3400g long time AMD Ryzen 5 3400G testing with a ASUS PRIME B450M-A (2006 BIOS) and ASUS AMD Picasso on Ubuntu 19.10 via the Phoronix Test Suite. a: Processor: AMD Ryzen 5 3400G @ 3.70GHz (4 Cores / 8 Threads), Motherboard: ASUS PRIME B450M-A (2006 BIOS), Chipset: AMD Raven/Raven2, Memory: 2 x 8192 MB DDR4-3000MT/s CRUCIAL, Disk: 29GB INTEL MEMPEK1W032GA + 4 x 6001GB Seagate ST6000VN0033-2EE, Graphics: ASUS AMD Picasso (1400/1500MHz), Audio: AMD Raven/Raven2/Fenghuang, Monitor: SyncMaster, Network: Realtek RTL8111/8168/8411 OS: Ubuntu 19.10, Kernel: 5.3.0-46-generic (x86_64), Desktop: GNOME Shell 3.34.1, Display Server: X Server, Vulkan: 1.1.107, Compiler: GCC 9.2.1 20191008, File-System: ext4, Screen Resolution: 1280x800 b: Processor: AMD Ryzen 5 3400G @ 3.70GHz (4 Cores / 8 Threads), Motherboard: ASUS PRIME B450M-A (2006 BIOS), Chipset: AMD Raven/Raven2, Memory: 2 x 8192 MB DDR4-3000MT/s CRUCIAL, Disk: 29GB INTEL MEMPEK1W032GA + 4 x 6001GB Seagate ST6000VN0033-2EE, Graphics: ASUS AMD Picasso (1400/1500MHz), Audio: AMD Raven/Raven2/Fenghuang, Monitor: SyncMaster, Network: Realtek RTL8111/8168/8411 OS: Ubuntu 19.10, Kernel: 5.3.0-46-generic (x86_64), Desktop: GNOME Shell 3.34.1, Display Server: X Server, Vulkan: 1.1.107, Compiler: GCC 9.2.1 20191008, File-System: ext4, Screen Resolution: 1280x800 c: Processor: AMD Ryzen 5 3400G @ 3.70GHz (4 Cores / 8 Threads), Motherboard: ASUS PRIME B450M-A (2006 BIOS), Chipset: AMD Raven/Raven2, Memory: 2 x 8192 MB DDR4-3000MT/s CRUCIAL, Disk: 29GB INTEL MEMPEK1W032GA + 4 x 6001GB Seagate ST6000VN0033-2EE, Graphics: ASUS AMD Picasso (1400/1500MHz), Audio: AMD Raven/Raven2/Fenghuang, Monitor: SyncMaster, Network: Realtek RTL8111/8168/8411 OS: Ubuntu 19.10, Kernel: 5.3.0-46-generic (x86_64), Desktop: GNOME Shell 3.34.1, Display Server: X Server, Vulkan: 1.1.107, Compiler: GCC 9.2.1 20191008, File-System: ext4, Screen Resolution: 1280x800 OpenVKL 1.3.1 Benchmark: vklBenchmark ISPC Items / Sec > Higher Is Better a . 41 |================================================================= b . 45 |======================================================================= c . 45 |======================================================================= OpenVKL 1.3.1 Benchmark: vklBenchmark Scalar Items / Sec > Higher Is Better a . 25 |================================================================== b . 27 |======================================================================= c . 27 |======================================================================= Timed Linux Kernel Compilation 6.1 Build: defconfig Seconds < Lower Is Better a . 267.43 |=================================================================== b . 251.17 |=============================================================== c . 251.61 |=============================================================== Timed Linux Kernel Compilation 6.1 Build: allmodconfig Seconds < Lower Is Better oneDNN 3.0 Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 17.09 |==================================================================== b . 17.07 |==================================================================== c . 16.94 |=================================================================== oneDNN 3.0 Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 11.47 |==================================================================== b . 11.47 |==================================================================== c . 11.48 |==================================================================== oneDNN 3.0 Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 12.24 |================================================================== b . 12.06 |================================================================= c . 12.59 |==================================================================== oneDNN 3.0 Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 3.27473 |================================================================== b . 3.29098 |================================================================== c . 3.27864 |================================================================== oneDNN 3.0 Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 3.0 Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 3.0 Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 20.30 |=================================================================== b . 20.40 |==================================================================== c . 20.52 |==================================================================== oneDNN 3.0 Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 20.47 |================================================================== b . 21.00 |==================================================================== c . 20.90 |==================================================================== oneDNN 3.0 Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 27.15 |================================================================== b . 27.86 |==================================================================== c . 27.28 |=================================================================== oneDNN 3.0 Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 23.25 |=================================================================== b . 22.93 |================================================================== c . 23.69 |==================================================================== oneDNN 3.0 Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 14.29 |=================================================================== b . 14.61 |==================================================================== c . 14.48 |=================================================================== oneDNN 3.0 Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 20.31 |================================================================= b . 21.15 |==================================================================== c . 19.80 |================================================================ oneDNN 3.0 Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 18924.5 |================================================================== b . 14571.5 |=================================================== c . 14731.5 |=================================================== oneDNN 3.0 Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 9986.17 |================================================================== b . 8874.35 |=========================================================== c . 9029.64 |============================================================ oneDNN 3.0 Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 19386.1 |================================================================== b . 18715.9 |================================================================ c . 18849.9 |================================================================ oneDNN 3.0 Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 3.0 Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 3.0 Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 3.0 Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 9810.43 |================================================================== b . 8728.57 |=========================================================== c . 8811.38 |=========================================================== oneDNN 3.0 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 7.22278 |================================================================== b . 6.86696 |=============================================================== c . 7.07990 |================================================================= oneDNN 3.0 Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better a . 19461.6 |================================================================== b . 18253.8 |============================================================== c . 18179.3 |============================================================== oneDNN 3.0 Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better a . 10020.56 |================================================================= b . 9701.35 |=============================================================== c . 9712.67 |=============================================================== oneDNN 3.0 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 5.29396 |========================================================== b . 5.93079 |================================================================= c . 5.99041 |================================================================== oneDNN 3.0 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better