3200u april AMD Ryzen 3 3200U testing with a MOTILE PF4PU1F (N.1.03 BIOS) and AMD Radeon Vega 3 512MB on Ubuntu 20.04 via the Phoronix Test Suite. A: Processor: AMD Ryzen 3 3200U @ 2.60GHz (2 Cores / 4 Threads), Motherboard: MOTILE PF4PU1F (N.1.03 BIOS), Chipset: AMD Raven/Raven2, Memory: 3584MB, Disk: 128GB BIWIN SSD, Graphics: AMD Radeon Vega 3 512MB (1200/1200MHz), Audio: AMD Raven/Raven2/Fenghuang, Network: Realtek RTL8111/8168/8411 + Intel Dual Band-AC 3168NGW OS: Ubuntu 20.04, Kernel: 5.15.0-051500-generic (x86_64), Desktop: GNOME Shell 3.36.9, Display Server: X Server 1.20.13, OpenGL: 4.6 Mesa 22.0.0-devel (git-9cb9101 2022-01-08 focal-oibaf-ppa) (LLVM 13.0.0 DRM 3.42), Compiler: GCC 9.4.0, File-System: ext4, Screen Resolution: 1920x1080 B: Processor: AMD Ryzen 3 3200U @ 2.60GHz (2 Cores / 4 Threads), Motherboard: MOTILE PF4PU1F (N.1.03 BIOS), Chipset: AMD Raven/Raven2, Memory: 3584MB, Disk: 128GB BIWIN SSD, Graphics: AMD Radeon Vega 3 512MB (1200/1200MHz), Audio: AMD Raven/Raven2/Fenghuang, Network: Realtek RTL8111/8168/8411 + Intel Dual Band-AC 3168NGW OS: Ubuntu 20.04, Kernel: 5.15.0-051500-generic (x86_64), Desktop: GNOME Shell 3.36.9, Display Server: X Server 1.20.13, OpenGL: 4.6 Mesa 22.0.0-devel (git-9cb9101 2022-01-08 focal-oibaf-ppa) (LLVM 13.0.0 DRM 3.42), Compiler: GCC 9.4.0, File-System: ext4, Screen Resolution: 1920x1080 C: Processor: AMD Ryzen 3 3200U @ 2.60GHz (2 Cores / 4 Threads), Motherboard: MOTILE PF4PU1F (N.1.03 BIOS), Chipset: AMD Raven/Raven2, Memory: 3584MB, Disk: 128GB BIWIN SSD, Graphics: AMD Radeon Vega 3 512MB (1200/1200MHz), Audio: AMD Raven/Raven2/Fenghuang, Network: Realtek RTL8111/8168/8411 + Intel Dual Band-AC 3168NGW OS: Ubuntu 20.04, Kernel: 5.15.0-051500-generic (x86_64), Desktop: GNOME Shell 3.36.9, Display Server: X Server 1.20.13, OpenGL: 4.6 Mesa 22.0.0-devel (git-9cb9101 2022-01-08 focal-oibaf-ppa) (LLVM 13.0.0 DRM 3.42), Compiler: GCC 9.4.0, File-System: ext4, Screen Resolution: 1920x1080 fast-cli Internet Download Speed Mbit/s > Higher Is Better A . 80 |======================================================================= B . 70 |============================================================== C . 53 |=============================================== fast-cli Internet Upload Speed Mbit/s > Higher Is Better A . 4.9 |====================================================================== B . 4.8 |===================================================================== C . 4.8 |===================================================================== fast-cli Internet Latency ms < Lower Is Better A . 25 |======================================================================= B . 25 |======================================================================= C . 25 |======================================================================= fast-cli Internet Loaded Latency (Bufferbloat) ms < Lower Is Better A . 89 |=========================== B . 188 |======================================================== C . 234 |====================================================================== speedtest-cli 2.1.3 Internet Download Speed Mbit/s > Higher Is Better A . 70.15 |=================================================================== B . 71.72 |==================================================================== C . 49.96 |=============================================== speedtest-cli 2.1.3 Internet Upload Speed Mbit/s > Higher Is Better A . 5.89 |===================================================================== B . 5.92 |===================================================================== C . 5.68 |================================================================== speedtest-cli 2.1.3 Internet Latency ms < Lower Is Better A . 42.54 |================================================================== B . 29.93 |============================================== C . 44.09 |==================================================================== perf-bench Benchmark: Epoll Wait ops/sec > Higher Is Better A . 495289 |=================================================================== B . 493971 |=================================================================== C . 493722 |=================================================================== perf-bench Benchmark: Futex Hash ops/sec > Higher Is Better A . 4479948 |================================================================== B . 4373853 |================================================================ C . 4405813 |================================================================= perf-bench Benchmark: Memcpy 1MB GB/sec > Higher Is Better A . 14.94 |==================================================================== B . 14.63 |=================================================================== C . 14.82 |=================================================================== perf-bench Benchmark: Memset 1MB GB/sec > Higher Is Better A . 45.84 |==================================================================== B . 44.37 |================================================================== C . 44.10 |================================================================= perf-bench Benchmark: Sched Pipe ops/sec > Higher Is Better A . 218987 |=================================================================== B . 213964 |================================================================= C . 211366 |================================================================= perf-bench Benchmark: Futex Lock-Pi ops/sec > Higher Is Better A . 3725 |==================================================================== B . 3747 |===================================================================== C . 3755 |===================================================================== perf-bench Benchmark: Syscall Basic ops/sec > Higher Is Better A . 12457259 |============================================================= B . 13171066 |================================================================= C . 13256292 |================================================================= oneDNN 2.6 Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU ms < Lower Is Better A . 36.87 |================================================================= B . 38.17 |==================================================================== C . 38.39 |==================================================================== oneDNN 2.6 Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU ms < Lower Is Better A . 16.85 |==================================================================== B . 16.83 |==================================================================== C . 16.94 |==================================================================== oneDNN 2.6 Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better A . 28.55 |=================================================================== B . 28.77 |==================================================================== C . 28.63 |==================================================================== oneDNN 2.6 Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better A . 6.68978 |================================================================= B . 6.75147 |================================================================== C . 6.73920 |================================================================== oneDNN 2.6 Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 2.6 Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 2.6 Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU ms < Lower Is Better A . 48.30 |================================================================== B . 49.08 |=================================================================== C . 49.50 |==================================================================== oneDNN 2.6 Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU ms < Lower Is Better A . 55.87 |=================================================================== B . 56.82 |==================================================================== C . 56.89 |==================================================================== oneDNN 2.6 Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU ms < Lower Is Better A . 64.40 |================================================================== B . 65.65 |==================================================================== C . 65.96 |==================================================================== oneDNN 2.6 Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better A . 68.55 |=================================================================== B . 69.82 |==================================================================== C . 69.76 |==================================================================== oneDNN 2.6 Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better A . 37.06 |================================================================= B . 39.05 |==================================================================== C . 38.74 |=================================================================== oneDNN 2.6 Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better A . 49.06 |=================================================================== B . 49.56 |=================================================================== C . 50.03 |==================================================================== oneDNN 2.6 Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU ms < Lower Is Better A . 38761.8 |================================================================ B . 39175.7 |================================================================= C . 39745.0 |================================================================== oneDNN 2.6 Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU ms < Lower Is Better A . 20075.5 |================================================================ B . 20542.5 |================================================================== C . 20661.9 |================================================================== oneDNN 2.6 Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better A . 38532.4 |================================================================ B . 39394.2 |================================================================= C . 39940.8 |================================================================== oneDNN 2.6 Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 2.6 Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 2.6 Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 2.6 Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better A . 20225.0 |================================================================ B . 20561.2 |================================================================= C . 20752.7 |================================================================== oneDNN 2.6 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU ms < Lower Is Better A . 16.45 |=================================================================== B . 16.65 |==================================================================== C . 16.68 |==================================================================== oneDNN 2.6 Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better A . 38516.6 |=============================================================== B . 39570.9 |================================================================= C . 40176.8 |================================================================== oneDNN 2.6 Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better A . 20185.1 |=============================================================== B . 20641.7 |================================================================= C . 20988.0 |================================================================== oneDNN 2.6 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better A . 13.34 |==================================================================== B . 13.33 |==================================================================== C . 13.38 |==================================================================== oneDNN 2.6 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better Java JMH Throughput Ops/s > Higher Is Better A . 2843596070.78 |========================================================== B . 2953181899.58 |============================================================ C . 2961014078.74 |============================================================