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
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"
"Timed Mesa Compilation - Time To Compile (sec)",LIB,14.66,14.756,14.711
"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
"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
"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
"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
"BRL-CAD - VGR Performance Metric (VGR Performance Metric)",HIB,5956612,5927564,5794040
"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