10600K pre RKL
Intel Core i5-10600K testing with a ASUS PRIME Z490M-PLUS (1001 BIOS) and ASUS Intel UHD 630 3GB on Ubuntu 20.04 via the Phoronix Test Suite.
HTML result view exported from: https://openbenchmarking.org/result/2103183-IB-10600KPRE35&rdt&gru.
Sysbench
Test: CPU
AOM AV1
Encoder Mode: Speed 0 Two-Pass
AOM AV1
Encoder Mode: Speed 4 Two-Pass
AOM AV1
Encoder Mode: Speed 6 Realtime
AOM AV1
Encoder Mode: Speed 6 Two-Pass
AOM AV1
Encoder Mode: Speed 8 Realtime
SVT-HEVC
Tuning: 1 - Input: Bosphorus 1080p
SVT-HEVC
Tuning: 7 - Input: Bosphorus 1080p
SVT-HEVC
Tuning: 10 - Input: Bosphorus 1080p
SVT-VP9
Tuning: VMAF Optimized - Input: Bosphorus 1080p
SVT-VP9
Tuning: PSNR/SSIM Optimized - Input: Bosphorus 1080p
SVT-VP9
Tuning: Visual Quality Optimized - Input: Bosphorus 1080p
simdjson
Throughput Test: Kostya
simdjson
Throughput Test: LargeRandom
simdjson
Throughput Test: PartialTweets
simdjson
Throughput Test: DistinctUserID
Sysbench
Test: RAM / Memory
oneDNN
Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU
oneDNN
Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU
oneDNN
Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU
oneDNN
Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU
oneDNN
Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU
oneDNN
Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU
oneDNN
Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU
oneDNN
Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU
oneDNN
Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU
oneDNN
Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU
oneDNN
Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU
oneDNN
Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU
oneDNN
Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU
oneDNN
Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU
oneDNN
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU
oneDNN
Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU
oneDNN
Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU
oneDNN
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU
Mobile Neural Network
Model: SqueezeNetV1.0
Mobile Neural Network
Model: resnet-v2-50
Mobile Neural Network
Model: MobileNetV2_224
Mobile Neural Network
Model: mobilenet-v1-1.0
Mobile Neural Network
Model: inception-v3
Xcompact3d Incompact3d
Input: input.i3d 129 Cells Per Direction
Xcompact3d Incompact3d
Input: input.i3d 193 Cells Per Direction
Timed Mesa Compilation
Time To Compile
Timed Node.js Compilation
Time To Compile
ASTC Encoder
Preset: Medium
ASTC Encoder
Preset: Thorough
ASTC Encoder
Preset: Exhaustive
Basis Universal
Settings: ETC1S
Basis Universal
Settings: UASTC Level 0
Basis Universal
Settings: UASTC Level 2
Basis Universal
Settings: UASTC Level 3
Phoronix Test Suite v10.8.5