MBP M1 Max Machine Learning, sys76-kudu-ML

Apple M1 Max testing with a Apple MacBook Pro and Apple M1 Max on macOS 12.1 via the Phoronix Test Suite.

sys76-kudu-ML: AMD Ryzen 9 5900HX testing with a System76 Kudu (1.07.09RSA1 BIOS) and AMD Cezanne on Pop 21.10 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 2202161-NE-MBPM1MAXM40
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BLAS (Basic Linear Algebra Sub-Routine) Tests 2 Tests
CPU Massive 7 Tests
Creator Workloads 4 Tests
HPC - High Performance Computing 20 Tests
Machine Learning 20 Tests
Multi-Core 2 Tests
NVIDIA GPU Compute 4 Tests
Intel oneAPI 2 Tests
Python 3 Tests
Server CPU Tests 3 Tests
Single-Threaded 3 Tests
Speech 2 Tests
Telephony 2 Tests

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MBP M1 Max Machine Learning
February 16 2022
  6 Hours, 21 Minutes
ML Tests
February 15 2022
  7 Hours, 15 Minutes
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  6 Hours, 48 Minutes
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MBP M1 Max Machine Learning, sys76-kudu-ML Suite 1.0.0 System Test suite extracted from MBP M1 Max Machine Learning, sys76-kudu-ML. pts/ncnn-1.3.0 Target: Vulkan GPU - Model: alexnet pts/mnn-1.3.0 Model: MobileNetV2_224 pts/ncnn-1.3.0 Target: Vulkan GPU - Model: resnet50 pts/ncnn-1.3.0 Target: Vulkan GPU - Model: googlenet pts/ncnn-1.3.0 Target: Vulkan GPU - Model: resnet18 pts/ncnn-1.3.0 -1 Target: CPU - Model: alexnet pts/ncnn-1.3.0 Target: Vulkan GPU - Model: mobilenet pts/ncnn-1.3.0 -1 Target: CPU - Model: googlenet pts/ncnn-1.3.0 -1 Target: CPU - Model: resnet50 pts/ncnn-1.3.0 -1 Target: CPU - Model: efficientnet-b0 pts/ncnn-1.3.0 -1 Target: CPU - Model: mnasnet pts/ncnn-1.3.0 Target: Vulkan GPU - Model: yolov4-tiny pts/ncnn-1.3.0 Target: Vulkan GPU - Model: vgg16 pts/ncnn-1.3.0 Target: Vulkan GPU - Model: mnasnet pts/ncnn-1.3.0 -1 Target: CPU - Model: blazeface pts/ncnn-1.3.0 Target: Vulkan GPU-v2-v2 - Model: mobilenet-v2 pts/ncnn-1.3.0 Target: Vulkan GPU - Model: regnety_400m pts/ncnn-1.3.0 Target: Vulkan GPU - Model: squeezenet_ssd pts/ncnn-1.3.0 -1 Target: CPU-v2-v2 - Model: mobilenet-v2 pts/ncnn-1.3.0 -1 Target: CPU-v3-v3 - Model: mobilenet-v3 pts/ncnn-1.3.0 -1 Target: CPU - Model: mobilenet pts/ncnn-1.3.0 -1 Target: CPU - Model: shufflenet-v2 pts/ncnn-1.3.0 Target: Vulkan GPU - Model: blazeface pts/ncnn-1.3.0 -1 Target: CPU - Model: yolov4-tiny pts/ncnn-1.3.0 Target: Vulkan GPU - Model: efficientnet-b0 pts/ncnn-1.3.0 Target: Vulkan GPU - Model: shufflenet-v2 pts/ncnn-1.3.0 -1 Target: CPU - Model: squeezenet_ssd pts/ncnn-1.3.0 Target: Vulkan GPU-v3-v3 - Model: mobilenet-v3 pts/ncnn-1.3.0 -1 Target: CPU - Model: resnet18 pts/ncnn-1.3.0 -1 Target: CPU - Model: regnety_400m pts/ncnn-1.3.0 -1 Target: CPU - Model: vgg16 pts/mlpack-1.0.2 SCIKIT_LINEARRIDGEREGRESSION Benchmark: scikit_linearridgeregression pts/mlpack-1.0.2 SCIKIT_SVM Benchmark: scikit_svm pts/mlpack-1.0.2 SCIKIT_QDA Benchmark: scikit_qda pts/mlpack-1.0.2 SCIKIT_ICA Benchmark: scikit_ica pts/ecp-candle-1.1.0 P3B2 Benchmark: P3B2 pts/ecp-candle-1.1.0 P3B1 Benchmark: P3B1 pts/ecp-candle-1.1.0 P1B2 Benchmark: P1B2 pts/plaidml-1.0.4 --no-fp16 --no-train resnet50 CPU FP16: No - Mode: Inference - Network: ResNet 50 - Device: CPU pts/plaidml-1.0.4 --no-fp16 --no-train vgg16 CPU FP16: No - Mode: Inference - Network: VGG16 - Device: CPU pts/tnn-1.1.0 -dt NAIVE -mp ../benchmark/benchmark-model/squeezenet_v1.1.tnnproto Target: CPU - Model: SqueezeNet v1.1 pts/tnn-1.1.0 -dt NAIVE -mp ../benchmark/benchmark-model/shufflenet_v2.tnnproto Target: CPU - Model: SqueezeNet v2 pts/tnn-1.1.0 -dt NAIVE -mp ../benchmark/benchmark-model/mobilenet_v2.tnnproto Target: CPU - Model: MobileNet v2 pts/tnn-1.1.0 -dt NAIVE -mp ../benchmark/benchmark-model/densenet.tnnproto Target: CPU - Model: DenseNet pts/caffe-1.5.0 --model=../models/bvlc_googlenet/deploy.prototxt -iterations 1000 Model: GoogleNet - Acceleration: CPU - Iterations: 1000 pts/caffe-1.5.0 --model=../models/bvlc_googlenet/deploy.prototxt -iterations 200 Model: GoogleNet - Acceleration: CPU - Iterations: 200 pts/caffe-1.5.0 --model=../models/bvlc_googlenet/deploy.prototxt -iterations 100 Model: GoogleNet - Acceleration: CPU - Iterations: 100 pts/caffe-1.5.0 --model=../models/bvlc_alexnet/deploy.prototxt -iterations 1000 Model: AlexNet - Acceleration: CPU - Iterations: 1000 pts/caffe-1.5.0 --model=../models/bvlc_alexnet/deploy.prototxt -iterations 200 Model: AlexNet - Acceleration: CPU - Iterations: 200 pts/caffe-1.5.0 --model=../models/bvlc_alexnet/deploy.prototxt -iterations 100 Model: AlexNet - Acceleration: CPU - Iterations: 100 pts/tensorflow-lite-1.0.0 --graph=inception_resnet_v2.tflite Model: Inception ResNet V2 pts/tensorflow-lite-1.0.0 --graph=mobilenet_v1_1.0_224_quant.tflite Model: Mobilenet Quant pts/tensorflow-lite-1.0.0 --graph=mobilenet_v1_1.0_224.tflite Model: Mobilenet Float pts/tensorflow-lite-1.0.0 --graph=nasnet_mobile.tflite Model: NASNet Mobile pts/tensorflow-lite-1.0.0 --graph=inception_v4.tflite Model: Inception V4 pts/tensorflow-lite-1.0.0 --graph=squeezenet.tflite Model: SqueezeNet pts/rnnoise-1.0.2 pts/rbenchmark-1.0.3 pts/lczero-1.6.0 -b blas Backend: BLAS pts/deepspeech-1.0.0 CPU Acceleration: CPU pts/numpy-1.2.1 pts/onednn-1.7.0 --matmul --batch=inputs/matmul/shapes_transformer --cfg=u8s8f32 --engine=cpu Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.7.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=bf16bf16bf16 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.7.0 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=bf16bf16bf16 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.7.0 --matmul --batch=inputs/matmul/shapes_transformer --cfg=f32 --engine=cpu Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU pts/onednn-1.7.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=u8s8f32 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.7.0 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=u8s8f32 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.7.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=f32 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU pts/onednn-1.7.0 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=f32 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU pts/onednn-1.7.0 --deconv --batch=inputs/deconv/shapes_3d --cfg=u8s8f32 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.7.0 --deconv --batch=inputs/deconv/shapes_1d --cfg=u8s8f32 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.7.0 --conv --batch=inputs/conv/shapes_auto --cfg=u8s8f32 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.7.0 --deconv --batch=inputs/deconv/shapes_3d --cfg=f32 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU pts/onednn-1.7.0 --deconv --batch=inputs/deconv/shapes_1d --cfg=f32 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU pts/onednn-1.7.0 --conv --batch=inputs/conv/shapes_auto --cfg=f32 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU pts/onednn-1.7.0 --ip --batch=inputs/ip/shapes_3d --cfg=u8s8f32 --engine=cpu Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.7.0 --ip --batch=inputs/ip/shapes_1d --cfg=u8s8f32 --engine=cpu Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.7.0 --ip --batch=inputs/ip/shapes_3d --cfg=f32 --engine=cpu Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU pts/onednn-1.7.0 --ip --batch=inputs/ip/shapes_1d --cfg=f32 --engine=cpu Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU pts/opencv-1.1.0 dnn Test: DNN - Deep Neural Network pts/onnx-1.3.0 super_resolution/super_resolution.onnx -e cpu Model: super-resolution-10 - Device: CPU pts/onnx-1.3.0 model/test_shufflenetv2/model.onnx -e cpu Model: shufflenet-v2-10 - Device: CPU pts/onnx-1.3.0 fcn-resnet101-11/model.onnx -e cpu Model: fcn-resnet101-11 - Device: CPU pts/onnx-1.3.0 yolov4/yolov4.onnx -e cpu Model: yolov4 - Device: CPU pts/openvino-1.0.4 -m models/intel/age-gender-recognition-retail-0013/FP32/age-gender-recognition-retail-0013.xml -d GPU Model: Age Gender Recognition Retail 0013 FP32 - Device: Intel GPU pts/openvino-1.0.4 -m models/intel/age-gender-recognition-retail-0013/FP16/age-gender-recognition-retail-0013.xml -d GPU Model: Age Gender Recognition Retail 0013 FP16 - Device: Intel GPU pts/openvino-1.0.4 -m models/intel/age-gender-recognition-retail-0013/FP32/age-gender-recognition-retail-0013.xml -d CPU Model: Age Gender Recognition Retail 0013 FP32 - Device: CPU pts/openvino-1.0.4 -m models/intel/age-gender-recognition-retail-0013/FP16/age-gender-recognition-retail-0013.xml -d CPU Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU pts/openvino-1.0.4 -m models/intel/person-detection-0106/FP32/person-detection-0106.xml -d GPU Model: Person Detection 0106 FP32 - Device: Intel GPU pts/openvino-1.0.4 -m models/intel/person-detection-0106/FP16/person-detection-0106.xml -d GPU Model: Person Detection 0106 FP16 - Device: Intel GPU pts/openvino-1.0.4 -m models/intel/face-detection-0106/FP32/face-detection-0106.xml -d GPU Model: Face Detection 0106 FP32 - Device: Intel GPU pts/openvino-1.0.4 -m models/intel/face-detection-0106/FP16/face-detection-0106.xml -d GPU Model: Face Detection 0106 FP16 - Device: Intel GPU pts/openvino-1.0.4 -m models/intel/person-detection-0106/FP32/person-detection-0106.xml -d CPU Model: Person Detection 0106 FP32 - Device: CPU pts/openvino-1.0.4 -m models/intel/person-detection-0106/FP16/person-detection-0106.xml -d CPU Model: Person Detection 0106 FP16 - Device: CPU pts/openvino-1.0.4 -m models/intel/face-detection-0106/FP32/face-detection-0106.xml -d CPU Model: Face Detection 0106 FP32 - Device: CPU pts/openvino-1.0.4 -m models/intel/face-detection-0106/FP16/face-detection-0106.xml -d CPU Model: Face Detection 0106 FP16 - Device: CPU pts/tensorflow-1.1.0 cifar10_train.py --max_steps 1000 Build: Cifar10 pts/ai-benchmark-1.0.1 pts/numenta-nab-1.1.0 -d bayesChangePt Detector: Bayesian Changepoint pts/numenta-nab-1.1.0 -d earthgeckoSkyline Detector: Earthgecko Skyline pts/numenta-nab-1.1.0 -d windowedGaussian Detector: Windowed Gaussian pts/numenta-nab-1.1.0 -d relativeEntropy Detector: Relative Entropy pts/numenta-nab-1.1.0 -d expose Detector: EXPoSE pts/mnn-1.3.0 Model: inception-v3 pts/mnn-1.3.0 Model: mobilenet-v1-1.0 pts/mnn-1.3.0 Model: SqueezeNetV1.0 pts/mnn-1.3.0 Model: resnet-v2-50 pts/mnn-1.3.0 Model: squeezenetv1.1 pts/mnn-1.3.0 Model: mobilenetV3 pts/onednn-1.7.0 --matmul --batch=inputs/matmul/shapes_transformer --cfg=bf16bf16bf16 --engine=cpu Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.7.0 --deconv --batch=inputs/deconv/shapes_3d --cfg=bf16bf16bf16 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.7.0 --deconv --batch=inputs/deconv/shapes_1d --cfg=bf16bf16bf16 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.7.0 --conv --batch=inputs/conv/shapes_auto --cfg=bf16bf16bf16 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.7.0 --ip --batch=inputs/ip/shapes_3d --cfg=bf16bf16bf16 --engine=cpu Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.7.0 --ip --batch=inputs/ip/shapes_1d --cfg=bf16bf16bf16 --engine=cpu Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU