9684x-march

2 x AMD EPYC 9684X 96-Core testing with a AMD Titanite_4G (RTI1007B BIOS) and ASPEED on Ubuntu 23.10 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 2403274-NE-9684XMARC65
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 3 Tests
Creator Workloads 2 Tests
HPC - High Performance Computing 2 Tests
Machine Learning 2 Tests
Multi-Core 2 Tests
Python Tests 3 Tests
Common Workstation Benchmarks 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
PRE
March 27
  2 Hours, 34 Minutes
a
March 27
  8 Hours, 3 Minutes
b
March 27
  2 Hours, 46 Minutes
Invert Hiding All Results Option
  4 Hours, 28 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):

9684x-march 2 x AMD EPYC 9684X 96-Core testing with a AMD Titanite_4G (RTI1007B BIOS) and ASPEED on Ubuntu 23.10 via the Phoronix Test Suite. ,,"PRE","a","b" Processor,,2 x AMD EPYC 9684X 96-Core @ 2.55GHz (192 Cores / 384 Threads),2 x AMD EPYC 9684X 96-Core @ 2.55GHz (192 Cores / 384 Threads),2 x AMD EPYC 9684X 96-Core @ 2.55GHz (192 Cores / 384 Threads) Motherboard,,AMD Titanite_4G (RTI1007B BIOS),AMD Titanite_4G (RTI1007B BIOS),AMD Titanite_4G (RTI1007B BIOS) Chipset,,AMD Device 14a4,AMD Device 14a4,AMD Device 14a4 Memory,,1520GB,1520GB,1520GB Disk,,3201GB Micron_7450_MTFDKCB3T2TFS + 257GB Flash Drive,3201GB Micron_7450_MTFDKCB3T2TFS + 257GB Flash Drive,3201GB Micron_7450_MTFDKCB3T2TFS + 257GB Flash Drive Graphics,,ASPEED,ASPEED,ASPEED Network,,Broadcom NetXtreme BCM5720 PCIe,Broadcom NetXtreme BCM5720 PCIe,Broadcom NetXtreme BCM5720 PCIe OS,,Ubuntu 23.10,Ubuntu 23.10,Ubuntu 23.10 Kernel,,6.5.0-25-generic (x86_64),6.5.0-25-generic (x86_64),6.5.0-25-generic (x86_64) Compiler,,GCC 13.2.0,GCC 13.2.0,GCC 13.2.0 File-System,,ext4,ext4,ext4 Screen Resolution,,640x480,640x480,640x480 ,,"PRE","a","b" "BRL-CAD - VGR Performance Metric (VGR Performance Metric)",HIB,5956612,5927564,5794040 "TensorFlow - Device: CPU - Batch Size: 1 - Model: AlexNet (images/sec)",HIB,21.16,20.78,21.01 "TensorFlow - Device: CPU - Batch Size: 16 - Model: AlexNet (images/sec)",HIB,242.29,247.55,236.56 "TensorFlow - Device: CPU - Batch Size: 32 - Model: AlexNet (images/sec)",HIB,424.06,436.25,461.6 "TensorFlow - Device: CPU - Batch Size: 64 - Model: AlexNet (images/sec)",HIB,765.55,749.46,743.5 "TensorFlow - Device: CPU - Batch Size: 1 - Model: GoogLeNet (images/sec)",HIB,12.58,13.20,13.52 "TensorFlow - Device: CPU - Batch Size: 1 - Model: ResNet-50 (images/sec)",HIB,4.05,3.9,4.01 "TensorFlow - Device: CPU - Batch Size: 256 - Model: AlexNet (images/sec)",HIB,1652.23,1604.52,1656.79 "TensorFlow - Device: CPU - Batch Size: 512 - Model: AlexNet (images/sec)",HIB,1980.51,2010.56,2010.6 "TensorFlow - Device: CPU - Batch Size: 16 - Model: GoogLeNet (images/sec)",HIB,112.64,114.26,119.22 "TensorFlow - Device: CPU - Batch Size: 16 - Model: ResNet-50 (images/sec)",HIB,39.68,41.26,35.92 "TensorFlow - Device: CPU - Batch Size: 32 - Model: GoogLeNet (images/sec)",HIB,185.16,176.36,190.74 "TensorFlow - Device: CPU - Batch Size: 32 - Model: ResNet-50 (images/sec)",HIB,65.88,60.25,66.68 "TensorFlow - Device: CPU - Batch Size: 64 - Model: GoogLeNet (images/sec)",HIB,275.34,273.68,256.87 "TensorFlow - Device: CPU - Batch Size: 64 - Model: ResNet-50 (images/sec)",HIB,87.72,88.93,88.95 "TensorFlow - Device: CPU - Batch Size: 256 - Model: GoogLeNet (images/sec)",HIB,400.03,399.46,400.61 "TensorFlow - Device: CPU - Batch Size: 256 - Model: ResNet-50 (images/sec)",HIB,119.83,118.88,118.77 "TensorFlow - Device: CPU - Batch Size: 512 - Model: GoogLeNet (images/sec)",HIB,493.31,484.02,494.46 "TensorFlow - Device: CPU - Batch Size: 512 - Model: ResNet-50 (images/sec)",HIB,140.59,140.49,141.16 "TensorFlow - Device: CPU - Batch Size: 1 - Model: VGG-16 (images/sec)",HIB,,,9.39 "TensorFlow - Device: CPU - Batch Size: 16 - Model: VGG-16 (images/sec)",HIB,,,60.69 "TensorFlow - Device: CPU - Batch Size: 32 - Model: VGG-16 (images/sec)",HIB,,,76.04 "TensorFlow - Device: CPU - Batch Size: 64 - Model: VGG-16 (images/sec)",HIB,,,95.91 "TensorFlow - Device: CPU - Batch Size: 256 - Model: VGG-16 (images/sec)",HIB,,,127.18 "TensorFlow - Device: CPU - Batch Size: 512 - Model: VGG-16 (images/sec)",HIB,,,135.78 "PyTorch - Device: CPU - Batch Size: 1 - Model: ResNet-50 (batches/sec)",HIB,23.06,23.20,23.24 "PyTorch - Device: CPU - Batch Size: 1 - Model: ResNet-152 (batches/sec)",HIB,9.97,10.58,10.60 "PyTorch - Device: CPU - Batch Size: 16 - Model: ResNet-50 (batches/sec)",HIB,20.93,21.53,20.36 "PyTorch - Device: CPU - Batch Size: 32 - Model: ResNet-50 (batches/sec)",HIB,20.19,20.84,21.03 "PyTorch - Device: CPU - Batch Size: 64 - Model: ResNet-50 (batches/sec)",HIB,21.59,21.08,20.90 "PyTorch - Device: CPU - Batch Size: 16 - Model: ResNet-152 (batches/sec)",HIB,8.93,9.01,9.12 "PyTorch - Device: CPU - Batch Size: 256 - Model: ResNet-50 (batches/sec)",HIB,21.20,20.77,20.85 "PyTorch - Device: CPU - Batch Size: 32 - Model: ResNet-152 (batches/sec)",HIB,8.72,9.34,9.28 "PyTorch - Device: CPU - Batch Size: 512 - Model: ResNet-50 (batches/sec)",HIB,20.43,21.01,21.01 "PyTorch - Device: CPU - Batch Size: 64 - Model: ResNet-152 (batches/sec)",HIB,9.21,8.91,8.79 "PyTorch - Device: CPU - Batch Size: 256 - Model: ResNet-152 (batches/sec)",HIB,8.92,9.09,8.85 "PyTorch - Device: CPU - Batch Size: 512 - Model: ResNet-152 (batches/sec)",HIB,9.47,9.33,8.81 "PyTorch - Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l (batches/sec)",HIB,6.29,6.45,6.50 "PyTorch - Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l (batches/sec)",HIB,2.33,2.33,2.35 "PyTorch - Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_l (batches/sec)",HIB,2.33,2.31,2.32 "PyTorch - Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_l (batches/sec)",HIB,2.32,2.31,2.33 "PyTorch - Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_l (batches/sec)",HIB,2.29,2.33,2.31 "PyTorch - Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_l (batches/sec)",HIB,2.31,2.33,2.32 "Blender - Blend File: BMW27 - Compute: CPU-Only (sec)",LIB,7.55,7.55,7.48 "Blender - Blend File: Junkshop - Compute: CPU-Only (sec)",LIB,11.4,11.44,11.61 "Blender - Blend File: Classroom - Compute: CPU-Only (sec)",LIB,18.03,18.08,18.04 "Blender - Blend File: Fishy Cat - Compute: CPU-Only (sec)",LIB,9.96,9.85,9.94 "Blender - Blend File: Barbershop - Compute: CPU-Only (sec)",LIB,67.38,67.66,67.65 "Blender - Blend File: Pabellon Barcelona - Compute: CPU-Only (sec)",LIB,22.99,23.1,23.11 "Timed Mesa Compilation - Time To Compile (sec)",LIB,14.66,14.756,14.711 "RocksDB - Test: Overwrite (Op/s)",HIB,421049,421616,439602 "RocksDB - Test: Random Read (Op/s)",HIB,1105306233,1108892776,1108469308 "RocksDB - Test: Update Random (Op/s)",HIB,421266,425687,427391 "RocksDB - Test: Read While Writing (Op/s)",HIB,27130363,26406662,26135567 "RocksDB - Test: Read Random Write Random (Op/s)",HIB,3619142,3643263,3638929