ddda AMD Ryzen 9 5900HX testing with a ASUS G513QY v1.0 (G513QY.318 BIOS) and ASUS AMD Cezanne 512MB on Ubuntu 22.10 via the Phoronix Test Suite. a: Processor: AMD Ryzen 9 5900HX @ 3.30GHz (8 Cores / 16 Threads), Motherboard: ASUS G513QY v1.0 (G513QY.318 BIOS), Chipset: AMD Renoir/Cezanne, Memory: 16GB, Disk: 512GB SAMSUNG MZVLQ512HBLU-00B00, Graphics: ASUS AMD Cezanne 512MB (2500/1000MHz), Audio: AMD Navi 21/23, Monitor: LQ156M1JW25, Network: Realtek RTL8111/8168/8411 + MEDIATEK MT7921 802.11ax PCI OS: Ubuntu 22.10, Kernel: 5.19.0-46-generic (x86_64), Desktop: GNOME Shell 43.0, Display Server: X Server 1.21.1.4 + Wayland, OpenGL: 4.6 Mesa 22.2.5 (LLVM 15.0.2 DRM 3.47), Vulkan: 1.3.224, Compiler: GCC 12.2.0, File-System: ext4, Screen Resolution: 1920x1080 b: Processor: AMD Ryzen 9 5900HX @ 3.30GHz (8 Cores / 16 Threads), Motherboard: ASUS G513QY v1.0 (G513QY.318 BIOS), Chipset: AMD Renoir/Cezanne, Memory: 16GB, Disk: 512GB SAMSUNG MZVLQ512HBLU-00B00, Graphics: ASUS AMD Cezanne 512MB, Audio: AMD Navi 21/23, Monitor: LQ156M1JW25, Network: Realtek RTL8111/8168/8411 + MEDIATEK MT7921 802.11ax PCI OS: Ubuntu 22.10, Kernel: 5.19.0-46-generic (x86_64), Desktop: GNOME Shell 43.0, Display Server: X Server 1.21.1.4 + Wayland, OpenGL: 4.6 Mesa 22.2.5 (LLVM 15.0.2 DRM 3.47), Vulkan: 1.3.224, Compiler: GCC 12.2.0, File-System: ext4, Screen Resolution: 1920x1080 easyWave r34 Input: e2Asean Grid + BengkuluSept2007 Source - Time: 1200 Seconds < Lower Is Better a . 448.37 |=================================================================== b . 448.45 |=================================================================== easyWave r34 Input: e2Asean Grid + BengkuluSept2007 Source - Time: 2400 Seconds < Lower Is Better a . 1120.87 |================================================================== b . 1119.45 |================================================================== QuantLib 1.32 Configuration: Multi-Threaded MFLOPS > Higher Is Better a . 33193.9 |================================================================= b . 33532.7 |================================================================== QuantLib 1.32 Configuration: Single-Threaded MFLOPS > Higher Is Better a . 3384.6 |============================================================== b . 3659.7 |=================================================================== easyWave r34 Input: e2Asean Grid + BengkuluSept2007 Source - Time: 240 Seconds < Lower Is Better a . 20.17 |==================================================================== b . 20.09 |==================================================================== OpenRadioss 2023.09.15 Model: Bumper Beam Seconds < Lower Is Better a . 193.13 |=================================================================== b . 190.68 |================================================================== OpenRadioss 2023.09.15 Model: Chrysler Neon 1M Seconds < Lower Is Better a . 2378.09 |================================================================== b . 2331.98 |================================================================= OpenRadioss 2023.09.15 Model: Cell Phone Drop Test Seconds < Lower Is Better a . 145.89 |=================================================================== b . 145.88 |=================================================================== OpenRadioss 2023.09.15 Model: Bird Strike on Windshield Seconds < Lower Is Better a . 370.52 |=================================================================== b . 371.69 |=================================================================== OpenRadioss 2023.09.15 Model: Rubber O-Ring Seal Installation Seconds < Lower Is Better a . 329.91 |================================================================= b . 339.17 |=================================================================== OpenRadioss 2023.09.15 Model: INIVOL and Fluid Structure Interaction Drop Container Seconds < Lower Is Better a . 936.04 |=================================================================== b . 942.06 |=================================================================== oneDNN 3.3 Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 3.85988 |================================================================== b . 3.85158 |================================================================== oneDNN 3.3 Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 13.96 |=================================================================== b . 14.17 |==================================================================== oneDNN 3.3 Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 1.31445 |================================================================== b . 1.31051 |================================================================== oneDNN 3.3 Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 3.11617 |============================================================== b . 3.31702 |================================================================== oneDNN 3.3 Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 3.3 Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 3.3 Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 30.89 |==================================================================== b . 30.86 |==================================================================== oneDNN 3.3 Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 7.69132 |================================================================== b . 7.56516 |================================================================= oneDNN 3.3 Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 6.44936 |================================================================== b . 6.45538 |================================================================== oneDNN 3.3 Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 32.54 |==================================================================== b . 32.56 |==================================================================== oneDNN 3.3 Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 1.92395 |================================================================== b . 1.92924 |================================================================== oneDNN 3.3 Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 2.90102 |=============================================================== b . 3.02808 |================================================================== oneDNN 3.3 Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 4196.12 |================================================================== b . 4148.72 |================================================================= oneDNN 3.3 Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 2751.87 |================================================================== b . 2745.69 |================================================================== oneDNN 3.3 Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 4174.07 |================================================================== b . 4189.31 |================================================================== oneDNN 3.3 Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 3.3 Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 3.3 Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 3.3 Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 2719.66 |================================================================== b . 2731.55 |================================================================== oneDNN 3.3 Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better a . 4205.55 |================================================================== b . 4183.17 |================================================================== oneDNN 3.3 Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better a . 2718.62 |================================================================== b . 2704.69 |================================================================== OpenVINO 2023.1 Model: Face Detection FP16 - Device: CPU FPS > Higher Is Better a . 2.27 |===================================================================== b . 2.27 |===================================================================== OpenVINO 2023.1 Model: Face Detection FP16 - Device: CPU ms < Lower Is Better a . 1746.07 |================================================================== b . 1747.73 |================================================================== OpenVINO 2023.1 Model: Person Detection FP16 - Device: CPU FPS > Higher Is Better a . 15.89 |==================================================================== b . 15.96 |==================================================================== OpenVINO 2023.1 Model: Person Detection FP16 - Device: CPU ms < Lower Is Better a . 251.59 |=================================================================== b . 250.41 |=================================================================== OpenVINO 2023.1 Model: Person Detection FP32 - Device: CPU FPS > Higher Is Better a . 15.90 |==================================================================== b . 15.42 |================================================================== OpenVINO 2023.1 Model: Person Detection FP32 - Device: CPU ms < Lower Is Better a . 251.36 |================================================================= b . 259.08 |=================================================================== OpenVINO 2023.1 Model: Vehicle Detection FP16 - Device: CPU FPS > Higher Is Better a . 97.40 |==================================================================== b . 95.97 |=================================================================== OpenVINO 2023.1 Model: Vehicle Detection FP16 - Device: CPU ms < Lower Is Better a . 41.02 |=================================================================== b . 41.64 |==================================================================== OpenVINO 2023.1 Model: Face Detection FP16-INT8 - Device: CPU FPS > Higher Is Better a . 5.32 |===================================================================== b . 5.33 |===================================================================== OpenVINO 2023.1 Model: Face Detection FP16-INT8 - Device: CPU ms < Lower Is Better a . 750.14 |=================================================================== b . 748.23 |=================================================================== OpenVINO 2023.1 Model: Face Detection Retail FP16 - Device: CPU FPS > Higher Is Better a . 407.0 |==================================================================== b . 408.5 |==================================================================== OpenVINO 2023.1 Model: Face Detection Retail FP16 - Device: CPU ms < Lower Is Better a . 9.81 |===================================================================== b . 9.77 |===================================================================== OpenVINO 2023.1 Model: Road Segmentation ADAS FP16 - Device: CPU FPS > Higher Is Better a . 22.2 |===================================================================== b . 22.3 |===================================================================== OpenVINO 2023.1 Model: Road Segmentation ADAS FP16 - Device: CPU ms < Lower Is Better a . 180.09 |=================================================================== b . 179.26 |=================================================================== OpenVINO 2023.1 Model: Vehicle Detection FP16-INT8 - Device: CPU FPS > Higher Is Better a . 258.35 |=================================================================== b . 257.91 |=================================================================== OpenVINO 2023.1 Model: Vehicle Detection FP16-INT8 - Device: CPU ms < Lower Is Better a . 15.47 |==================================================================== b . 15.50 |==================================================================== OpenVINO 2023.1 Model: Weld Porosity Detection FP16 - Device: CPU FPS > Higher Is Better a . 236.08 |=================================================================== b . 235.51 |=================================================================== OpenVINO 2023.1 Model: Weld Porosity Detection FP16 - Device: CPU ms < Lower Is Better a . 16.93 |==================================================================== b . 16.97 |==================================================================== OpenVINO 2023.1 Model: Face Detection Retail FP16-INT8 - Device: CPU FPS > Higher Is Better a . 1093.26 |================================================================== b . 1097.09 |================================================================== OpenVINO 2023.1 Model: Face Detection Retail FP16-INT8 - Device: CPU ms < Lower Is Better a . 3.65 |===================================================================== b . 3.64 |===================================================================== OpenVINO 2023.1 Model: Road Segmentation ADAS FP16-INT8 - Device: CPU FPS > Higher Is Better a . 100.32 |=================================================================== b . 99.27 |================================================================== OpenVINO 2023.1 Model: Road Segmentation ADAS FP16-INT8 - Device: CPU ms < Lower Is Better a . 39.81 |=================================================================== b . 40.24 |==================================================================== OpenVINO 2023.1 Model: Machine Translation EN To DE FP16 - Device: CPU FPS > Higher Is Better a . 23.09 |==================================================================== b . 23.19 |==================================================================== OpenVINO 2023.1 Model: Machine Translation EN To DE FP16 - Device: CPU ms < Lower Is Better a . 173.11 |=================================================================== b . 172.41 |=================================================================== OpenVINO 2023.1 Model: Weld Porosity Detection FP16-INT8 - Device: CPU FPS > Higher Is Better a . 525.09 |=================================================================== b . 524.34 |=================================================================== OpenVINO 2023.1 Model: Weld Porosity Detection FP16-INT8 - Device: CPU ms < Lower Is Better a . 15.23 |==================================================================== b . 15.25 |==================================================================== OpenVINO 2023.1 Model: Person Vehicle Bike Detection FP16 - Device: CPU FPS > Higher Is Better a . 263.85 |================================================================ b . 276.25 |=================================================================== OpenVINO 2023.1 Model: Person Vehicle Bike Detection FP16 - Device: CPU ms < Lower Is Better a . 15.14 |==================================================================== b . 14.46 |================================================================= OpenVINO 2023.1 Model: Handwritten English Recognition FP16 - Device: CPU FPS > Higher Is Better a . 62.58 |=================================================================== b . 63.69 |==================================================================== OpenVINO 2023.1 Model: Handwritten English Recognition FP16 - Device: CPU ms < Lower Is Better a . 127.73 |=================================================================== b . 125.48 |================================================================== OpenVINO 2023.1 Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU FPS > Higher Is Better a . 6935.11 |================================================================== b . 6863.29 |================================================================= OpenVINO 2023.1 Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU ms < Lower Is Better a . 1.14 |==================================================================== b . 1.15 |===================================================================== OpenVINO 2023.1 Model: Handwritten English Recognition FP16-INT8 - Device: CPU FPS > Higher Is Better a . 79.11 |================================================================ b . 84.61 |==================================================================== OpenVINO 2023.1 Model: Handwritten English Recognition FP16-INT8 - Device: CPU ms < Lower Is Better a . 101.07 |=================================================================== b . 94.48 |=============================================================== OpenVINO 2023.1 Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU FPS > Higher Is Better a . 14143.38 |================================================================= b . 14118.20 |================================================================= OpenVINO 2023.1 Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU ms < Lower Is Better a . 0.56 |===================================================================== b . 0.56 |===================================================================== Embree 4.3 Binary: Pathtracer - Model: Crown Frames Per Second > Higher Is Better a . 11.76 |==================================================================== b . 11.69 |==================================================================== Embree 4.3 Binary: Pathtracer ISPC - Model: Crown Frames Per Second > Higher Is Better a . 10.55 |==================================================================== b . 10.58 |==================================================================== Embree 4.3 Binary: Pathtracer - Model: Asian Dragon Frames Per Second > Higher Is Better a . 12.53 |==================================================================== b . 12.52 |==================================================================== Embree 4.3 Binary: Pathtracer - Model: Asian Dragon Obj Frames Per Second > Higher Is Better a . 11.14 |==================================================================== b . 11.15 |==================================================================== Embree 4.3 Binary: Pathtracer ISPC - Model: Asian Dragon Frames Per Second > Higher Is Better a . 11.71 |==================================================================== b . 11.67 |==================================================================== Embree 4.3 Binary: Pathtracer ISPC - Model: Asian Dragon Obj Frames Per Second > Higher Is Better a . 10.16 |==================================================================== b . 10.07 |=================================================================== Intel Open Image Denoise 2.1 Run: RT.hdr_alb_nrm.3840x2160 - Device: CPU-Only Images / Sec > Higher Is Better a . 0.34 |===================================================================== b . 0.34 |===================================================================== Intel Open Image Denoise 2.1 Run: RT.ldr_alb_nrm.3840x2160 - Device: CPU-Only Images / Sec > Higher Is Better a . 0.34 |===================================================================== b . 0.34 |===================================================================== Intel Open Image Denoise 2.1 Run: RTLightmap.hdr.4096x4096 - Device: CPU-Only Images / Sec > Higher Is Better a . 0.17 |===================================================================== b . 0.17 |===================================================================== OpenVKL 2.0.0 Benchmark: vklBenchmarkCPU ISPC Items / Sec > Higher Is Better a . 187 |====================================================================== b . 187 |====================================================================== OpenVKL 2.0.0 Benchmark: vklBenchmarkCPU Scalar Items / Sec > Higher Is Better a . 97 |======================================================================= b . 97 |=======================================================================