AMD Ryzen 5 5500U testing with a NB01 TUXEDO Aura 15 Gen2 NL5xNU (1.07.11RTR1 BIOS) and AMD Lucienne 512MB on Tuxedo 22.04 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 2403275-NE-TESTS110635
tests
AMD Ryzen 5 5500U testing with a NB01 TUXEDO Aura 15 Gen2 NL5xNU (1.07.11RTR1 BIOS) and AMD Lucienne 512MB on Tuxedo 22.04 via the Phoronix Test Suite.
a:
Processor: AMD Ryzen 5 5500U @ 4.06GHz (6 Cores / 12 Threads), Motherboard: NB01 TUXEDO Aura 15 Gen2 NL5xNU (1.07.11RTR1 BIOS), Chipset: AMD Renoir/Cezanne, Memory: 2 x 8GB DDR4-3200MT/s Samsung M471A1K43DB1-CWE, Disk: Samsung SSD 970 EVO Plus 500GB, Graphics: AMD Lucienne 512MB (1800/1333MHz), Audio: AMD Renoir Radeon HD Audio, Network: Realtek RTL8111/8168/8211/8411 + Intel Wi-Fi 6 AX200
OS: Tuxedo 22.04, Kernel: 6.5.0-10027-tuxedo (x86_64), Desktop: KDE Plasma 5.27.10, Display Server: X Server 1.21.1.4, OpenGL: 4.6 Mesa 24.0.3-0tux2 (LLVM 15.0.7 DRM 3.54), Vulkan: 1.3.274, Compiler: GCC 11.4.0, File-System: ext4, Screen Resolution: 1920x1080
b:
Processor: AMD Ryzen 5 5500U @ 4.06GHz (6 Cores / 12 Threads), Motherboard: NB01 TUXEDO Aura 15 Gen2 NL5xNU (1.07.11RTR1 BIOS), Chipset: AMD Renoir/Cezanne, Memory: 2 x 8GB DDR4-3200MT/s Samsung M471A1K43DB1-CWE, Disk: Samsung SSD 970 EVO Plus 500GB, Graphics: AMD Lucienne 512MB (1800/1333MHz), Audio: AMD Renoir Radeon HD Audio, Network: Realtek RTL8111/8168/8211/8411 + Intel Wi-Fi 6 AX200
OS: Tuxedo 22.04, Kernel: 6.5.0-10027-tuxedo (x86_64), Desktop: KDE Plasma 5.27.10, Display Server: X Server 1.21.1.4, OpenGL: 4.6 Mesa 24.0.3-0tux2 (LLVM 15.0.7 DRM 3.54), Vulkan: 1.3.274, Compiler: GCC 11.4.0, File-System: ext4, Screen Resolution: 1920x1080
Blender 4.1
Blend File: Barbershop - Compute: CPU-Only
Seconds < Lower Is Better
a . 3327.01 |==================================================================
b . 3128.57 |==============================================================
TensorFlow 2.16.1
Device: CPU - Batch Size: 256 - Model: GoogLeNet
images/sec > Higher Is Better
a . 19.48 |====================================================================
b . 19.53 |====================================================================
TensorFlow 2.16.1
Device: CPU - Batch Size: 32 - Model: VGG-16
images/sec > Higher Is Better
a . 3.27 |=====================================================================
b . 3.05 |================================================================
TensorFlow 2.16.1
Device: CPU - Batch Size: 64 - Model: ResNet-50
images/sec > Higher Is Better
a . 6.41 |=====================================================================
b . 6.32 |====================================================================
Blender 4.1
Blend File: Pabellon Barcelona - Compute: CPU-Only
Seconds < Lower Is Better
a . 1067.46 |==================================================================
b . 994.19 |=============================================================
TensorFlow 2.16.1
Device: CPU - Batch Size: 512 - Model: AlexNet
images/sec > Higher Is Better
a . 57.39 |====================================================================
b . 57.50 |====================================================================
Blender 4.1
Blend File: Classroom - Compute: CPU-Only
Seconds < Lower Is Better
a . 841.77 |===================================================================
b . 827.48 |==================================================================
TensorFlow 2.16.1
Device: CPU - Batch Size: 16 - Model: VGG-16
images/sec > Higher Is Better
a . 3.20 |=====================================================================
b . 3.09 |===================================================================
TensorFlow 2.16.1
Device: CPU - Batch Size: 32 - Model: ResNet-50
images/sec > Higher Is Better
a . 6.41 |=====================================================================
b . 6.42 |=====================================================================
PyTorch 2.2.1
Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_l
batches/sec > Higher Is Better
a . 3.33 |================================================================
b . 3.58 |=====================================================================
PyTorch 2.2.1
Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_l
batches/sec > Higher Is Better
a . 3.40 |=================================================================
b . 3.60 |=====================================================================
PyTorch 2.2.1
Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_l
batches/sec > Higher Is Better
a . 3.42 |=================================================================
b . 3.62 |=====================================================================
PyTorch 2.2.1
Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_l
batches/sec > Higher Is Better
a . 3.43 |==================================================================
b . 3.60 |=====================================================================
PyTorch 2.2.1
Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l
batches/sec > Higher Is Better
a . 3.43 |=================================================================
b . 3.65 |=====================================================================
TensorFlow 2.16.1
Device: CPU - Batch Size: 256 - Model: AlexNet
images/sec > Higher Is Better
a . 56.50 |====================================================================
b . 56.55 |====================================================================
Blender 4.1
Blend File: Fishy Cat - Compute: CPU-Only
Seconds < Lower Is Better
a . 385.47 |===================================================================
b . 372.08 |=================================================================
TensorFlow 2.16.1
Device: CPU - Batch Size: 64 - Model: GoogLeNet
images/sec > Higher Is Better
a . 19.73 |====================================================================
b . 19.80 |====================================================================
PyTorch 2.2.1
Device: CPU - Batch Size: 256 - Model: ResNet-152
batches/sec > Higher Is Better
a . 5.25 |==================================================================
b . 5.50 |=====================================================================
PyTorch 2.2.1
Device: CPU - Batch Size: 512 - Model: ResNet-152
batches/sec > Higher Is Better
a . 5.23 |================================================================
b . 5.60 |=====================================================================
PyTorch 2.2.1
Device: CPU - Batch Size: 32 - Model: ResNet-152
batches/sec > Higher Is Better
a . 5.55 |=====================================================================
b . 5.49 |====================================================================
PyTorch 2.2.1
Device: CPU - Batch Size: 64 - Model: ResNet-152
batches/sec > Higher Is Better
a . 5.46 |====================================================================
b . 5.51 |=====================================================================
PyTorch 2.2.1
Device: CPU - Batch Size: 16 - Model: ResNet-152
batches/sec > Higher Is Better
a . 5.67 |=====================================================================
b . 5.50 |===================================================================
Blender 4.1
Blend File: BMW27 - Compute: CPU-Only
Seconds < Lower Is Better
a . 321.21 |===================================================================
b . 319.31 |===================================================================
TensorFlow 2.16.1
Device: CPU - Batch Size: 16 - Model: ResNet-50
images/sec > Higher Is Better
a . 6.41 |====================================================================
b . 6.50 |=====================================================================
PyTorch 2.2.1
Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l
batches/sec > Higher Is Better
a . 5.16 |================================================================
b . 5.55 |=====================================================================
TensorFlow 2.16.1
Device: CPU - Batch Size: 32 - Model: GoogLeNet
images/sec > Higher Is Better
a . 20.12 |===================================================================
b . 20.52 |====================================================================
PyTorch 2.2.1
Device: CPU - Batch Size: 512 - Model: ResNet-50
batches/sec > Higher Is Better
a . 12.68 |====================================================================
b . 12.21 |=================================================================
PyTorch 2.2.1
Device: CPU - Batch Size: 64 - Model: ResNet-50
batches/sec > Higher Is Better
a . 12.91 |====================================================================
b . 12.75 |===================================================================
PyTorch 2.2.1
Device: CPU - Batch Size: 256 - Model: ResNet-50
batches/sec > Higher Is Better
a . 12.84 |====================================================================
b . 12.90 |====================================================================
PyTorch 2.2.1
Device: CPU - Batch Size: 32 - Model: ResNet-50
batches/sec > Higher Is Better
a . 12.84 |===================================================================
b . 13.01 |====================================================================
TensorFlow 2.16.1
Device: CPU - Batch Size: 64 - Model: AlexNet
images/sec > Higher Is Better
a . 51.53 |====================================================================
b . 51.25 |====================================================================
PyTorch 2.2.1
Device: CPU - Batch Size: 16 - Model: ResNet-50
batches/sec > Higher Is Better
a . 13.03 |===================================================================
b . 13.19 |====================================================================
PyTorch 2.2.1
Device: CPU - Batch Size: 1 - Model: ResNet-152
batches/sec > Higher Is Better
a . 9.35 |=====================================================================
b . 9.15 |====================================================================
TensorFlow 2.16.1
Device: CPU - Batch Size: 16 - Model: GoogLeNet
images/sec > Higher Is Better
a . 19.85 |===================================================================
b . 20.19 |====================================================================
TensorFlow 2.16.1
Device: CPU - Batch Size: 1 - Model: VGG-16
images/sec > Higher Is Better
a . 1.39 |=====================================================================
b . 1.38 |=====================================================================
TensorFlow 2.16.1
Device: CPU - Batch Size: 32 - Model: AlexNet
images/sec > Higher Is Better
a . 45.55 |====================================================================
b . 44.96 |===================================================================
Timed Mesa Compilation 24.0
Time To Compile
Seconds < Lower Is Better
a . 58.73 |================================================================
b . 62.05 |====================================================================
PyTorch 2.2.1
Device: CPU - Batch Size: 1 - Model: ResNet-50
batches/sec > Higher Is Better
a . 21.00 |===================================================================
b . 21.31 |====================================================================
TensorFlow 2.16.1
Device: CPU - Batch Size: 16 - Model: AlexNet
images/sec > Higher Is Better
a . 36.39 |====================================================================
b . 36.02 |===================================================================
TensorFlow 2.16.1
Device: CPU - Batch Size: 1 - Model: ResNet-50
images/sec > Higher Is Better
a . 4.56 |=====================================================================
b . 4.54 |=====================================================================
TensorFlow 2.16.1
Device: CPU - Batch Size: 1 - Model: AlexNet
images/sec > Higher Is Better
a . 5.15 |=====================================================================
b . 5.10 |====================================================================
TensorFlow 2.16.1
Device: CPU - Batch Size: 1 - Model: GoogLeNet
images/sec > Higher Is Better
a . 10.84 |====================================================================
b . 10.83 |====================================================================