Fedora 32 vs. Fedora 33 Beta Benchmarks

Intel Core i9-10900K testing on Fedora 32 and Fedora 33 Beta by Michael Larabel.

HTML result view exported from: https://openbenchmarking.org/result/2009290-FI-FEDORA85348&rdt&gru.

dav1d

Video Input: Chimera 1080p

dav1d

Video Input: Summer Nature 4K

dav1d

Video Input: Summer Nature 1080p

dav1d

Video Input: Chimera 1080p 10-bit

Basemark GPU

Renderer: Vulkan - Resolution: 3840 x 2160 - Graphics Preset: High

Basemark GPU

Renderer: Vulkan - Resolution: 3840 x 2160 - Graphics Preset: Medium

Unigine Heaven

Resolution: 1920 x 1080 - Mode: Fullscreen - Renderer: OpenGL

Unigine Superposition

Resolution: 1920 x 1080 - Mode: Fullscreen - Quality: Low - Renderer: OpenGL

Unigine Superposition

Resolution: 1920 x 1080 - Mode: Fullscreen - Quality: High - Renderer: OpenGL

Embree

Binary: Pathtracer - Model: Asian Dragon

Embree

Binary: Pathtracer ISPC - Model: Asian Dragon

SVT-AV1

Encoder Mode: Enc Mode 0 - Input: 1080p

SVT-AV1

Encoder Mode: Enc Mode 4 - Input: 1080p

SVT-AV1

Encoder Mode: Enc Mode 8 - Input: 1080p

x265

H.265 1080p Video Encoding

High Performance Conjugate Gradient

Intel Open Image Denoise

Scene: Memorial

OpenVKL

Benchmark: vklBenchmark

Cryptsetup

PBKDF2-whirlpool

Cryptsetup

PBKDF2-sha512

BYTE Unix Benchmark

Computational Test: Dhrystone 2

LuxCoreRender

Scene: DLSC

LuxCoreRender

Scene: Rainbow Colors and Prism

Zstd Compression

Compression Level: 3

Zstd Compression

Compression Level: 19

LevelDB

Benchmark: Overwrite

LevelDB

Benchmark: Random Fill

LevelDB

Benchmark: Sequential Fill

LibRaw

Post-Processing Benchmark

Crafty

Elapsed Time

TSCP

AI Chess Performance

Stockfish

Total Time

GROMACS

Water Benchmark

LAMMPS Molecular Dynamics Simulator

Model: Rhodopsin Protein

Hierarchical INTegration

Test: FLOAT

AI Benchmark Alpha

Device Inference Score

AI Benchmark Alpha

Device Training Score

AI Benchmark Alpha

Device AI Score

PHPBench

PHP Benchmark Suite

NAMD

ATPase Simulation - 327,506 Atoms

WebP Image Encode

Encode Settings: Default

WebP Image Encode

Encode Settings: Quality 100

WebP Image Encode

Encode Settings: Quality 100, Lossless

WebP Image Encode

Encode Settings: Quality 100, Highest Compression

WebP Image Encode

Encode Settings: Quality 100, Lossless, Highest Compression

LevelDB

Benchmark: Hot Read

LevelDB

Benchmark: Overwrite

LevelDB

Benchmark: Random Fill

LevelDB

Benchmark: Random Read

LevelDB

Benchmark: Seek Random

LevelDB

Benchmark: Random Delete

LevelDB

Benchmark: Sequential Fill

TensorFlow Lite

Model: SqueezeNet

TensorFlow Lite

Model: Inception V4

TensorFlow Lite

Model: NASNet Mobile

TensorFlow Lite

Model: Mobilenet Float

TensorFlow Lite

Model: Mobilenet Quant

TensorFlow Lite

Model: Inception ResNet V2

Caffe

Model: AlexNet - Acceleration: CPU - Iterations: 100

Caffe

Model: AlexNet - Acceleration: CPU - Iterations: 200

Caffe

Model: GoogleNet - Acceleration: CPU - Iterations: 100

Caffe

Model: GoogleNet - Acceleration: CPU - Iterations: 200

PyBench

Total For Average Test Times

PyPerformance

Benchmark: go

PyPerformance

Benchmark: 2to3

PyPerformance

Benchmark: chaos

PyPerformance

Benchmark: float

PyPerformance

Benchmark: nbody

PyPerformance

Benchmark: pathlib

PyPerformance

Benchmark: raytrace

PyPerformance

Benchmark: json_loads

PyPerformance

Benchmark: crypto_pyaes

PyPerformance

Benchmark: regex_compile

PyPerformance

Benchmark: python_startup

PyPerformance

Benchmark: django_template

PyPerformance

Benchmark: pickle_pure_python

Renaissance

Test: Scala Dotty

Renaissance

Test: Random Forest

Renaissance

Test: Apache Spark ALS

Renaissance

Test: Savina Reactors.IO

Renaissance

Test: Apache Spark PageRank

Renaissance

Test: Twitter HTTP Requests

Renaissance

Test: Akka Unbalanced Cobwebbed Tree

Renaissance

Test: Genetic Algorithm Using Jenetics + Futures

Mobile Neural Network

Model: SqueezeNetV1.0

Mobile Neural Network

Model: resnet-v2-50

Mobile Neural Network

Model: mobilenet-v1-1.0

Mobile Neural Network

Model: inception-v3

NCNN

Target: CPU - Model: squeezenet

NCNN

Target: CPU - Model: mobilenet

NCNN

Target: CPU-v2-v2 - Model: mobilenet-v2

NCNN

Target: CPU-v3-v3 - Model: mobilenet-v3

NCNN

Target: CPU - Model: shufflenet-v2

NCNN

Target: CPU - Model: mnasnet

NCNN

Target: CPU - Model: efficientnet-b0

NCNN

Target: CPU - Model: blazeface

NCNN

Target: CPU - Model: googlenet

NCNN

Target: CPU - Model: vgg16

NCNN

Target: CPU - Model: resnet18

NCNN

Target: CPU - Model: alexnet

NCNN

Target: CPU - Model: resnet50

NCNN

Target: CPU - Model: yolov4-tiny

TNN

Target: CPU - Model: MobileNet v2

TNN

Target: CPU - Model: SqueezeNet v1.1

Systemd Total Boot Time

Test: Total

Systemd Total Boot Time

Test: Kernel

Systemd Total Boot Time

Test: Loader

Systemd Total Boot Time

Test: Firmware

Systemd Total Boot Time

Test: Userspace

DaCapo Benchmark

Java Test: Jython

DaCapo Benchmark

Java Test: Tradesoap

DaCapo Benchmark

Java Test: Tradebeans

glibc bench

Benchmark: cos

glibc bench

Benchmark: exp

glibc bench

Benchmark: ffs

glibc bench

Benchmark: sin

glibc bench

Benchmark: log2

glibc bench

Benchmark: modf

glibc bench

Benchmark: sinh

glibc bench

Benchmark: sqrt

glibc bench

Benchmark: tanh

glibc bench

Benchmark: asinh

glibc bench

Benchmark: atanh

glibc bench

Benchmark: ffsll

glibc bench

Benchmark: sincos

glibc bench

Benchmark: pthread_once

SQLite

Threads / Copies: 1

RealSR-NCNN

Scale: 4x - TAA: No

Rodinia

Test: OpenMP LavaMD

Rodinia

Test: OpenMP HotSpot3D

Rodinia

Test: OpenMP Leukocyte

Rodinia

Test: OpenMP CFD Solver

Rodinia

Test: OpenMP Streamcluster

Incompact3D

Input: Cylinder

Timed MAFFT Alignment

Multiple Sequence Alignment - LSU RNA

Bork File Encrypter

File Encryption Time