c6i.4xlarge

c6i.4xlarge

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
Jump To Table - Results

View

Do Not Show Noisy Results
Do Not Show Results With Incomplete Data
Do Not Show Results With Little Change/Spread
List Notable Results
Show Result Confidence Charts
Allow Limiting Results To Certain Suite(s)

Statistics

Show Overall Harmonic Mean(s)
Show Overall Geometric Mean
Show Wins / Losses Counts (Pie Chart)
Normalize Results
Remove Outliers Before Calculating Averages

Graph Settings

Force Line Graphs Where Applicable
Convert To Scalar Where Applicable
Disable Color Branding
Prefer Vertical Bar Graphs

Multi-Way Comparison

Condense Multi-Option Tests Into Single Result Graphs

Table

Show Detailed System Result Table

Run Management

Highlight
Result
Toggle/Hide
Result
Result
Identifier
View Logs
Performance Per
Dollar
Date
Run
  Test
  Duration
c6i.4xlarge
June 21
  13 Hours, 57 Minutes
Intel Xeon Platinum 8375C - EFI VGA - Amazon EC2
June 24
  4 Minutes
Invert Behavior (Only Show Selected Data)
  7 Hours
Only show results matching title/arguments (delimit multiple options with a comma):
Do not show results matching title/arguments (delimit multiple options with a comma):


c6i.4xlargeOpenBenchmarking.orgPhoronix Test SuiteIntel Xeon Platinum 8375C (8 Cores / 16 Threads)Amazon EC2 c6i.4xlarge (1.0 BIOS)Intel 440FX 82441FX PMC1 x 32 GB DDR4-3200MT/s215GB Amazon Elastic Block StoreEFI VGAAmazon ElasticUbuntu 22.046.5.0-1020-aws (x86_64)1.3.255GCC 11.4.0ext4800x600amazonProcessorMotherboardChipsetMemoryDiskGraphicsNetworkOSKernelVulkanCompilerFile-SystemScreen ResolutionSystem LayerC6i.4xlarge BenchmarksSystem Logs- Transparent Huge Pages: madvise- --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- Python 3.11.9- 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

c6i.4xlargescikit-learn: Isotonic / Perturbed Logarithmscikit-learn: Isotonic / Logisticscikit-learn: SAGAscikit-learn: Sparse Rand Projections / 100 Iterationsscikit-learn: Covertype Dataset Benchmarkscikit-learn: Lassoscikit-learn: Isolation Forestscikit-learn: SGDOneClassSVMscikit-learn: TSNE MNIST Datasetscikit-learn: GLMscikit-learn: Plot Lasso Pathscikit-learn: Plot Hierarchicalscikit-learn: Kernel PCA Solvers / Time vs. N Samplesscikit-learn: Plot Polynomial Kernel Approximationscikit-learn: Feature Expansionsscikit-learn: Plot Neighborsscikit-learn: Plot Fast KMeansscikit-learn: Sample Without Replacementscikit-learn: Hist Gradient Boosting Higgs Bosonscikit-learn: SGD Regressionscikit-learn: Sparsifyscikit-learn: Hist Gradient Boostingscikit-learn: Hist Gradient Boosting Adultscikit-learn: MNIST Datasetscikit-learn: Hist Gradient Boosting Threadingscikit-learn: Kernel PCA Solvers / Time vs. N Componentsscikit-learn: Plot Incremental PCAonednn: Deconvolution Batch shapes_1d - CPUscikit-learn: LocalOutlierFactorscikit-learn: Plot Wardmlpack: scikit_qdascikit-learn: Text Vectorizersscikit-learn: Plot Singular Value Decompositiononednn: Recurrent Neural Network Training - CPUonednn: Recurrent Neural Network Inference - CPUmlpack: scikit_linearridgeregressionscikit-learn: Treescikit-learn: 20 Newsgroups / Logistic Regressionscikit-learn: Plot OMP vs. LARSmlpack: scikit_icascikit-learn: Hist Gradient Boosting Categorical Onlymlpack: scikit_svmonednn: IP Shapes 1D - CPUonednn: IP Shapes 3D - CPUonednn: Convolution Batch Shapes Auto - CPUonednn: Deconvolution Batch shapes_3d - CPUscikit-learn: Glmnetc6i.4xlargeIntel Xeon Platinum 8375C - EFI VGA - Amazon EC22252.4101885.9511191.571852.988483.167405.473349.905297.026318.777268.268262.867259.189258.399225.038222.478204.786199.025153.21054.544125.254106.328104.03190.72185.56095.31490.35877.4147.7388975.2574.63330.2070.93662.0052967.621557.472.2350.38050.24748.87346.0418.07313.582.011022.949253.971664.3906249.300OpenBenchmarking.org

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Isotonic / Perturbed Logarithmc6i.4xlarge5001000150020002500SE +/- 7.47, N = 32252.411. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Isotonic / Logisticc6i.4xlarge400800120016002000SE +/- 8.47, N = 31885.951. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: SAGAc6i.4xlarge30060090012001500SE +/- 1.80, N = 31191.571. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Sparse Random Projections / 100 Iterationsc6i.4xlarge2004006008001000SE +/- 5.20, N = 3852.991. (F9X) gfortran options: -O0

Benchmark: Isotonic / Pathological

c6i.4xlarge: The test quit with a non-zero exit status.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Covertype Dataset Benchmarkc6i.4xlarge100200300400500SE +/- 1.75, N = 3483.171. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Lassoc6i.4xlarge90180270360450SE +/- 3.54, N = 3405.471. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Isolation Forestc6i.4xlarge80160240320400SE +/- 0.82, N = 3349.911. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: SGDOneClassSVMc6i.4xlarge60120180240300SE +/- 0.38, N = 3297.031. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: TSNE MNIST Datasetc6i.4xlarge70140210280350SE +/- 0.31, N = 3318.781. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: GLMc6i.4xlarge60120180240300SE +/- 0.45, N = 3268.271. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Lasso Pathc6i.4xlarge60120180240300SE +/- 0.86, N = 3262.871. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Hierarchicalc6i.4xlarge60120180240300SE +/- 1.56, N = 3259.191. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Kernel PCA Solvers / Time vs. N Samplesc6i.4xlarge60120180240300SE +/- 0.31, N = 3258.401. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Polynomial Kernel Approximationc6i.4xlarge50100150200250SE +/- 2.40, N = 3225.041. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Feature Expansionsc6i.4xlarge50100150200250SE +/- 0.08, N = 3222.481. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Neighborsc6i.4xlarge4080120160200SE +/- 0.88, N = 3204.791. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Fast KMeansc6i.4xlarge4080120160200SE +/- 0.99, N = 3199.031. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Sample Without Replacementc6i.4xlarge306090120150SE +/- 0.75, N = 3153.211. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Hist Gradient Boosting Higgs Bosonc6i.4xlarge1224364860SE +/- 0.14, N = 354.541. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: SGD Regressionc6i.4xlarge306090120150SE +/- 0.45, N = 3125.251. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Sparsifyc6i.4xlarge20406080100SE +/- 0.07, N = 3106.331. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Hist Gradient Boostingc6i.4xlarge20406080100SE +/- 0.21, N = 3104.031. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Hist Gradient Boosting Adultc6i.4xlarge20406080100SE +/- 0.08, N = 390.721. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: MNIST Datasetc6i.4xlarge20406080100SE +/- 0.07, N = 385.561. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Hist Gradient Boosting Threadingc6i.4xlarge20406080100SE +/- 0.74, N = 395.311. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Kernel PCA Solvers / Time vs. N Componentsc6i.4xlarge20406080100SE +/- 0.63, N = 390.361. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Incremental PCAc6i.4xlarge20406080100SE +/- 0.32, N = 377.411. (F9X) gfortran options: -O0

oneDNN

This is a test of the Intel oneDNN as an Intel-optimized library for Deep Neural Networks and making use of its built-in benchdnn functionality. The result is the total perf time reported. Intel oneDNN was formerly known as DNNL (Deep Neural Network Library) and MKL-DNN before being rebranded as part of the Intel oneAPI toolkit. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 3.4Harness: Deconvolution Batch shapes_1d - Engine: CPUc6i.4xlarge246810SE +/- 0.14831, N = 157.73889MIN: 5.31. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: LocalOutlierFactorc6i.4xlarge20406080100SE +/- 0.14, N = 375.251. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Wardc6i.4xlarge20406080100SE +/- 0.02, N = 374.631. (F9X) gfortran options: -O0

Mlpack Benchmark

Mlpack benchmark scripts for machine learning libraries Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterMlpack BenchmarkBenchmark: scikit_qdac6i.4xlarge714212835SE +/- 0.01, N = 330.20

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Text Vectorizersc6i.4xlarge1632486480SE +/- 0.04, N = 370.941. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot Singular Value Decompositionc6i.4xlarge1428425670SE +/- 0.06, N = 362.011. (F9X) gfortran options: -O0

oneDNN

This is a test of the Intel oneDNN as an Intel-optimized library for Deep Neural Networks and making use of its built-in benchdnn functionality. The result is the total perf time reported. Intel oneDNN was formerly known as DNNL (Deep Neural Network Library) and MKL-DNN before being rebranded as part of the Intel oneAPI toolkit. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 3.4Harness: Recurrent Neural Network Training - Engine: CPUc6i.4xlarge6001200180024003000SE +/- 4.69, N = 32967.62MIN: 2944.311. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 3.4Harness: Recurrent Neural Network Inference - Engine: CPUc6i.4xlarge30060090012001500SE +/- 0.86, N = 31557.47MIN: 1539.331. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl

Mlpack Benchmark

Mlpack benchmark scripts for machine learning libraries Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterMlpack BenchmarkBenchmark: scikit_linearridgeregressionc6i.4xlarge0.50181.00361.50542.00722.509SE +/- 0.01, N = 32.23

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Treec6i.4xlarge1122334455SE +/- 0.20, N = 350.381. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: 20 Newsgroups / Logistic RegressionIntel Xeon Platinum 8375C - EFI VGA - Amazon EC2c6i.4xlarge1122334455SE +/- 0.21, N = 3SE +/- 0.06, N = 349.3050.251. (F9X) gfortran options: -O0

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Plot OMP vs. LARSc6i.4xlarge1122334455SE +/- 0.19, N = 348.871. (F9X) gfortran options: -O0

Benchmark: Plot Non-Negative Matrix Factorization

c6i.4xlarge: The test quit with a non-zero exit status. E: KeyError:

Mlpack Benchmark

Mlpack benchmark scripts for machine learning libraries Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterMlpack BenchmarkBenchmark: scikit_icac6i.4xlarge1020304050SE +/- 0.11, N = 346.04

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Hist Gradient Boosting Categorical Onlyc6i.4xlarge48121620SE +/- 0.06, N = 318.071. (F9X) gfortran options: -O0

Mlpack Benchmark

Mlpack benchmark scripts for machine learning libraries Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterMlpack BenchmarkBenchmark: scikit_svmc6i.4xlarge3691215SE +/- 0.17, N = 313.58

oneDNN

This is a test of the Intel oneDNN as an Intel-optimized library for Deep Neural Networks and making use of its built-in benchdnn functionality. The result is the total perf time reported. Intel oneDNN was formerly known as DNNL (Deep Neural Network Library) and MKL-DNN before being rebranded as part of the Intel oneAPI toolkit. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 3.4Harness: IP Shapes 1D - Engine: CPUc6i.4xlarge0.45250.9051.35751.812.2625SE +/- 0.01758, N = 32.01102MIN: 1.921. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

Benchmark: RCV1 Logreg Convergencet

c6i.4xlarge: The test quit with a non-zero exit status. E: IndexError: list index out of range

oneDNN

This is a test of the Intel oneDNN as an Intel-optimized library for Deep Neural Networks and making use of its built-in benchdnn functionality. The result is the total perf time reported. Intel oneDNN was formerly known as DNNL (Deep Neural Network Library) and MKL-DNN before being rebranded as part of the Intel oneAPI toolkit. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 3.4Harness: IP Shapes 3D - Engine: CPUc6i.4xlarge0.66361.32721.99082.65443.318SE +/- 0.00799, N = 32.94925MIN: 2.781. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 3.4Harness: Convolution Batch Shapes Auto - Engine: CPUc6i.4xlarge0.89361.78722.68083.57444.468SE +/- 0.01442, N = 33.97166MIN: 3.741. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 3.4Harness: Deconvolution Batch shapes_3d - Engine: CPUc6i.4xlarge0.98791.97582.96373.95164.9395SE +/- 0.00353, N = 34.39062MIN: 4.361. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

Benchmark: Plot Parallel Pairwise

c6i.4xlarge: 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

Benchmark: Glmnet

c6i.4xlarge: The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'glmnet'