595992565155029296875
],
"test_run_times": [
130.039999999999992041921359486877918243408203125,
85.56999999999999317878973670303821563720703125,
85.43000000000000682121026329696178436279296875,
85.6700000000000017053025658242404460906982421875
],
"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": {
"c6i.4xlarge": {
"value": 95.313999999999992951416061259806156158447265625,
"raw_values": [
96.5319999999999964757080306299030780792236328125,
93.989000000000004320099833421409130096435546875,
95.4200000000000017053025658242404460906982421875
],
"test_run_times": [
96.3700000000000045474735088646411895751953125,
96.530000000000001136868377216160297393798828125,
93.9899999999999948840923025272786617279052734375,
95.4200000000000017053025658242404460906982421875
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
}
}
},
"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": {
"c6i.4xlarge": {
"value": 90.35800000000000409272615797817707061767578125,
"raw_values": [
91.6200000000000045474735088646411895751953125,
89.808999999999997498889570124447345733642578125,
89.6460000000000007958078640513122081756591796875
],
"test_run_times": [
93.9800000000000039790393202565610408782958984375,
91.6200000000000045474735088646411895751953125,
89.81000000000000227373675443232059478759765625,
89.650000000000005684341886080801486968994140625
],
"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": {
"c6i.4xlarge": {
"value": 77.4140000000000014779288903810083866119384765625,
"raw_values": [
77.94499999999999317878973670303821563720703125,
76.8479999999999989768184605054557323455810546875,
77.4489999999999980673237587325274944305419921875
],
"test_run_times": [
114.56000000000000227373675443232059478759765625,
77.93999999999999772626324556767940521240234375,
76.849999999999994315658113919198513031005859375,
77.4500000000000028421709430404007434844970703125
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
}
}
},
"d8a158c9d5cf7db0d9c36abbcd79d2f6c457c0e8": {
"identifier": "pts\/onednn-3.4.0",
"title": "oneDNN",
"app_version": "3.4",
"arguments": "--deconv --batch=inputs\/deconv\/shapes_1d --engine=cpu",
"description": "Harness: Deconvolution Batch shapes_1d - Engine: CPU",
"scale": "ms",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"c6i.4xlarge": {
"value": 7.7388899999999996026645021629519760608673095703125,
"raw_values": [
6.82139000000000006451728040701709687709808349609375,
7.53056000000000036465053199208341538906097412109375,
8.88571999999999917463355814106762409210205078125,
7.796820000000000305817593471147119998931884765625,
8.3863199999999995526422935654409229755401611328125,
7.41781999999999985817566994228400290012359619140625,
7.781819999999999737383404863066971302032470703125,
7.64768000000000025551116777933202683925628662109375,
7.48186999999999979849008013843558728694915771484375,
6.9695999999999997953636921010911464691162109375,
7.08387000000000011112888387287966907024383544921875,
7.69367000000000000881072992342524230480194091796875,
8.452510000000000189857018995098769664764404296875,
8.253299999999999414512785733677446842193603515625,
7.880359999999999587316779070533812046051025390625
],
"min_result": [
"5.3"
],
"test_run_times": [
21.1400000000000005684341886080801486968994140625,
21.1400000000000005684341886080801486968994140625,
21.1400000000000005684341886080801486968994140625,
21.1400000000000005684341886080801486968994140625,
21.1400000000000005684341886080801486968994140625,
21.1400000000000005684341886080801486968994140625,
21.1400000000000005684341886080801486968994140625,
21.1400000000000005684341886080801486968994140625,
21.129999999999999005240169935859739780426025390625,
21.1400000000000005684341886080801486968994140625,
21.129999999999999005240169935859739780426025390625,
21.1400000000000005684341886080801486968994140625,
21.1400000000000005684341886080801486968994140625,
21.1400000000000005684341886080801486968994140625,
21.129999999999999005240169935859739780426025390625
],
"details": {
"compiler-options": {
"compiler-type": "CXX",
"compiler": "g++",
"compiler-options": "-O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl"
}
}
}
}
},
"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": {
"c6i.4xlarge": {
"value": 75.25,
"raw_values": [
75.1400000000000005684341886080801486968994140625,
75.530000000000001136868377216160297393798828125,
75.06999999999999317878973670303821563720703125
],
"test_run_times": [
84.3900000000000005684341886080801486968994140625,
75.1400000000000005684341886080801486968994140625,
75.530000000000001136868377216160297393798828125,
75.06999999999999317878973670303821563720703125
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
}
}
},
"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": {
"c6i.4xlarge": {
"value": 74.6329999999999955662133288569748401641845703125,
"raw_values": [
74.6400000000000005684341886080801486968994140625,
74.6689999999999969304553815163671970367431640625,
74.5909999999999939745976007543504238128662109375
],
"test_run_times": [
74.2900000000000062527760746888816356658935546875,
74.6400000000000005684341886080801486968994140625,
74.6700000000000017053025658242404460906982421875,
74.590000000000003410605131648480892181396484375
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
}
}
},
"a4ef571508e0145a960b428b4500ad810557312e": {
"identifier": "pts\/mlpack-1.0.2",
"title": "Mlpack Benchmark",
"arguments": "SCIKIT_QDA",
"description": "Benchmark: scikit_qda",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"c6i.4xlarge": {
"value": 30.199999999999999289457264239899814128875732421875,
"raw_values": [
30.216769933700998507219992461614310741424560546875,
30.174476385116999921365277259610593318939208984375,
30.216842651366999206175023573450744152069091796875
],
"test_run_times": [
97.3599999999999994315658113919198513031005859375,
98.8599999999999994315658113919198513031005859375,
98.93999999999999772626324556767940521240234375
]
}
}
},
"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": {
"c6i.4xlarge": {
"value": 70.936000000000007048583938740193843841552734375,
"raw_values": [
70.8700000000000045474735088646411895751953125,
70.993999999999999772626324556767940521240234375,
70.9429999999999978399500832892954349517822265625
],
"test_run_times": [
70.840000000000003410605131648480892181396484375,
70.8700000000000045474735088646411895751953125,
70.9899999999999948840923025272786617279052734375,
70.93999999999999772626324556767940521240234375
],
"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": {
"c6i.4xlarge": {
"value": 62.00500000000000255795384873636066913604736328125,
"raw_values": [
61.93599999999999994315658113919198513031005859375,
62.13199999999999789679350215010344982147216796875,
61.9470000000000027284841053187847137451171875
],
"test_run_times": [
60.97999999999999687361196265555918216705322265625,
61.93999999999999772626324556767940521240234375,
62.13000000000000255795384873636066913604736328125,
61.9500000000000028421709430404007434844970703125
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
}
}
},
"d9548296a130d41521408adebd2110bf646bed60": {
"identifier": "pts\/onednn-3.4.0",
"title": "oneDNN",
"app_version": "3.4",
"arguments": "--rnn --batch=inputs\/rnn\/perf_rnn_training --engine=cpu",
"description": "Harness: Recurrent Neural Network Training - Engine: CPU",
"scale": "ms",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"c6i.4xlarge": {
"value": 2967.6199999999998908606357872486114501953125,
"raw_values": [
2976.67999999999983629095368087291717529296875,
2961.010000000000218278728425502777099609375,
2965.15999999999985448084771633148193359375
],
"min_result": [
"2944.31"
],
"test_run_times": [
82.1099999999999994315658113919198513031005859375,
82.1099999999999994315658113919198513031005859375,
82.18999999999999772626324556767940521240234375
],
"details": {
"compiler-options": {
"compiler-type": "CXX",
"compiler": "g++",
"compiler-options": "-O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl"
}
}
}
}
},
"421721b6ff3ad3e06131ca5667bb283fe7e7415d": {
"identifier": "pts\/onednn-3.4.0",
"title": "oneDNN",
"app_version": "3.4",
"arguments": "--rnn --batch=inputs\/rnn\/perf_rnn_inference_lb --engine=cpu",
"description": "Harness: Recurrent Neural Network Inference - Engine: CPU",
"scale": "ms",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"c6i.4xlarge": {
"value": 1557.470000000000027284841053187847137451171875,
"raw_values": [
1557.09999999999990905052982270717620849609375,
1559.109999999999899955582804977893829345703125,
1556.2100000000000363797880709171295166015625
],
"min_result": [
"1539.33"
],
"test_run_times": [
75.969999999999998863131622783839702606201171875,
75.969999999999998863131622783839702606201171875,
75.8900000000000005684341886080801486968994140625
],
"details": {
"compiler-options": {
"compiler-type": "CXX",
"compiler": "g++",
"compiler-options": "-O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl"
}
}
}
}
},
"6e4a1114ac4c4b97f28942fcfc77f0071864290c": {
"identifier": "pts\/mlpack-1.0.2",
"title": "Mlpack Benchmark",
"arguments": "SCIKIT_LINEARRIDGEREGRESSION",
"description": "Benchmark: scikit_linearridgeregression",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"c6i.4xlarge": {
"value": 2.229999999999999982236431605997495353221893310546875,
"raw_values": [
2.21357774734500001301285010413266718387603759765625,
2.2406206130981001223290149937383830547332763671875,
2.243242740631099962911321199499070644378662109375
],
"test_run_times": [
70.9800000000000039790393202565610408782958984375,
70.719999999999998863131622783839702606201171875,
70.3599999999999994315658113919198513031005859375
]
}
}
},
"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": {
"c6i.4xlarge": {
"value": 50.38000000000000255795384873636066913604736328125,
"raw_values": [
50.53600000000000136424205265939235687255859375,
49.9759999999999990905052982270717620849609375,
50.62700000000000244426701101474463939666748046875
],
"test_run_times": [
50.85000000000000142108547152020037174224853515625,
50.53999999999999914734871708787977695465087890625,
49.97999999999999687361196265555918216705322265625,
50.63000000000000255795384873636066913604736328125
],
"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": {
"c6i.4xlarge": {
"value": 50.2469999999999998863131622783839702606201171875,
"raw_values": [
50.25699999999999789679350215010344982147216796875,
50.1340000000000003410605131648480892181396484375,
50.3509999999999990905052982270717620849609375
],
"test_run_times": [
51.21000000000000085265128291212022304534912109375,
50.25999999999999801048033987171947956085205078125,
50.13000000000000255795384873636066913604736328125,
50.35000000000000142108547152020037174224853515625
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
},
"Intel Xeon Platinum 8375C - EFI VGA - Amazon EC2": {
"value": 49.2999999999999971578290569595992565155029296875,
"raw_values": [
49.2049999999999982946974341757595539093017578125,
49.701999999999998181010596454143524169921875,
48.993999999999999772626324556767940521240234375
],
"test_run_times": [
49.17999999999999971578290569595992565155029296875,
49.2000000000000028421709430404007434844970703125,
49.7000000000000028421709430404007434844970703125,
48.99000000000000198951966012828052043914794921875
],
"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": {
"c6i.4xlarge": {
"value": 48.87299999999999755573298898525536060333251953125,
"raw_values": [
49.22500000000000142108547152020037174224853515625,
48.804000000000002046363078989088535308837890625,
48.590000000000003410605131648480892181396484375
],
"test_run_times": [
48.96000000000000085265128291212022304534912109375,
49.22999999999999687361196265555918216705322265625,
48.7999999999999971578290569595992565155029296875,
48.590000000000003410605131648480892181396484375
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
}
}
},
"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": {
"c6i.4xlarge": {
"test_run_times": [
62.85000000000000142108547152020037174224853515625,
62.11999999999999744204615126363933086395263671875,
64.2300000000000039790393202565610408782958984375
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
},
"error": "The test quit with a non-zero exit status. E: KeyError:
"
}
}
}
},
"082e91ee6f9fd09c616b9f84f2ff189d752ff466": {
"identifier": "pts\/mlpack-1.0.2",
"title": "Mlpack Benchmark",
"arguments": "SCIKIT_ICA",
"description": "Benchmark: scikit_ica",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"c6i.4xlarge": {
"value": 46.03999999999999914734871708787977695465087890625,
"raw_values": [
46.24211835861199659802878159098327159881591796875,
45.8892524242399986178497783839702606201171875,
45.9742767810819970009106327779591083526611328125
],
"test_run_times": [
49.8299999999999982946974341757595539093017578125,
49.35000000000000142108547152020037174224853515625,
49.280000000000001136868377216160297393798828125
]
}
}
},
"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": {
"c6i.4xlarge": {
"value": 18.07300000000000039790393202565610408782958984375,
"raw_values": [
17.971000000000000085265128291212022304534912109375,
18.080999999999999516830939683131873607635498046875,
18.16799999999999926103555480949580669403076171875
],
"test_run_times": [
18.10000000000000142108547152020037174224853515625,
17.969999999999998863131622783839702606201171875,
18.0799999999999982946974341757595539093017578125,
18.1700000000000017053025658242404460906982421875
],
"details": {
"compiler-options": {
"compiler-type": "F9X",
"compiler": "gfortran",
"compiler-options": "-O0"
}
}
}
}
},
"d99f55e37cf79cb02f548b43c73d0851e1d39fea": {
"identifier": "pts\/mlpack-1.0.2",
"title": "Mlpack Benchmark",
"arguments": "SCIKIT_SVM",
"description": "Benchmark: scikit_svm",
"scale": "Seconds",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"c6i.4xlarge": {
"value": 13.5800000000000000710542735760100185871124267578125,
"raw_values": [
13.3560738563539995737983190338127315044403076171875,
13.4769227504729993682985877967439591884613037109375,
13.9147017002109993910607954603619873523712158203125
],
"test_run_times": [
18.199999999999999289457264239899814128875732421875,
18.239999999999998436805981327779591083526611328125,
18.67999999999999971578290569595992565155029296875
]
}
}
},
"8f240080f4a9e0a0dc2c41137a7d1437c7ffdcbc": {
"identifier": "pts\/onednn-3.4.0",
"title": "oneDNN",
"app_version": "3.4",
"arguments": "--ip --batch=inputs\/ip\/shapes_1d --engine=cpu",
"description": "Harness: IP Shapes 1D - Engine: CPU",
"scale": "ms",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"c6i.4xlarge": {
"value": 2.011019999999999807727135703316889703273773193359375,
"raw_values": [
2.045230000000000103455022326670587062835693359375,
1.9868799999999999794653149365331046283245086669921875,
2.00096000000000007190692485892213881015777587890625
],
"min_result": [
"1.92"
],
"test_run_times": [
15.410000000000000142108547152020037174224853515625,
15.1699999999999999289457264239899814128875732421875,
15.160000000000000142108547152020037174224853515625
],
"details": {
"compiler-options": {
"compiler-type": "CXX",
"compiler": "g++",
"compiler-options": "-O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl"
}
}
}
}
},
"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": {
"c6i.4xlarge": {
"test_run_times": [
16.489999999999998436805981327779591083526611328125,
14.0299999999999993605115378159098327159881591796875,
13.9700000000000006394884621840901672840118408203125
],
"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"
}
}
}
},
"71fb2672401eac9e14360f0d24b0dac568d4e27d": {
"identifier": "pts\/onednn-3.4.0",
"title": "oneDNN",
"app_version": "3.4",
"arguments": "--ip --batch=inputs\/ip\/shapes_3d --engine=cpu",
"description": "Harness: IP Shapes 3D - Engine: CPU",
"scale": "ms",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"c6i.4xlarge": {
"value": 2.94925000000000014921397450962103903293609619140625,
"raw_values": [
2.936170000000000168682845469447784125804901123046875,
2.947830000000000172377667695400305092334747314453125,
2.963740000000000041069370126933790743350982666015625
],
"min_result": [
"2.78"
],
"test_run_times": [
9.4399999999999995026200849679298698902130126953125,
9.4399999999999995026200849679298698902130126953125,
9.449999999999999289457264239899814128875732421875
],
"details": {
"compiler-options": {
"compiler-type": "CXX",
"compiler": "g++",
"compiler-options": "-O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl"
}
}
}
}
},
"0ef6103275fbe37507d64e861136b8757d7fa5c6": {
"identifier": "pts\/onednn-3.4.0",
"title": "oneDNN",
"app_version": "3.4",
"arguments": "--conv --batch=inputs\/conv\/shapes_auto --engine=cpu",
"description": "Harness: Convolution Batch Shapes Auto - Engine: CPU",
"scale": "ms",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"c6i.4xlarge": {
"value": 3.9716599999999999681676854379475116729736328125,
"raw_values": [
3.944879999999999942161821309127844870090484619140625,
3.975789999999999935198502498678863048553466796875,
3.9943200000000000926547727431170642375946044921875
],
"min_result": [
"3.74"
],
"test_run_times": [
6.4900000000000002131628207280300557613372802734375,
6.2599999999999997868371792719699442386627197265625,
6.2599999999999997868371792719699442386627197265625
],
"details": {
"compiler-options": {
"compiler-type": "CXX",
"compiler": "g++",
"compiler-options": "-O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl"
}
}
}
}
},
"b98add05d66e1331f141db44b09d1130a8b17f20": {
"identifier": "pts\/onednn-3.4.0",
"title": "oneDNN",
"app_version": "3.4",
"arguments": "--deconv --batch=inputs\/deconv\/shapes_3d --engine=cpu",
"description": "Harness: Deconvolution Batch shapes_3d - Engine: CPU",
"scale": "ms",
"proportion": "LIB",
"display_format": "BAR_GRAPH",
"results": {
"c6i.4xlarge": {
"value": 4.3906200000000001892885848064906895160675048828125,
"raw_values": [
4.39580999999999999516830939683131873607635498046875,
4.38388000000000044309445001999847590923309326171875,
4.39215999999999962000174491549842059612274169921875
],
"min_result": [
"4.36"
],
"test_run_times": [
3.060000000000000053290705182007513940334320068359375,
3.060000000000000053290705182007513940334320068359375,
3.060000000000000053290705182007513940334320068359375
],
"details": {
"compiler-options": {
"compiler-type": "CXX",
"compiler": "g++",
"compiler-options": "-O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl"
}
}
}
}
},
"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": {
"c6i.4xlarge": {
"test_run_times": [
1,
1,
1.0100000000000000088817841970012523233890533447265625
],
"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"
}
}
}
},
"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": {
"c6i.4xlarge": {
"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'"
}
}
}
}
}
}