ubu20-wk1-ML-05sep2020

VMware testing on Ubuntu 20.04 via the Phoronix Test Suite.

Compare your own system(s) to this result file with the Phoronix Test Suite by running the command: phoronix-test-suite benchmark 2009061-NE-UBU20WK1M26
Jump To Table - Results

Statistics

Remove Outliers Before Calculating Averages

Graph Settings

Prefer Vertical Bar Graphs

Multi-Way Comparison

Condense Multi-Option Tests Into Single Result Graphs

Table

Show Detailed System Result Table

Run Management

Result
Identifier
View Logs
Performance Per
Dollar
Date
Run
  Test
  Duration
ubu20-wk1-ML-05sep2020
September 06 2020
  4 Hours, 27 Minutes
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):

ubu20-wk1-ML-05sep2020 VMware testing on Ubuntu 20.04 via the Phoronix Test Suite. ,,"ubu20-wk1-ML-05sep2020" Processor,,16 x AMD Ryzen Threadripper 3960X 24-Core (31 Cores) Motherboard,,Intel 440BX (6.00 BIOS) Chipset,,Intel 440BX/ZX/DX Memory,,16GB Disk,,193GB VMware Virtual S Graphics,,SVGA3D; build: RELEASE; LLVM; Audio,,Ensoniq ES1371/ES1373 Network,,Intel 82545EM + 4 x AMD 79c970 OS,,Ubuntu 20.04 Kernel,,5.4.0-45-generic (x86_64) Desktop,,GNOME Shell 3.36.4 Display Server,,X Server 1.20.8 Display Driver,,modesetting 1.20.8 OpenGL,,2.1 Mesa 20.0.8 Compiler,,GCC 9.3.0 File-System,,ext4 Screen Resolution,,1680x968 System Layer,,VMware ,,"ubu20-wk1-ML-05sep2020" "oneDNN - Harness: IP Batch 1D - Data Type: f32 - Engine: CPU (ms)",LIB,6.68402 "oneDNN - Harness: IP Batch All - Data Type: f32 - Engine: CPU (ms)",LIB,78.4388 "oneDNN - Harness: IP Batch 1D - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,3.80789 "oneDNN - Harness: IP Batch All - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,43.8748 "oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU (ms)",LIB,15.7514 "oneDNN - Harness: Deconvolution Batch deconv_1d - Data Type: f32 - Engine: CPU (ms)",LIB,6.02825 "oneDNN - Harness: Deconvolution Batch deconv_3d - Data Type: f32 - Engine: CPU (ms)",LIB,9.92993 "oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,16.5838 "oneDNN - Harness: Deconvolution Batch deconv_1d - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,7.91315 "oneDNN - Harness: Deconvolution Batch deconv_3d - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,6.16964 "oneDNN - Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU (ms)",LIB,381.475 "oneDNN - Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU (ms)",LIB,97.9147 "oneDNN - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU (ms)",LIB,1.84022 "oneDNN - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,2.90286 "Numpy Benchmark - (Score)",HIB,345.45 "DeepSpeech - (sec)",LIB,60.79795 "TensorFlow Lite - Model: SqueezeNet (us)",LIB,155523 "TensorFlow Lite - Model: Inception V4 (us)",LIB,2127763 "TensorFlow Lite - Model: NASNet Mobile (us)",LIB,161302 "TensorFlow Lite - Model: Mobilenet Float (us)",LIB,105987 "TensorFlow Lite - Model: Mobilenet Quant (us)",LIB,112000 "TensorFlow Lite - Model: Inception ResNet V2 (us)",LIB,1909437 "PlaidML - FP16: No - Mode: Inference - Network: VGG16 - Device: CPU (FPS)",HIB,16.65 "PlaidML - FP16: No - Mode: Inference - Network: ResNet 50 - Device: CPU (FPS)",HIB,6.10 "Numenta Anomaly Benchmark - Detector: EXPoSE (sec)",LIB,800.854 "Numenta Anomaly Benchmark - Detector: Relative Entropy (sec)",LIB,16.980 "Numenta Anomaly Benchmark - Detector: Windowed Gaussian (sec)",LIB,9.223 "Numenta Anomaly Benchmark - Detector: Earthgecko Skyline (sec)",LIB,97.088 "Numenta Anomaly Benchmark - Detector: Bayesian Changepoint (sec)",LIB,29.968 "Mlpack Benchmark - Benchmark: scikit_ica (sec)",LIB,54.84 "Mlpack Benchmark - Benchmark: scikit_qda (sec)",LIB,60.99 "Mlpack Benchmark - Benchmark: scikit_svm (sec)",LIB,21.29 "Mlpack Benchmark - Benchmark: scikit_linearridgeregression (sec)",LIB,2.71 "Scikit-Learn - (sec)",LIB,9.024