contabo-vps-s-nvme-python
KVM VMware testing on Ubuntu 20.04 via the Phoronix Test Suite.


AMD EPYC 7282 16-Core: 

	Processor: AMD EPYC 7282 16-Core (4 Cores), Motherboard: QEMU Standard PC (i440FX + PIIX 1996) (rel-1.14.0-0-g155821a1990b-prebuilt.qemu.org BIOS), Chipset: Intel 440FX 82441FX PMC, Memory: 1 x 8192 MB RAM QEMU, Disk: 54GB QEMU HDD, Graphics: VMware SVGA II, Network: Red Hat Virtio device

	OS: Ubuntu 20.04, Kernel: 5.4.0-81-generic (x86_64), Vulkan: 1.0.2, Compiler: GCC 9.3.0, File-System: ext4, System Layer: KVM VMware


Numpy Benchmark

Score > Higher Is Better
AMD EPYC 7282 16-Core . 234.46 |===============================================


Cython Benchmark 0.29.21
Test: N-Queens
Seconds < Lower Is Better
AMD EPYC 7282 16-Core . 36.01 |================================================


PyBench 2018-02-16
Total For Average Test Times
Milliseconds < Lower Is Better
AMD EPYC 7282 16-Core . 1271 |=================================================


PyPerformance 1.0.0
Benchmark: go
Milliseconds < Lower Is Better
AMD EPYC 7282 16-Core . 356 |==================================================


PyPerformance 1.0.0
Benchmark: 2to3
Milliseconds < Lower Is Better
AMD EPYC 7282 16-Core . 518 |==================================================


PyPerformance 1.0.0
Benchmark: chaos
Milliseconds < Lower Is Better
AMD EPYC 7282 16-Core . 157 |==================================================


PyPerformance 1.0.0
Benchmark: float
Milliseconds < Lower Is Better
AMD EPYC 7282 16-Core . 166 |==================================================


PyPerformance 1.0.0
Benchmark: nbody
Milliseconds < Lower Is Better
AMD EPYC 7282 16-Core . 163 |==================================================


PyPerformance 1.0.0
Benchmark: pathlib
Milliseconds < Lower Is Better
AMD EPYC 7282 16-Core . 28.3 |=================================================


PyPerformance 1.0.0
Benchmark: raytrace
Milliseconds < Lower Is Better
AMD EPYC 7282 16-Core . 697 |==================================================


PyPerformance 1.0.0
Benchmark: json_loads
Milliseconds < Lower Is Better
AMD EPYC 7282 16-Core . 34.7 |=================================================


PyPerformance 1.0.0
Benchmark: crypto_pyaes
Milliseconds < Lower Is Better
AMD EPYC 7282 16-Core . 158 |==================================================


PyPerformance 1.0.0
Benchmark: regex_compile
Milliseconds < Lower Is Better
AMD EPYC 7282 16-Core . 253 |==================================================


PyPerformance 1.0.0
Benchmark: python_startup
Milliseconds < Lower Is Better
AMD EPYC 7282 16-Core . 14.3 |=================================================


PyPerformance 1.0.0
Benchmark: django_template
Milliseconds < Lower Is Better
AMD EPYC 7282 16-Core . 68.2 |=================================================


PyPerformance 1.0.0
Benchmark: pickle_pure_python
Milliseconds < Lower Is Better
AMD EPYC 7282 16-Core . 658 |==================================================


Numenta Anomaly Benchmark 1.1
Detector: EXPoSE
Seconds < Lower Is Better
AMD EPYC 7282 16-Core . 209.73 |===============================================


Numenta Anomaly Benchmark 1.1
Detector: Relative Entropy
Seconds < Lower Is Better
AMD EPYC 7282 16-Core . 70.91 |================================================


Numenta Anomaly Benchmark 1.1
Detector: Windowed Gaussian
Seconds < Lower Is Better
AMD EPYC 7282 16-Core . 38.79 |================================================


Numenta Anomaly Benchmark 1.1
Detector: Earthgecko Skyline
Seconds < Lower Is Better
AMD EPYC 7282 16-Core . 493.51 |===============================================


Numenta Anomaly Benchmark 1.1
Detector: Bayesian Changepoint
Seconds < Lower Is Better
AMD EPYC 7282 16-Core . 104.84 |===============================================


Mlpack Benchmark
Benchmark: scikit_svm
Seconds < Lower Is Better
AMD EPYC 7282 16-Core . 33.56 |================================================


Scikit-Learn 0.22.1

Seconds < Lower Is Better
AMD EPYC 7282 16-Core . 165.02 |===============================================