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
c6i.4xlarge
c6i.4xlarge
,,"c6i.4xlarge","Intel Xeon Platinum 8375C - EFI VGA - Amazon EC2"
Processor,,Intel Xeon Platinum 8375C (8 Cores / 16 Threads),Intel Xeon Platinum 8375C (8 Cores / 16 Threads)
Motherboard,,Amazon EC2 c6i.4xlarge (1.0 BIOS),Amazon EC2 c6i.4xlarge (1.0 BIOS)
Chipset,,Intel 440FX 82441FX PMC,Intel 440FX 82441FX PMC
Memory,,1 x 32 GB DDR4-3200MT/s,1 x 32 GB DDR4-3200MT/s
Disk,,215GB Amazon Elastic Block Store,215GB Amazon Elastic Block Store
Graphics,,EFI VGA,EFI VGA
Network,,Amazon Elastic,Amazon Elastic
OS,,Ubuntu 22.04,Ubuntu 22.04
Kernel,,6.5.0-1020-aws (x86_64),6.5.0-1020-aws (x86_64)
Vulkan,,1.3.255,1.3.255
Compiler,,GCC 11.4.0,GCC 11.4.0
File-System,,ext4,ext4
Screen Resolution,,800x600,800x600
System Layer,,amazon,amazon
,,"c6i.4xlarge","Intel Xeon Platinum 8375C - EFI VGA - Amazon EC2"
"Mlpack Benchmark - Benchmark: scikit_ica (sec)",LIB,46.04,
"Mlpack Benchmark - Benchmark: scikit_qda (sec)",LIB,30.20,
"Mlpack Benchmark - Benchmark: scikit_svm (sec)",LIB,13.58,
"Mlpack Benchmark - Benchmark: scikit_linearridgeregression (sec)",LIB,2.23,
"oneDNN - Harness: Convolution Batch Shapes Auto - Engine: CPU (ms)",LIB,3.97166,
"oneDNN - Harness: IP Shapes 1D - Engine: CPU (ms)",LIB,2.01102,
"oneDNN - Harness: IP Shapes 3D - Engine: CPU (ms)",LIB,2.94925,
"oneDNN - Harness: Deconvolution Batch shapes_1d - Engine: CPU (ms)",LIB,7.73889,
"oneDNN - Harness: Deconvolution Batch shapes_3d - Engine: CPU (ms)",LIB,4.39062,
"oneDNN - Harness: Recurrent Neural Network Training - Engine: CPU (ms)",LIB,2967.62,
"oneDNN - Harness: Recurrent Neural Network Inference - Engine: CPU (ms)",LIB,1557.47,
"Scikit-Learn - Benchmark: 20 Newsgroups / Logistic Regression (sec)",LIB,50.247,49.300
"Scikit-Learn - Benchmark: GLM (sec)",LIB,268.268,
"Scikit-Learn - Benchmark: SAGA (sec)",LIB,1191.571,
"Scikit-Learn - Benchmark: Tree (sec)",LIB,50.380,
"Scikit-Learn - Benchmark: Lasso (sec)",LIB,405.473,
"Scikit-Learn - Benchmark: Glmnet (sec)",LIB,,
"Scikit-Learn - Benchmark: Sparsify (sec)",LIB,106.328,
"Scikit-Learn - Benchmark: Plot Ward (sec)",LIB,74.633,
"Scikit-Learn - Benchmark: MNIST Dataset (sec)",LIB,85.560,
"Scikit-Learn - Benchmark: Plot Neighbors (sec)",LIB,204.786,
"Scikit-Learn - Benchmark: SGD Regression (sec)",LIB,125.254,
"Scikit-Learn - Benchmark: SGDOneClassSVM (sec)",LIB,297.026,
"Scikit-Learn - Benchmark: Plot Lasso Path (sec)",LIB,262.867,
"Scikit-Learn - Benchmark: Isolation Forest (sec)",LIB,349.905,
"Scikit-Learn - Benchmark: Plot Fast KMeans (sec)",LIB,199.025,
"Scikit-Learn - Benchmark: Text Vectorizers (sec)",LIB,70.936,
"Scikit-Learn - Benchmark: Plot Hierarchical (sec)",LIB,259.189,
"Scikit-Learn - Benchmark: Plot OMP vs. LARS (sec)",LIB,48.873,
"Scikit-Learn - Benchmark: Feature Expansions (sec)",LIB,222.478,
"Scikit-Learn - Benchmark: LocalOutlierFactor (sec)",LIB,75.25,
"Scikit-Learn - Benchmark: TSNE MNIST Dataset (sec)",LIB,318.777,
"Scikit-Learn - Benchmark: Isotonic / Logistic (sec)",LIB,1885.951,
"Scikit-Learn - Benchmark: Plot Incremental PCA (sec)",LIB,77.414,
"Scikit-Learn - Benchmark: Hist Gradient Boosting (sec)",LIB,104.031,
"Scikit-Learn - Benchmark: Plot Parallel Pairwise (sec)",LIB,,
"Scikit-Learn - Benchmark: Isotonic / Pathological (sec)",LIB,,
"Scikit-Learn - Benchmark: RCV1 Logreg Convergencet (sec)",LIB,,
"Scikit-Learn - Benchmark: Sample Without Replacement (sec)",LIB,153.210,
"Scikit-Learn - Benchmark: Covertype Dataset Benchmark (sec)",LIB,483.167,
"Scikit-Learn - Benchmark: Hist Gradient Boosting Adult (sec)",LIB,90.721,
"Scikit-Learn - Benchmark: Isotonic / Perturbed Logarithm (sec)",LIB,2252.410,
"Scikit-Learn - Benchmark: Hist Gradient Boosting Threading (sec)",LIB,95.314,
"Scikit-Learn - Benchmark: Plot Singular Value Decomposition (sec)",LIB,62.005,
"Scikit-Learn - Benchmark: Hist Gradient Boosting Higgs Boson (sec)",LIB,54.544,
"Scikit-Learn - Benchmark: Plot Polynomial Kernel Approximation (sec)",LIB,225.038,
"Scikit-Learn - Benchmark: Plot Non-Negative Matrix Factorization (sec)",LIB,,
"Scikit-Learn - Benchmark: Hist Gradient Boosting Categorical Only (sec)",LIB,18.073,
"Scikit-Learn - Benchmark: Kernel PCA Solvers / Time vs. N Samples (sec)",LIB,258.399,
"Scikit-Learn - Benchmark: Kernel PCA Solvers / Time vs. N Components (sec)",LIB,90.358,
"Scikit-Learn - Benchmark: Sparse Random Projections / 100 Iterations (sec)",LIB,852.988,