fg

Intel Core i9-10980XE testing with a ASRock X299 Steel Legend (P1.50 BIOS) and llvmpipe on Ubuntu 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 2401113-PTS-FG17231050
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

Limit displaying results to tests within:

CPU Massive 2 Tests
HPC - High Performance Computing 3 Tests
Machine Learning 3 Tests
Python Tests 2 Tests

Statistics

Show Overall Harmonic Mean(s)
Show Overall Geometric Mean
Show Geometric Means Per-Suite/Category
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
January 11
  36 Minutes
b
January 11
  36 Minutes
Invert Hiding All Results Option
  36 Minutes
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):

fg Suite 1.0.0 System Test suite extracted from fg. pts/y-cruncher-1.4.0 500m Pi Digits To Calculate: 500M pts/tensorflow-2.1.1 --device cpu --batch_size=16 --model=alexnet Device: CPU - Batch Size: 16 - Model: AlexNet pts/y-cruncher-1.4.0 1b Pi Digits To Calculate: 1B pts/tensorflow-2.1.1 --device cpu --batch_size=1 --model=alexnet Device: CPU - Batch Size: 1 - Model: AlexNet pts/tensorflow-2.1.1 --device cpu --batch_size=1 --model=vgg16 Device: CPU - Batch Size: 1 - Model: VGG-16 pts/quicksilver-1.0.0 ../Examples/CORAL2_Benchmark/Problem2/Coral2_P2.inp Input: CORAL2 P2 pts/llama-cpp-1.0.0 -m ../llama-2-7b.Q4_0.gguf Model: llama-2-7b.Q4_0.gguf pts/speedb-1.0.1 --benchmarks="readwhilewriting" Test: Read While Writing pts/cachebench-1.2.0 -b Test: Read / Modify / Write pts/llama-cpp-1.0.0 -m ../llama-2-13b.Q4_0.gguf Model: llama-2-13b.Q4_0.gguf pts/speedb-1.0.1 --benchmarks="fillseq" Test: Sequential Fill pts/quicksilver-1.0.0 ../Examples/CORAL2_Benchmark/Problem1/Coral2_P1.inp Input: CORAL2 P1 pts/tensorflow-2.1.1 --device cpu --batch_size=1 --model=googlenet Device: CPU - Batch Size: 1 - Model: GoogLeNet pts/tensorflow-2.1.1 --device cpu --batch_size=16 --model=vgg16 Device: CPU - Batch Size: 16 - Model: VGG-16 pts/tensorflow-2.1.1 --device cpu --batch_size=16 --model=googlenet Device: CPU - Batch Size: 16 - Model: GoogLeNet pts/tensorflow-2.1.1 --device cpu --batch_size=1 --model=resnet50 Device: CPU - Batch Size: 1 - Model: ResNet-50 pts/tensorflow-2.1.1 --device cpu --batch_size=16 --model=resnet50 Device: CPU - Batch Size: 16 - Model: ResNet-50 pts/speedb-1.0.1 --benchmarks="fillsync" Test: Random Fill Sync pts/speedb-1.0.1 --benchmarks="fillrandom" Test: Random Fill pts/speedb-1.0.1 --benchmarks="readrandom" Test: Random Read pts/quicksilver-1.0.0 ../Examples/CTS2_Benchmark/CTS2.inp Input: CTS2 pts/speedb-1.0.1 --benchmarks="readrandomwriterandom" Test: Read Random Write Random pts/speedb-1.0.1 --benchmarks="updaterandom" Test: Update Random pts/cachebench-1.2.0 -r Test: Read pts/cachebench-1.2.0 -w Test: Write pts/pytorch-1.0.1 cpu 16 efficientnet_v2_l Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l pts/pytorch-1.0.1 cpu 1 efficientnet_v2_l Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l pts/pytorch-1.0.1 cpu 16 resnet152 Device: CPU - Batch Size: 16 - Model: ResNet-152 pts/pytorch-1.0.1 cpu 16 resnet50 Device: CPU - Batch Size: 16 - Model: ResNet-50 pts/pytorch-1.0.1 cpu 1 resnet152 Device: CPU - Batch Size: 1 - Model: ResNet-152 pts/pytorch-1.0.1 cpu 1 resnet50 Device: CPU - Batch Size: 1 - Model: ResNet-50