fg
AMD Ryzen Z1 Extreme testing with a ASUS RC71L v1.0 (RC71L.319 BIOS) and ASUS AMD Phoenix1 4GB on Ubuntu 23.10 via the Phoronix Test Suite.


a: 

	Processor: AMD Ryzen Z1 Extreme @ 5.29GHz (8 Cores / 16 Threads), Motherboard: ASUS RC71L v1.0 (RC71L.319 BIOS), Chipset: AMD Device 14e8, Memory: 12GB, Disk: 512GB Micron_2400_MTFDKBK512QFM + 1000GB RTL9210B-CG, Graphics: ASUS AMD Phoenix1 4GB (2700/800MHz), Audio: AMD Rembrandt Radeon HD Audio, Network: MEDIATEK MT7922 802.11ax PCI

	OS: Ubuntu 23.10, Kernel: 6.7.0-060700rc5-generic (x86_64), Desktop: GNOME Shell 45.1, Display Server: X Server + Wayland, OpenGL: 4.6 Mesa 24.0~git2401050600.91ec3c~oibaf~m (git-91ec3cc 2024-01-05 mantic-oibaf-ppa) (LLVM 16.0.6 DRM 3.56), Compiler: GCC 13.2.0, File-System: ext4, Screen Resolution: 1920x1080

n: 

	Processor: AMD Ryzen Z1 Extreme @ 5.29GHz (8 Cores / 16 Threads), Motherboard: ASUS RC71L v1.0 (RC71L.319 BIOS), Chipset: AMD Device 14e8, Memory: 12GB, Disk: 512GB Micron_2400_MTFDKBK512QFM + 1000GB RTL9210B-CG, Graphics: ASUS AMD Phoenix1 4GB (2700/800MHz), Audio: AMD Rembrandt Radeon HD Audio, Network: MEDIATEK MT7922 802.11ax PCI

	OS: Ubuntu 23.10, Kernel: 6.7.0-060700rc5-generic (x86_64), Desktop: GNOME Shell 45.1, Display Server: X Server + Wayland, OpenGL: 4.6 Mesa 24.0~git2401050600.91ec3c~oibaf~m (git-91ec3cc 2024-01-05 mantic-oibaf-ppa) (LLVM 16.0.6 DRM 3.56), Compiler: GCC 13.2.0, File-System: ext4, Screen Resolution: 1920x1080


PyTorch 2.1
Device: CPU - Batch Size: 1 - Model: ResNet-50
batches/sec > Higher Is Better
a . 47.25 |====================================================================
n . 47.06 |====================================================================


PyTorch 2.1
Device: CPU - Batch Size: 1 - Model: ResNet-152
batches/sec > Higher Is Better
a . 19.53 |====================================================================
n . 19.10 |===================================================================


PyTorch 2.1
Device: CPU - Batch Size: 16 - Model: ResNet-50
batches/sec > Higher Is Better
a . 28.81 |====================================================================
n . 28.10 |==================================================================


PyTorch 2.1
Device: CPU - Batch Size: 16 - Model: ResNet-152
batches/sec > Higher Is Better
a . 12.20 |====================================================================
n . 11.67 |=================================================================


PyTorch 2.1
Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l
batches/sec > Higher Is Better
a . 11.45 |====================================================================
n . 11.31 |===================================================================


PyTorch 2.1
Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l
batches/sec > Higher Is Better
a . 7.85 |=====================================================================
n . 7.84 |=====================================================================


Quicksilver 20230818
Input: CTS2
Figure Of Merit > Higher Is Better
a . 10550000 |=================================================================
n . 10490000 |=================================================================


Quicksilver 20230818
Input: CORAL2 P1
Figure Of Merit > Higher Is Better
a . 10970000 |=================================================================
n . 10960000 |=================================================================


Quicksilver 20230818
Input: CORAL2 P2
Figure Of Merit > Higher Is Better
a . 20470000 |=================================================================
n . 20490000 |=================================================================


TensorFlow 2.12
Device: CPU - Batch Size: 1 - Model: VGG-16
images/sec > Higher Is Better
a . 3.74 |=====================================================================
n . 3.76 |=====================================================================


TensorFlow 2.12
Device: CPU - Batch Size: 1 - Model: AlexNet
images/sec > Higher Is Better
a . 12.72 |====================================================================
n . 12.70 |====================================================================


TensorFlow 2.12
Device: CPU - Batch Size: 16 - Model: VGG-16
images/sec > Higher Is Better
a . 7.13 |=====================================================================
n . 7.10 |=====================================================================


TensorFlow 2.12
Device: CPU - Batch Size: 16 - Model: AlexNet
images/sec > Higher Is Better
a . 92.76 |====================================================================
n . 92.49 |====================================================================


TensorFlow 2.12
Device: CPU - Batch Size: 1 - Model: GoogLeNet
images/sec > Higher Is Better
a . 41.67 |====================================================================
n . 41.47 |====================================================================


TensorFlow 2.12
Device: CPU - Batch Size: 1 - Model: ResNet-50
images/sec > Higher Is Better
a . 12.19 |====================================================================
n . 12.17 |====================================================================


TensorFlow 2.12
Device: CPU - Batch Size: 16 - Model: GoogLeNet
images/sec > Higher Is Better
a . 67.79 |====================================================================
n . 67.46 |====================================================================


TensorFlow 2.12
Device: CPU - Batch Size: 16 - Model: ResNet-50
images/sec > Higher Is Better
a . 22.62 |====================================================================
n . 22.61 |====================================================================


Y-Cruncher 0.8.3
Pi Digits To Calculate: 1B
Seconds < Lower Is Better
a . 37.20 |====================================================================
n . 36.76 |===================================================================


Y-Cruncher 0.8.3
Pi Digits To Calculate: 500M
Seconds < Lower Is Better
a . 16.16 |====================================================================
n . 16.17 |====================================================================