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.
Compare your own system(s) to this result file with the
Phoronix Test Suite by running the command:
phoronix-test-suite benchmark 2009250-PTS-3900XNEW05
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
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 |====================================================================
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 |================================================================
LibRaw 0.20
Post-Processing Benchmark
Mpix/sec > Higher Is Better
1 . 42.42 |===================================================================
2 . 42.32 |===================================================================
3 . 42.74 |====================================================================
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 |=====================================================================
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 |=====================================================================
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 |====================================================================
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 |===================================================================
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 |==================================================================
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 |====================================================================