1280p tf

Intel Core i7-1280P testing with a MSI MS-14C6 (E14C6IMS.115 BIOS) and MSI Intel ADL GT2 14GB on Ubuntu 22.10 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 2210085-NE-1280PTF6300
Jump To Table - Results

View

Do Not Show Noisy Results
Do Not Show Results With Incomplete Data
Do Not Show Results With Little Change/Spread
List Notable Results

Statistics

Show Overall Harmonic Mean(s)
Show Overall Geometric Mean
Show Wins / Losses Counts (Pie Chart)
Normalize Results
Remove Outliers Before Calculating Averages

Graph Settings

Force Line Graphs Where Applicable
Convert To Scalar Where Applicable
Prefer Vertical Bar Graphs

Multi-Way Comparison

Condense Multi-Option Tests Into Single Result Graphs

Table

Show Detailed System Result Table

Run Management

Highlight
Result
Hide
Result
Result
Identifier
Performance Per
Dollar
Date
Run
  Test
  Duration
A
October 07 2022
  2 Hours, 30 Minutes
B
October 07 2022
  2 Hours, 29 Minutes
C
October 07 2022
  2 Hours, 28 Minutes
D
October 07 2022
  2 Hours, 29 Minutes
Invert Hiding All Results Option
  2 Hours, 29 Minutes

Only show results where is faster than
Only show results matching title/arguments (delimit multiple options with a comma):
Do not show results matching title/arguments (delimit multiple options with a comma):

1280p tf Suite 1.0.0 System Test suite extracted from 1280p tf. pts/smhasher-1.1.0 --test=Speed wyhash Hash: wyhash pts/smhasher-1.1.0 --test=Speed sha3-256 Hash: SHA3-256 pts/smhasher-1.1.0 --test=Speed Spooky32 Hash: Spooky32 pts/smhasher-1.1.0 --test=Speed fasthash32 Hash: fasthash32 pts/smhasher-1.1.0 --test=Speed FarmHash128 Hash: FarmHash128 pts/smhasher-1.1.0 --test=Speed t1ha2_atonce Hash: t1ha2_atonce pts/smhasher-1.1.0 --test=Speed FarmHash32 Hash: FarmHash32 x86_64 AVX pts/smhasher-1.1.0 --test=Speed t1ha0_aes_avx2 Hash: t1ha0_aes_avx2 x86_64 pts/smhasher-1.1.0 --test=Speed MeowHash Hash: MeowHash x86_64 AES-NI pts/tensorflow-2.0.0 --device cpu --batch_size=16 --model=vgg16 Device: CPU - Batch Size: 16 - Model: VGG-16 pts/tensorflow-2.0.0 --device cpu --batch_size=32 --model=vgg16 Device: CPU - Batch Size: 32 - Model: VGG-16 pts/tensorflow-2.0.0 --device cpu --batch_size=64 --model=vgg16 Device: CPU - Batch Size: 64 - Model: VGG-16 pts/tensorflow-2.0.0 --device cpu --batch_size=16 --model=alexnet Device: CPU - Batch Size: 16 - Model: AlexNet pts/tensorflow-2.0.0 --device cpu --batch_size=256 --model=vgg16 Device: CPU - Batch Size: 256 - Model: VGG-16 pts/tensorflow-2.0.0 --device cpu --batch_size=32 --model=alexnet Device: CPU - Batch Size: 32 - Model: AlexNet pts/tensorflow-2.0.0 --device cpu --batch_size=512 --model=vgg16 Device: CPU - Batch Size: 512 - Model: VGG-16 pts/tensorflow-2.0.0 --device cpu --batch_size=64 --model=alexnet Device: CPU - Batch Size: 64 - Model: AlexNet pts/tensorflow-2.0.0 --device cpu --batch_size=256 --model=alexnet Device: CPU - Batch Size: 256 - Model: AlexNet pts/tensorflow-2.0.0 --device cpu --batch_size=512 --model=alexnet Device: CPU - Batch Size: 512 - Model: AlexNet pts/tensorflow-2.0.0 --device cpu --batch_size=16 --model=googlenet Device: CPU - Batch Size: 16 - Model: GoogLeNet pts/tensorflow-2.0.0 --device cpu --batch_size=16 --model=resnet50 Device: CPU - Batch Size: 16 - Model: ResNet-50 pts/tensorflow-2.0.0 --device cpu --batch_size=32 --model=googlenet Device: CPU - Batch Size: 32 - Model: GoogLeNet pts/tensorflow-2.0.0 --device cpu --batch_size=32 --model=resnet50 Device: CPU - Batch Size: 32 - Model: ResNet-50 pts/tensorflow-2.0.0 --device cpu --batch_size=64 --model=googlenet Device: CPU - Batch Size: 64 - Model: GoogLeNet pts/tensorflow-2.0.0 --device cpu --batch_size=64 --model=resnet50 Device: CPU - Batch Size: 64 - Model: ResNet-50 pts/tensorflow-2.0.0 --device cpu --batch_size=256 --model=googlenet Device: CPU - Batch Size: 256 - Model: GoogLeNet pts/tensorflow-2.0.0 --device cpu --batch_size=256 --model=resnet50 Device: CPU - Batch Size: 256 - Model: ResNet-50 pts/tensorflow-2.0.0 --device cpu --batch_size=512 --model=googlenet Device: CPU - Batch Size: 512 - Model: GoogLeNet pts/tensorflow-2.0.0 --device cpu --batch_size=512 --model=resnet50 Device: CPU - Batch Size: 512 - Model: ResNet-50