renoir march AMD Ryzen 7 4700U testing with a LENOVO LNVNB161216 (DTCN18WWV1.04 BIOS) and AMD Renoir 512MB on Ubuntu 22.04 via the Phoronix Test Suite. a: Processor: AMD Ryzen 7 4700U @ 2.00GHz (8 Cores), Motherboard: LENOVO LNVNB161216 (DTCN18WWV1.04 BIOS), Chipset: AMD Renoir/Cezanne, Memory: 16GB, Disk: 512GB SAMSUNG MZALQ512HALU-000L2, Graphics: AMD Renoir 512MB (1600/400MHz), Audio: AMD Renoir Radeon HD Audio, Network: Intel Wi-Fi 6 AX200 OS: Ubuntu 22.04, Kernel: 5.19.0-35-generic (x86_64), Desktop: GNOME Shell 42.2, Display Server: X Server + Wayland, OpenGL: 4.6 Mesa 22.0.5 (LLVM 13.0.1 DRM 3.47), Vulkan: 1.3.204, Compiler: GCC 11.3.0, File-System: ext4, Screen Resolution: 1920x1080 b: Processor: AMD Ryzen 7 4700U @ 2.00GHz (8 Cores), Motherboard: LENOVO LNVNB161216 (DTCN18WWV1.04 BIOS), Chipset: AMD Renoir/Cezanne, Memory: 16GB, Disk: 512GB SAMSUNG MZALQ512HALU-000L2, Graphics: AMD Renoir 512MB (1600/400MHz), Audio: AMD Renoir Radeon HD Audio, Network: Intel Wi-Fi 6 AX200 OS: Ubuntu 22.04, Kernel: 5.19.0-35-generic (x86_64), Desktop: GNOME Shell 42.2, Display Server: X Server + Wayland, OpenGL: 4.6 Mesa 22.0.5 (LLVM 13.0.1 DRM 3.47), Vulkan: 1.3.204, Compiler: GCC 11.3.0, File-System: ext4, Screen Resolution: 1920x1080 c: Processor: AMD Ryzen 7 4700U @ 2.00GHz (8 Cores), Motherboard: LENOVO LNVNB161216 (DTCN18WWV1.04 BIOS), Chipset: AMD Renoir/Cezanne, Memory: 16GB, Disk: 512GB SAMSUNG MZALQ512HALU-000L2, Graphics: AMD Renoir 512MB (1600/400MHz), Audio: AMD Renoir Radeon HD Audio, Network: Intel Wi-Fi 6 AX200 OS: Ubuntu 22.04, Kernel: 5.19.0-35-generic (x86_64), Desktop: GNOME Shell 42.2, Display Server: X Server + Wayland, OpenGL: 4.6 Mesa 22.0.5 (LLVM 13.0.1 DRM 3.47), Vulkan: 1.3.204, Compiler: GCC 11.3.0, File-System: ext4, Screen Resolution: 1920x1080 oneDNN 3.1 Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 10.23 |=================================================================== b . 10.28 |=================================================================== c . 10.44 |==================================================================== oneDNN 3.1 Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 12.38 |=================================================== b . 11.10 |============================================== c . 16.37 |==================================================================== oneDNN 3.1 Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 2.65350 |=========================================================== b . 2.65892 |=========================================================== c . 2.96854 |================================================================== oneDNN 3.1 Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 3.13837 |============================================================ b . 2.87009 |======================================================= c . 3.46889 |================================================================== oneDNN 3.1 Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 3.1 Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 3.1 Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 32.62 |=================================================================== b . 32.03 |================================================================== c . 33.10 |==================================================================== oneDNN 3.1 Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 7.09507 |================================================================== b . 7.11850 |================================================================== c . 7.12157 |================================================================== oneDNN 3.1 Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 10.55 |==================================================================== b . 10.61 |==================================================================== c . 10.08 |================================================================= oneDNN 3.1 Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 30.12 |=================================================================== b . 29.78 |================================================================== c . 30.74 |==================================================================== oneDNN 3.1 Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 4.13791 |================================================================= b . 4.12970 |================================================================= c . 4.19127 |================================================================== oneDNN 3.1 Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 5.12357 |================================================================= b . 5.24125 |================================================================== c . 5.22570 |================================================================== oneDNN 3.1 Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 6306.82 |================================================================ b . 6314.56 |================================================================ c . 6527.85 |================================================================== oneDNN 3.1 Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 4019.28 |================================================================ b . 4017.47 |================================================================ c . 4131.35 |================================================================== oneDNN 3.1 Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 6560.30 |================================================================= b . 6566.52 |================================================================= c . 6666.73 |================================================================== oneDNN 3.1 Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 3.1 Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 3.1 Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 3.1 Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 4023.48 |================================================================ b . 3991.39 |================================================================ c . 4117.72 |================================================================== oneDNN 3.1 Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better a . 6556.30 |================================================================= b . 6579.69 |================================================================= c . 6656.34 |================================================================== oneDNN 3.1 Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better a . 4009.28 |================================================================ b . 4052.18 |================================================================= c . 4128.28 |================================================================== Memcached 1.6.19 Set To Get Ratio: 1:5 Ops/sec > Higher Is Better a . 768701.54 |================================================================ b . 771166.10 |================================================================ c . 767553.48 |================================================================ Memcached 1.6.19 Set To Get Ratio: 1:10 Ops/sec > Higher Is Better a . 742837.61 |================================================================ b . 741502.21 |================================================================ c . 734785.31 |=============================================================== Memcached 1.6.19 Set To Get Ratio: 1:100 Ops/sec > Higher Is Better a . 714874.04 |================================================================ b . 710328.12 |================================================================ c . 710780.33 |================================================================ TensorFlow 2.12 Device: CPU - Batch Size: 16 - Model: AlexNet images/sec > Higher Is Better a . 32.73 |==================================================================== b . 28.38 |=========================================================== c . 32.08 |=================================================================== TensorFlow 2.12 Device: CPU - Batch Size: 32 - Model: AlexNet images/sec > Higher Is Better a . 36.56 |============================================================== b . 36.91 |=============================================================== c . 39.93 |==================================================================== TensorFlow 2.12 Device: CPU - Batch Size: 64 - Model: AlexNet images/sec > Higher Is Better a . 42.12 |============================================================== b . 42.10 |============================================================== c . 46.32 |==================================================================== TensorFlow 2.12 Device: CPU - Batch Size: 16 - Model: GoogLeNet images/sec > Higher Is Better a . 16.55 |================================================================ b . 17.19 |=================================================================== c . 17.57 |==================================================================== TensorFlow 2.12 Device: CPU - Batch Size: 16 - Model: ResNet-50 images/sec > Higher Is Better a . 6.08 |===================================================================== b . 6.03 |==================================================================== c . 6.08 |===================================================================== TensorFlow 2.12 Device: CPU - Batch Size: 32 - Model: GoogLeNet images/sec > Higher Is Better a . 17.99 |==================================================================== b . 18.08 |==================================================================== c . 17.92 |=================================================================== TensorFlow 2.12 Device: CPU - Batch Size: 32 - Model: ResNet-50 images/sec > Higher Is Better a . 6.10 |==================================================================== b . 6.15 |===================================================================== c . 6.07 |==================================================================== TensorFlow 2.12 Device: CPU - Batch Size: 64 - Model: GoogLeNet images/sec > Higher Is Better a . 17.72 |==================================================================== b . 17.83 |==================================================================== c . 17.58 |=================================================================== TensorFlow 2.12 Device: CPU - Batch Size: 64 - Model: ResNet-50 images/sec > Higher Is Better a . 6.07 |==================================================================== b . 6.14 |===================================================================== c . 5.93 |=================================================================== Blender 3.5 Blend File: BMW27 - Compute: CPU-Only Seconds < Lower Is Better a . 293.14 |=================================================================== b . 292.36 |=================================================================== c . 292.63 |=================================================================== Blender 3.5 Blend File: Classroom - Compute: CPU-Only Seconds < Lower Is Better a . 790.23 |=================================================================== b . 789.45 |=================================================================== c . 790.08 |=================================================================== Blender 3.5 Blend File: Fishy Cat - Compute: CPU-Only Seconds < Lower Is Better a . 398.48 |=================================================================== b . 398.11 |=================================================================== c . 399.85 |=================================================================== Blender 3.5 Blend File: Barbershop - Compute: CPU-Only Seconds < Lower Is Better a . 3223.06 |================================================================== b . 3219.93 |================================================================== c . 3201.64 |================================================================== Blender 3.5 Blend File: Pabellon Barcelona - Compute: CPU-Only Seconds < Lower Is Better a . 1047.99 |================================================================== b . 1046.20 |================================================================== c . 1044.62 |================================================================== nginx 1.23.2 Connections: 100 Requests Per Second > Higher Is Better a . 27163.88 |================================================================= b . 27205.44 |================================================================= c . 27203.59 |================================================================= nginx 1.23.2 Connections: 200 Requests Per Second > Higher Is Better a . 26452.97 |================================================================= b . 26440.93 |================================================================= c . 26494.32 |================================================================= nginx 1.23.2 Connections: 500 Requests Per Second > Higher Is Better a . 24356.46 |================================================================= b . 24369.11 |================================================================= c . 24357.18 |================================================================= nginx 1.23.2 Connections: 1000 Requests Per Second > Higher Is Better a . 21701.31 |================================================================= b . 20476.95 |============================================================= c . 20588.12 |============================================================== Apache HTTP Server 2.4.56 Concurrent Requests: 100 Requests Per Second > Higher Is Better a . 37280.37 |=============================================================== b . 38208.54 |================================================================= c . 37473.95 |================================================================ Apache HTTP Server 2.4.56 Concurrent Requests: 200 Requests Per Second > Higher Is Better a . 38717.78 |================================================================= b . 38828.56 |================================================================= c . 38585.99 |================================================================= Apache HTTP Server 2.4.56 Concurrent Requests: 500 Requests Per Second > Higher Is Better a . 31777.03 |================================================================= b . 31652.93 |================================================================= c . 31531.58 |================================================================ Apache HTTP Server 2.4.56 Concurrent Requests: 1000 Requests Per Second > Higher Is Better a . 31326.72 |================================================================= b . 31317.49 |================================================================= c . 31328.25 |=================================================================