slehpc-wk1-ML-05sep2020

VMware testing on SUSE Linux Enterprise High Performance Computing 15 SP2 15.2 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 2009068-NI-SLEHPCWK174
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slehpc-wk1-ML-05sep2020
September 06 2020
  4 Hours, 10 Minutes
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slehpc-wk1-ML-05sep2020 Suite 1.0.0 System Test suite extracted from slehpc-wk1-ML-05sep2020. pts/numenta-nab-1.1.0 -d expose Detector: EXPoSE pts/plaidml-1.0.4 --no-fp16 --no-train resnet50 CPU FP16: No - Mode: Inference - Network: ResNet 50 - Device: CPU pts/numpy-1.2.0 pts/onednn-1.5.0 --rnn --batch=inputs/rnn/rnn_inference --cfg=f32 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU pts/numenta-nab-1.1.0 -d earthgeckoSkyline Detector: Earthgecko Skyline pts/plaidml-1.0.4 --no-fp16 --no-train vgg16 CPU FP16: No - Mode: Inference - Network: VGG16 - Device: CPU pts/onednn-1.5.0 --deconv --batch=inputs/deconv/deconv_1d --cfg=u8s8f32 --engine=cpu Harness: Deconvolution Batch deconv_1d - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.5.0 --deconv --batch=inputs/deconv/deconv_1d --cfg=f32 --engine=cpu Harness: Deconvolution Batch deconv_1d - Data Type: f32 - Engine: CPU pts/tensorflow-lite-1.0.0 --graph=inception_v4.tflite Model: Inception V4 pts/numenta-nab-1.1.0 -d relativeEntropy Detector: Relative Entropy pts/tensorflow-lite-1.0.0 --graph=inception_resnet_v2.tflite Model: Inception ResNet V2 pts/onednn-1.5.0 --ip --batch=inputs/ip/ip_1d --cfg=f32 --engine=cpu Harness: IP Batch 1D - Data Type: f32 - Engine: CPU pts/onednn-1.5.0 --ip --batch=inputs/ip/ip_1d --cfg=u8s8f32 --engine=cpu Harness: IP Batch 1D - Data Type: u8s8f32 - Engine: CPU pts/tensorflow-lite-1.0.0 --graph=nasnet_mobile.tflite Model: NASNet Mobile pts/tensorflow-lite-1.0.0 --graph=squeezenet.tflite Model: SqueezeNet pts/tensorflow-lite-1.0.0 --graph=mobilenet_v1_1.0_224.tflite Model: Mobilenet Float pts/onednn-1.5.0 --matmul --batch=inputs/matmul/shapes_transformer --cfg=f32 --engine=cpu Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU pts/tensorflow-lite-1.0.0 --graph=mobilenet_v1_1.0_224_quant.tflite Model: Mobilenet Quant pts/onednn-1.5.0 --ip --batch=inputs/ip/ip_all --cfg=f32 --engine=cpu Harness: IP Batch All - Data Type: f32 - Engine: CPU pts/onednn-1.5.0 --ip --batch=inputs/ip/ip_all --cfg=u8s8f32 --engine=cpu Harness: IP Batch All - Data Type: u8s8f32 - Engine: CPU pts/deepspeech-1.0.0 pts/onednn-1.5.0 --rnn --batch=inputs/rnn/rnn_training --cfg=f32 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU pts/numenta-nab-1.1.0 -d bayesChangePt Detector: Bayesian Changepoint pts/onednn-1.5.0 --deconv --batch=inputs/deconv/deconv_3d --cfg=u8s8f32 --engine=cpu Harness: Deconvolution Batch deconv_3d - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.5.0 --matmul --batch=inputs/matmul/shapes_transformer --cfg=u8s8f32 --engine=cpu Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU pts/numenta-nab-1.1.0 -d windowedGaussian Detector: Windowed Gaussian pts/onednn-1.5.0 --conv --batch=inputs/conv/shapes_auto --cfg=u8s8f32 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.5.0 --conv --batch=inputs/conv/shapes_auto --cfg=f32 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU pts/onednn-1.5.0 --deconv --batch=inputs/deconv/deconv_3d --cfg=f32 --engine=cpu Harness: Deconvolution Batch deconv_3d - Data Type: f32 - Engine: CPU