9900k-wiesn

Intel Core i9-9900K testing with a ASRock Z390M Pro4 (P4.20 BIOS) and Intel UHD 630 3GB 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 2009261-FI-9900KWIES08
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BLAS (Basic Linear Algebra Sub-Routine) Tests 2 Tests
C/C++ Compiler Tests 2 Tests
CPU Massive 2 Tests
Creator Workloads 5 Tests
Database Test Suite 2 Tests
Fortran Tests 3 Tests
HPC - High Performance Computing 10 Tests
Imaging 3 Tests
Machine Learning 6 Tests
Molecular Dynamics 2 Tests
MPI Benchmarks 4 Tests
Multi-Core 2 Tests
NVIDIA GPU Compute 3 Tests
OpenMPI Tests 4 Tests
Python Tests 3 Tests
Scientific Computing 4 Tests
Server 2 Tests
Single-Threaded 2 Tests
Vulkan Compute 2 Tests

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September 26 2020
  2 Hours, 4 Minutes
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September 26 2020
  8 Hours, 16 Minutes
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September 26 2020
  2 Hours, 2 Minutes
3a
September 26 2020
  7 Hours, 44 Minutes
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9900k-wiesn Suite 1.0.0 System Test suite extracted from 9900k-wiesn . pts/lammps-1.2.1 benchmark_20k_atoms.in Model: 20k Atoms pts/ai-benchmark-1.0.0 Device AI Score pts/ai-benchmark-1.0.0 Device Training Score pts/ai-benchmark-1.0.0 Device Inference Score pts/lczero-1.5.0 -b opencl Backend: OpenCL pts/incompact3d-1.0.0 examples/Cylinder/input.i3d Input: Cylinder pts/lczero-1.5.0 -b eigen Backend: Eigen pts/lczero-1.5.0 -b blas Backend: BLAS pts/gpaw-1.0.0 carbon-nanotube Input: Carbon Nanotube pts/opencv-1.0.0 features2d Test: Features 2D pts/realsr-ncnn-1.0.0 -s 4 Scale: 4x - TAA: No pts/mocassin-1.0.0 Input: Dust 2D tau100.0 pts/ncnn-1.0.3 Target: Vulkan GPU - Model: yolov4-tiny pts/ncnn-1.0.3 Target: Vulkan GPU - Model: resnet50 pts/ncnn-1.0.3 Target: Vulkan GPU - Model: alexnet pts/ncnn-1.0.3 Target: Vulkan GPU - Model: resnet18 pts/ncnn-1.0.3 Target: Vulkan GPU - Model: vgg16 pts/ncnn-1.0.3 Target: Vulkan GPU - Model: googlenet pts/ncnn-1.0.3 Target: Vulkan GPU - Model: blazeface pts/ncnn-1.0.3 Target: Vulkan GPU - Model: efficientnet-b0 pts/ncnn-1.0.3 Target: Vulkan GPU - Model: mnasnet pts/ncnn-1.0.3 Target: Vulkan GPU - Model: shufflenet-v2 pts/ncnn-1.0.3 Target: Vulkan GPU-v3-v3 - Model: mobilenet-v3 pts/ncnn-1.0.3 Target: Vulkan GPU-v2-v2 - Model: mobilenet-v2 pts/ncnn-1.0.3 Target: Vulkan GPU - Model: mobilenet pts/ncnn-1.0.3 Target: Vulkan GPU - Model: squeezenet pts/opencv-1.0.0 objdetect Test: Object Detection pts/glmark2-1.2.0 -s 1920x1080 Resolution: 1920 x 1080 pts/mnn-1.0.1 Model: inception-v3 pts/mnn-1.0.1 Model: mobilenet-v1-1.0 pts/mnn-1.0.1 Model: MobileNetV2_224 pts/mnn-1.0.1 Model: resnet-v2-50 pts/mnn-1.0.1 Model: SqueezeNetV1.0 pts/opencv-1.0.0 dnn Test: DNN - Deep Neural Network pts/influxdb-1.0.0 -c 4 -b 10000 -t 2,5000,1 -p 10000 Concurrent Streams: 4 - Batch Size: 10000 - Tags: 2,5000,1 - Points Per Series: 10000 pts/influxdb-1.0.0 -c 64 -b 10000 -t 2,5000,1 -p 10000 Concurrent Streams: 64 - Batch Size: 10000 - Tags: 2,5000,1 - Points Per Series: 10000 pts/influxdb-1.0.0 -c 1024 -b 10000 -t 2,5000,1 -p 10000 Concurrent Streams: 1024 - Batch Size: 10000 - Tags: 2,5000,1 - Points Per Series: 10000 pts/ncnn-1.0.3 -1 Target: CPU - Model: yolov4-tiny pts/ncnn-1.0.3 -1 Target: CPU - Model: resnet50 pts/ncnn-1.0.3 -1 Target: CPU - Model: alexnet pts/ncnn-1.0.3 -1 Target: CPU - Model: resnet18 pts/ncnn-1.0.3 -1 Target: CPU - Model: vgg16 pts/ncnn-1.0.3 -1 Target: CPU - Model: googlenet pts/ncnn-1.0.3 -1 Target: CPU - Model: blazeface pts/ncnn-1.0.3 -1 Target: CPU - Model: efficientnet-b0 pts/ncnn-1.0.3 -1 Target: CPU - Model: mnasnet pts/ncnn-1.0.3 -1 Target: CPU - Model: shufflenet-v2 pts/ncnn-1.0.3 -1 Target: CPU-v3-v3 - Model: mobilenet-v3 pts/ncnn-1.0.3 -1 Target: CPU-v2-v2 - Model: mobilenet-v2 pts/ncnn-1.0.3 -1 Target: CPU - Model: mobilenet pts/ncnn-1.0.3 -1 Target: CPU - Model: squeezenet pts/espeak-1.6.0 Text-To-Speech Synthesis pts/dcraw-1.1.1 RAW To PPM Image Conversion pts/webp-1.0.0 -q 100 -lossless -m 6 Encode Settings: Quality 100, Lossless, Highest Compression pts/libraw-1.0.0 Post-Processing Benchmark pts/aom-av1-2.1.2 --cpu-used=0 --limit=10 Encoder Mode: Speed 0 Two-Pass pts/tnn-1.0.0 -dt NAIVE -mp ../benchmark/benchmark-model/mobilenet_v2.tnnproto Target: CPU - Model: MobileNet v2 pts/tnn-1.0.0 -dt NAIVE -mp ../benchmark/benchmark-model/squeezenet_v1.1.tnnproto Target: CPU - Model: SqueezeNet v1.1 pts/webp-1.0.0 -q 100 -lossless Encode Settings: Quality 100, Lossless pts/webp-1.0.0 -q 100 -m 6 Encode Settings: Quality 100, Highest Compression pts/lammps-1.2.1 in.rhodo Model: Rhodopsin Protein pts/system-decompress-gzip-1.1.1 pts/webp-1.0.0 -q 100 Encode Settings: Quality 100 pts/webp-1.0.0 Encode Settings: Default