3900X New Tests AMD Ryzen 9 3900X 12-Core testing with a ASUS TUF GAMING X570-PLUS (WI-FI) (2203 BIOS) and MSI AMD Radeon RX 470/480/570/570X/580/580X/590 8GB on Ubuntu 20.04 via the Phoronix Test Suite. 1: Processor: AMD Ryzen 9 3900X 12-Core @ 3.80GHz (12 Cores / 24 Threads), Motherboard: ASUS TUF GAMING X570-PLUS (WI-FI) (2203 BIOS), Chipset: AMD Starship/Matisse, Memory: 16GB, Disk: Samsung SSD 970 EVO Plus 250GB, Graphics: MSI AMD Radeon RX 470/480/570/570X/580/580X/590 8GB (1366/2000MHz), Audio: AMD Ellesmere HDMI Audio, Monitor: G237HL, Network: Realtek RTL8111/8168/8411 + Intel-AC 9260 OS: Ubuntu 20.04, Kernel: 5.9.0-050900rc6daily20200922-generic (x86_64) 20200921, Desktop: GNOME Shell 3.36.4, Display Server: X Server 1.20.8, Display Driver: modesetting 1.20.8, OpenGL: 4.6 Mesa 20.2.0-devel (git-64cdc13 2020-07-02 focal-oibaf-ppa) (LLVM 10.0.0), Vulkan: 1.2.131, Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080 2: Processor: AMD Ryzen 9 3900X 12-Core @ 3.80GHz (12 Cores / 24 Threads), Motherboard: ASUS TUF GAMING X570-PLUS (WI-FI) (2203 BIOS), Chipset: AMD Starship/Matisse, Memory: 16GB, Disk: Samsung SSD 970 EVO Plus 250GB, Graphics: MSI AMD Radeon RX 470/480/570/570X/580/580X/590 8GB (1366/2000MHz), Audio: AMD Ellesmere HDMI Audio, Monitor: G237HL, Network: Realtek RTL8111/8168/8411 + Intel-AC 9260 OS: Ubuntu 20.04, Kernel: 5.9.0-050900rc6daily20200922-generic (x86_64) 20200921, Desktop: GNOME Shell 3.36.4, Display Server: X Server 1.20.8, Display Driver: modesetting 1.20.8, OpenGL: 4.6 Mesa 20.2.0-devel (git-64cdc13 2020-07-02 focal-oibaf-ppa) (LLVM 10.0.0), Vulkan: 1.2.131, Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080 3: Processor: AMD Ryzen 9 3900X 12-Core @ 3.80GHz (12 Cores / 24 Threads), Motherboard: ASUS TUF GAMING X570-PLUS (WI-FI) (2203 BIOS), Chipset: AMD Starship/Matisse, Memory: 16GB, Disk: Samsung SSD 970 EVO Plus 250GB, Graphics: MSI AMD Radeon RX 470/480/570/570X/580/580X/590 8GB (1366/2000MHz), Audio: AMD Ellesmere HDMI Audio, Monitor: G237HL, Network: Realtek RTL8111/8168/8411 + Intel-AC 9260 OS: Ubuntu 20.04, Kernel: 5.9.0-050900rc6daily20200922-generic (x86_64) 20200921, Desktop: GNOME Shell 3.36.4, Display Server: X Server 1.20.8, Display Driver: modesetting 1.20.8, OpenGL: 4.6 Mesa 20.2.0-devel (git-64cdc13 2020-07-02 focal-oibaf-ppa) (LLVM 10.0.0), Vulkan: 1.2.131, Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080 RealSR-NCNN 20200818 Scale: 4x - TAA: No Seconds < Lower Is Better 1 . 15.58 |==================================================================== 2 . 15.61 |==================================================================== 3 . 15.62 |==================================================================== RealSR-NCNN 20200818 Scale: 4x - TAA: Yes Seconds < Lower Is Better 1 . 111.64 |=================================================================== 2 . 111.93 |=================================================================== 3 . 111.70 |=================================================================== OSBench Test: Create Files us Per Event < Lower Is Better 1 . 12.17 |=================================================================== 2 . 12.42 |==================================================================== 3 . 12.05 |================================================================== OSBench Test: Create Threads us Per Event < Lower Is Better 1 . 14.06 |==================================================================== 2 . 12.55 |============================================================= 3 . 12.51 |============================================================= OSBench Test: Launch Programs us Per Event < Lower Is Better 1 . 41.74 |==================================================================== 2 . 40.91 |=================================================================== 3 . 40.99 |=================================================================== OSBench Test: Create Processes us Per Event < Lower Is Better 1 . 33.16 |==================================================================== 2 . 32.79 |=================================================================== 3 . 32.37 |================================================================== OSBench Test: Memory Allocations Ns Per Event < Lower Is Better 1 . 65.43 |================================================================= 2 . 68.54 |==================================================================== 3 . 68.11 |==================================================================== WebP Image Encode 1.1 Encode Settings: Default Encode Time - Seconds < Lower Is Better 1 . 1.455 |==================================================================== 2 . 1.433 |=================================================================== 3 . 1.445 |==================================================================== WebP Image Encode 1.1 Encode Settings: Quality 100 Encode Time - Seconds < Lower Is Better 1 . 2.240 |==================================================================== 2 . 2.175 |================================================================== 3 . 2.223 |=================================================================== WebP Image Encode 1.1 Encode Settings: Quality 100, Lossless Encode Time - Seconds < Lower Is Better 1 . 15.39 |=================================================================== 2 . 15.54 |==================================================================== 3 . 15.20 |=================================================================== WebP Image Encode 1.1 Encode Settings: Quality 100, Highest Compression Encode Time - Seconds < Lower Is Better 1 . 6.944 |==================================================================== 2 . 6.938 |==================================================================== 3 . 6.780 |================================================================== WebP Image Encode 1.1 Encode Settings: Quality 100, Lossless, Highest Compression Encode Time - Seconds < Lower Is Better 1 . 32.02 |================================================================== 2 . 32.53 |=================================================================== 3 . 33.09 |==================================================================== LibRaw 0.20 Post-Processing Benchmark Mpix/sec > Higher Is Better 1 . 42.42 |=================================================================== 2 . 42.32 |=================================================================== 3 . 42.74 |==================================================================== dcraw RAW To PPM Image Conversion Seconds < Lower Is Better 1 . 39.51 |==================================================================== 2 . 39.09 |=================================================================== 3 . 38.50 |================================================================== eSpeak-NG Speech Engine 20200907 Text-To-Speech Synthesis Seconds < Lower Is Better 1 . 27.30 |=================================================================== 2 . 27.32 |==================================================================== 3 . 27.51 |==================================================================== MPV Video Input: Big Buck Bunny Sunflower 4K - Decode: Software Only FPS > Higher Is Better 1 . 698.08 |=================================================================== 2 . 700.67 |=================================================================== 3 . 697.91 |=================================================================== MPV Video Input: Big Buck Bunny Sunflower 1080p - Decode: Software Only FPS > Higher Is Better 1 . 2345.26 |================================================================== 2 . 2334.36 |================================================================== 3 . 2327.51 |================================================================== NCNN 20200916 Target: CPU - Model: squeezenet ms < Lower Is Better 1 . 16.32 |==================================================================== 2 . 15.93 |================================================================== 3 . 16.10 |=================================================================== NCNN 20200916 Target: CPU - Model: mobilenet ms < Lower Is Better 1 . 16.68 |==================================================================== 2 . 16.43 |=================================================================== 3 . 16.54 |=================================================================== NCNN 20200916 Target: CPU-v2-v2 - Model: mobilenet-v2 ms < Lower Is Better 1 . 5.45 |===================================================================== 2 . 5.41 |==================================================================== 3 . 5.43 |===================================================================== NCNN 20200916 Target: CPU-v3-v3 - Model: mobilenet-v3 ms < Lower Is Better 1 . 4.78 |===================================================================== 2 . 4.79 |===================================================================== 3 . 4.78 |===================================================================== NCNN 20200916 Target: CPU - Model: shufflenet-v2 ms < Lower Is Better 1 . 4.90 |===================================================================== 2 . 4.90 |===================================================================== 3 . 4.88 |===================================================================== NCNN 20200916 Target: CPU - Model: mnasnet ms < Lower Is Better 1 . 4.85 |===================================================================== 2 . 4.83 |===================================================================== 3 . 4.84 |===================================================================== NCNN 20200916 Target: CPU - Model: efficientnet-b0 ms < Lower Is Better 1 . 6.65 |===================================================================== 2 . 6.63 |===================================================================== 3 . 6.63 |===================================================================== NCNN 20200916 Target: CPU - Model: blazeface ms < Lower Is Better 1 . 1.95 |===================================================================== 2 . 1.96 |===================================================================== 3 . 1.96 |===================================================================== NCNN 20200916 Target: CPU - Model: googlenet ms < Lower Is Better 1 . 17.16 |==================================================================== 2 . 17.13 |==================================================================== 3 . 17.25 |==================================================================== NCNN 20200916 Target: CPU - Model: vgg16 ms < Lower Is Better 1 . 67.17 |==================================================================== 2 . 66.98 |==================================================================== 3 . 67.37 |==================================================================== NCNN 20200916 Target: CPU - Model: resnet18 ms < Lower Is Better 1 . 16.40 |==================================================================== 2 . 16.31 |=================================================================== 3 . 16.44 |==================================================================== NCNN 20200916 Target: CPU - Model: alexnet ms < Lower Is Better 1 . 16.24 |==================================================================== 2 . 16.31 |==================================================================== 3 . 16.21 |==================================================================== NCNN 20200916 Target: CPU - Model: resnet50 ms < Lower Is Better 1 . 81.33 |==================================================================== 2 . 27.24 |======================= 3 . 27.69 |======================= NCNN 20200916 Target: CPU - Model: yolov4-tiny ms < Lower Is Better 1 . 28.70 |==================================================================== 2 . 28.34 |=================================================================== 3 . 28.55 |==================================================================== NCNN 20200916 Target: Vulkan GPU - Model: squeezenet ms < Lower Is Better 1 . 5.71 |===================================================================== 2 . 5.65 |==================================================================== 3 . 5.61 |==================================================================== NCNN 20200916 Target: Vulkan GPU - Model: mobilenet ms < Lower Is Better 1 . 6.11 |===================================================================== 2 . 6.05 |==================================================================== 3 . 6.06 |==================================================================== NCNN 20200916 Target: Vulkan GPU-v2-v2 - Model: mobilenet-v2 ms < Lower Is Better 1 . 2.90 |==================================================================== 2 . 2.93 |===================================================================== 3 . 2.88 |==================================================================== NCNN 20200916 Target: Vulkan GPU-v3-v3 - Model: mobilenet-v3 ms < Lower Is Better 1 . 4.07 |===================================================================== 2 . 4.08 |===================================================================== 3 . 4.09 |===================================================================== NCNN 20200916 Target: Vulkan GPU - Model: shufflenet-v2 ms < Lower Is Better 1 . 2.49 |===================================================================== 2 . 2.48 |===================================================================== 3 . 2.49 |===================================================================== NCNN 20200916 Target: Vulkan GPU - Model: mnasnet ms < Lower Is Better 1 . 3.06 |===================================================================== 2 . 3.04 |===================================================================== 3 . 3.06 |===================================================================== NCNN 20200916 Target: Vulkan GPU - Model: efficientnet-b0 ms < Lower Is Better 1 . 11.03 |==================================================================== 2 . 10.90 |=================================================================== 3 . 11.06 |==================================================================== NCNN 20200916 Target: Vulkan GPU - Model: blazeface ms < Lower Is Better 1 . 0.93 |==================================================================== 2 . 0.93 |==================================================================== 3 . 0.94 |===================================================================== NCNN 20200916 Target: Vulkan GPU - Model: googlenet ms < Lower Is Better 1 . 6.32 |===================================================================== 2 . 6.33 |===================================================================== 3 . 6.32 |===================================================================== NCNN 20200916 Target: Vulkan GPU - Model: vgg16 ms < Lower Is Better 1 . 15.15 |==================================================================== 2 . 15.11 |==================================================================== 3 . 15.06 |==================================================================== NCNN 20200916 Target: Vulkan GPU - Model: resnet18 ms < Lower Is Better 1 . 2.73 |==================================================================== 2 . 2.76 |===================================================================== 3 . 2.73 |==================================================================== NCNN 20200916 Target: Vulkan GPU - Model: alexnet ms < Lower Is Better 1 . 5.58 |===================================================================== 2 . 5.58 |===================================================================== 3 . 5.58 |===================================================================== NCNN 20200916 Target: Vulkan GPU - Model: resnet50 ms < Lower Is Better 1 . 7.71 |==================================================================== 2 . 7.74 |==================================================================== 3 . 7.85 |===================================================================== NCNN 20200916 Target: Vulkan GPU - Model: yolov4-tiny ms < Lower Is Better 1 . 8.61 |===================================================================== 2 . 8.57 |===================================================================== 3 . 8.58 |===================================================================== TNN 0.2.3 Target: CPU - Model: MobileNet v2 ms < Lower Is Better 1 . 253.97 |=================================================================== 2 . 250.08 |================================================================== 3 . 252.15 |=================================================================== TNN 0.2.3 Target: CPU - Model: SqueezeNet v1.1 ms < Lower Is Better 1 . 233.48 |=================================================================== 2 . 233.49 |=================================================================== 3 . 230.64 |================================================================== OpenCV 4.4 Test: Features 2D ms < Lower Is Better 1 . 143206 |=================================================================== 2 . 135808 |================================================================ 3 . 142960 |=================================================================== OpenCV 4.4 Test: Object Detection ms < Lower Is Better 1 . 66325 |==================================================================== 2 . 65007 |================================================================== 3 . 66757 |==================================================================== OpenCV 4.4 Test: DNN - Deep Neural Network ms < Lower Is Better 1 . 4384 |==================================================================== 2 . 4354 |==================================================================== 3 . 4443 |===================================================================== InfluxDB 1.8.2 Concurrent Streams: 4 - Batch Size: 10000 - Tags: 2,5000,1 - Points Per Series: 10000 val/sec > Higher Is Better 1 . 1310447.9 |================================================================ 2 . 1313053.3 |================================================================ 3 . 1313875.7 |================================================================ InfluxDB 1.8.2 Concurrent Streams: 64 - Batch Size: 10000 - Tags: 2,5000,1 - Points Per Series: 10000 val/sec > Higher Is Better 1 . 1444861.4 |================================================================ 2 . 1447435.3 |================================================================ 3 . 1445347.6 |================================================================ InfluxDB 1.8.2 Concurrent Streams: 1024 - Batch Size: 10000 - Tags: 2,5000,1 - Points Per Series: 10000 val/sec > Higher Is Better 1 . 1472018.5 |================================================================ 2 . 1468089.0 |================================================================ 3 . 1471736.4 |================================================================