c6i.4xlarge

c6i.4xlarge

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c6i.4xlarge
June 21
  14 Hours
Intel Xeon Platinum 8375C - EFI VGA - Amazon EC2
June 24
  4 Minutes
test
June 26
  1 Minute
ttessss
June 26
  1 Minute
ewg adg
June 26
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dfdasf
June 26
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c6i.4xlarge123
June 27
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ddd
June 27
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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- c6i.4xlarge: Python 3.11.9- Intel Xeon Platinum 8375C - EFI VGA - Amazon EC2: Python 3.11.9- test: Python 3.11.9- ttessss: Python 3.12.4- ewg adg: Python 3.11.9- dfdasf: Python 3.11.9- c6i.4xlarge123: Python 3.9.19- ddd: Python 3.9.19- 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: Plot OMP vs. LARSllama-cpp: Meta-Llama-3-8B-Instruct-Q8_0.ggufmlpack: scikit_icascikit-learn: Hist Gradient Boosting Categorical Onlymlpack: scikit_svmscikit-learn: 20 Newsgroups / Logistic Regressiononednn: 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 EC2testttessssewg adgdfdasfc6i.4xlarge123ddd2252.4101885.9511191.571852.988483.167405.473349.905297.026318.777268.268262.867259.189258.399225.038222.478204.786199.025153.21054.544125.254106.328104.03190.72185.56095.31490.35877.4147.7388975.2574.63330.2070.93662.0052967.621557.472.2350.38048.8739.6446.0418.07313.5850.2472.011022.949253.971664.3906249.300OpenBenchmarking.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: 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:

Llama.cpp

OpenBenchmarking.orgTokens Per Second, More Is BetterLlama.cpp b3067Model: Meta-Llama-3-8B-Instruct-Q8_0.ggufc6i.4xlarge3691215SE +/- 0.03, N = 39.641. (CXX) g++ options: -std=c++11 -fPIC -O3 -pthread -march=native -mtune=native -lopenblas

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

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: 20 Newsgroups / Logistic Regressionc6i.4xlargeIntel Xeon Platinum 8375C - EFI VGA - Amazon EC21122334455SE +/- 0.06, N = 3SE +/- 0.21, N = 350.2549.301. (F9X) gfortran options: -O0

Benchmark: 20 Newsgroups / Logistic Regression

test: The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'numpy._core'

ttessss: The test quit with a non-zero exit status. E: ImportError: /lib/x86_64-linux-gnu/liblapack.so.3: undefined symbol: gotoblas

ewg adg: The test quit with a non-zero exit status. E: ImportError: /lib/x86_64-linux-gnu/liblapack.so.3: undefined symbol: gotoblas

dfdasf: The test quit with a non-zero exit status. E: ImportError: /lib/x86_64-linux-gnu/liblapack.so.3: undefined symbol: gotoblas

c6i.4xlarge123: The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'numpy._core'

ddd: The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'numpy._core'

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'