Ryzen 7 2700 oneDNN 2.0 AMD Ryzen 7 2700 Eight-Core testing with a Gigabyte AB350N-Gaming WIFI-CF (F20 BIOS) and HIS AMD Radeon HD 6450/7450/8450 / R5 230 OEM 1GB on Ubuntu 19.10 via the Phoronix Test Suite. 1: Processor: AMD Ryzen 7 2700 Eight-Core @ 3.20GHz (8 Cores / 16 Threads), Motherboard: Gigabyte AB350N-Gaming WIFI-CF (F20 BIOS), Chipset: AMD 17h, Memory: 16GB, Disk: 120GB ADATA SU700, Graphics: HIS AMD Radeon HD 6450/7450/8450 / R5 230 OEM 1GB, Audio: AMD Caicos HDMI Audio, Monitor: DELL S2409W, Network: Realtek RTL8111/8168/8411 + Intel 3165 OS: Ubuntu 19.10, Kernel: 5.9.0-050900rc7daily20201004-generic (x86_64) 20201003, Desktop: GNOME Shell 3.34.1, Display Server: X Server 1.20.5, Display Driver: modesetting 1.20.5, OpenGL: 3.3 Mesa 19.2.8 (LLVM 9.0.0), Compiler: GCC 9.2.1 20191008, File-System: ext4, Screen Resolution: 1920x1080 2: Processor: AMD Ryzen 7 2700 Eight-Core @ 3.20GHz (8 Cores / 16 Threads), Motherboard: Gigabyte AB350N-Gaming WIFI-CF (F20 BIOS), Chipset: AMD 17h, Memory: 16GB, Disk: 120GB ADATA SU700, Graphics: HIS AMD Radeon HD 6450/7450/8450 / R5 230 OEM 1GB, Audio: AMD Caicos HDMI Audio, Monitor: DELL S2409W, Network: Realtek RTL8111/8168/8411 + Intel 3165 OS: Ubuntu 19.10, Kernel: 5.9.0-050900rc7daily20201004-generic (x86_64) 20201003, Desktop: GNOME Shell 3.34.1, Display Server: X Server 1.20.5, Display Driver: modesetting 1.20.5, OpenGL: 3.3 Mesa 19.2.8 (LLVM 9.0.0), Compiler: GCC 9.2.1 20191008, File-System: ext4, Screen Resolution: 1920x1080 3: Processor: AMD Ryzen 7 2700 Eight-Core @ 3.20GHz (8 Cores / 16 Threads), Motherboard: Gigabyte AB350N-Gaming WIFI-CF (F20 BIOS), Chipset: AMD 17h, Memory: 16GB, Disk: 120GB ADATA SU700, Graphics: HIS AMD Radeon HD 6450/7450/8450 / R5 230 OEM 1GB, Audio: AMD Caicos HDMI Audio, Monitor: DELL S2409W, Network: Realtek RTL8111/8168/8411 + Intel 3165 OS: Ubuntu 19.10, Kernel: 5.9.0-050900rc7daily20201004-generic (x86_64) 20201003, Desktop: GNOME Shell 3.34.1, Display Server: X Server 1.20.5, Display Driver: modesetting 1.20.5, OpenGL: 3.3 Mesa 19.2.8 (LLVM 9.0.0), Compiler: GCC 9.2.1 20191008, File-System: ext4, Screen Resolution: 1920x1080 oneDNN 2.0 Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 8.30216 |================================================================= 2 . 8.38407 |================================================================== 3 . 8.31945 |================================================================= oneDNN 2.0 Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 12.05 |==================================================================== 2 . 11.99 |==================================================================== 3 . 12.02 |==================================================================== oneDNN 2.0 Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 6.41140 |================================================================= 2 . 6.47135 |================================================================== 3 . 6.44652 |================================================================== oneDNN 2.0 Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 2.61603 |================================================================== 2 . 2.56768 |================================================================= 3 . 2.56705 |================================================================= oneDNN 2.0 Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 23.75 |==================================================================== 2 . 23.76 |==================================================================== 3 . 23.73 |==================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 10.26 |================================================================== 2 . 10.53 |==================================================================== 3 . 10.32 |=================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 14.40 |==================================================================== 2 . 14.40 |==================================================================== 3 . 14.38 |==================================================================== oneDNN 2.0 Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 24.24 |==================================================================== 2 . 24.19 |==================================================================== 3 . 24.19 |==================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 12.83 |============================================================== 2 . 14.09 |==================================================================== 3 . 12.26 |=========================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 12.62 |==================================================================== 2 . 12.66 |==================================================================== 3 . 12.62 |==================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 8137.11 |================================================================= 2 . 8199.88 |================================================================== 3 . 8159.58 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 4324.22 |================================================================== 2 . 4339.60 |================================================================== 3 . 4321.11 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 8213.83 |================================================================== 2 . 8222.43 |================================================================== 3 . 8188.93 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 4329.68 |================================================================== 2 . 4340.57 |================================================================== 3 . 4336.59 |================================================================== oneDNN 2.0 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 5.16128 |================================================================== 2 . 5.12423 |================================================================== 3 . 5.12143 |================================================================= oneDNN 2.0 Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 8201.12 |================================================================== 2 . 8212.31 |================================================================== 3 . 8165.85 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 4336.81 |================================================================== 2 . 4335.51 |================================================================== 3 . 4338.29 |================================================================== oneDNN 2.0 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 6.04440 |================================================================== 2 . 6.04323 |================================================================== 3 . 6.08775 |==================================================================