9684x-march

Tests for a future article. 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 2403270-NE-9684XMARC10
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CPU Massive 3 Tests
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PRE
March 27
  2 Hours, 34 Minutes
a
March 27
  8 Hours, 3 Minutes
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9684x-march Tests for a future article. 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" 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) Motherboard,,AMD Titanite_4G (RTI1007B BIOS),AMD Titanite_4G (RTI1007B BIOS) Chipset,,AMD Device 14a4,AMD Device 14a4 Memory,,1520GB,1520GB Disk,,3201GB Micron_7450_MTFDKCB3T2TFS + 257GB Flash Drive,3201GB Micron_7450_MTFDKCB3T2TFS + 257GB Flash Drive Graphics,,ASPEED,ASPEED Network,,Broadcom NetXtreme BCM5720 PCIe,Broadcom NetXtreme BCM5720 PCIe OS,,Ubuntu 23.10,Ubuntu 23.10 Kernel,,6.5.0-25-generic (x86_64),6.5.0-25-generic (x86_64) Compiler,,GCC 13.2.0,GCC 13.2.0 File-System,,ext4,ext4 Screen Resolution,,640x480,640x480 ,,"PRE","a" "BRL-CAD - VGR Performance Metric (VGR Performance Metric)",HIB,5956612,5927564 "TensorFlow - Device: CPU - Batch Size: 1 - Model: AlexNet (images/sec)",HIB,21.16,20.78 "TensorFlow - Device: CPU - Batch Size: 16 - Model: AlexNet (images/sec)",HIB,242.29,247.55 "TensorFlow - Device: CPU - Batch Size: 32 - Model: AlexNet (images/sec)",HIB,424.06,436.25 "TensorFlow - Device: CPU - Batch Size: 64 - Model: AlexNet (images/sec)",HIB,765.55,749.46 "TensorFlow - Device: CPU - Batch Size: 1 - Model: GoogLeNet (images/sec)",HIB,12.58,13.20 "TensorFlow - Device: CPU - Batch Size: 1 - Model: ResNet-50 (images/sec)",HIB,4.05,3.9 "TensorFlow - Device: CPU - Batch Size: 256 - Model: AlexNet (images/sec)",HIB,1652.23,1604.52 "TensorFlow - Device: CPU - Batch Size: 512 - Model: AlexNet (images/sec)",HIB,1980.51,2010.56 "TensorFlow - Device: CPU - Batch Size: 16 - Model: GoogLeNet (images/sec)",HIB,112.64,114.26 "TensorFlow - Device: CPU - Batch Size: 16 - Model: ResNet-50 (images/sec)",HIB,39.68,41.26 "TensorFlow - Device: CPU - Batch Size: 32 - Model: GoogLeNet (images/sec)",HIB,185.16,176.36 "TensorFlow - Device: CPU - Batch Size: 32 - Model: ResNet-50 (images/sec)",HIB,65.88,60.25 "TensorFlow - Device: CPU - Batch Size: 64 - Model: GoogLeNet (images/sec)",HIB,275.34,273.68 "TensorFlow - Device: CPU - Batch Size: 64 - Model: ResNet-50 (images/sec)",HIB,87.72,88.93 "TensorFlow - Device: CPU - Batch Size: 256 - Model: GoogLeNet (images/sec)",HIB,400.03,399.46 "TensorFlow - Device: CPU - Batch Size: 256 - Model: ResNet-50 (images/sec)",HIB,119.83,118.88 "TensorFlow - Device: CPU - Batch Size: 512 - Model: GoogLeNet (images/sec)",HIB,493.31,484.02 "TensorFlow - Device: CPU - Batch Size: 512 - Model: ResNet-50 (images/sec)",HIB,140.59,140.49 "PyTorch - Device: CPU - Batch Size: 1 - Model: ResNet-50 (batches/sec)",HIB,23.06,23.20 "PyTorch - Device: CPU - Batch Size: 1 - Model: ResNet-152 (batches/sec)",HIB,9.97,10.58 "PyTorch - Device: CPU - Batch Size: 16 - Model: ResNet-50 (batches/sec)",HIB,20.93,21.53 "PyTorch - Device: CPU - Batch Size: 32 - Model: ResNet-50 (batches/sec)",HIB,20.19,20.84 "PyTorch - Device: CPU - Batch Size: 64 - Model: ResNet-50 (batches/sec)",HIB,21.59,21.08 "PyTorch - Device: CPU - Batch Size: 16 - Model: ResNet-152 (batches/sec)",HIB,8.93,9.01 "PyTorch - Device: CPU - Batch Size: 256 - Model: ResNet-50 (batches/sec)",HIB,21.20,20.77 "PyTorch - Device: CPU - Batch Size: 32 - Model: ResNet-152 (batches/sec)",HIB,8.72,9.34 "PyTorch - Device: CPU - Batch Size: 512 - Model: ResNet-50 (batches/sec)",HIB,20.43,21.01 "PyTorch - Device: CPU - Batch Size: 64 - Model: ResNet-152 (batches/sec)",HIB,9.21,8.91 "PyTorch - Device: CPU - Batch Size: 256 - Model: ResNet-152 (batches/sec)",HIB,8.92,9.09 "PyTorch - Device: CPU - Batch Size: 512 - Model: ResNet-152 (batches/sec)",HIB,9.47,9.33 "PyTorch - Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l (batches/sec)",HIB,6.29,6.45 "PyTorch - Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l (batches/sec)",HIB,2.33,2.33 "PyTorch - Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_l (batches/sec)",HIB,2.33,2.31 "PyTorch - Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_l (batches/sec)",HIB,2.32,2.31 "PyTorch - Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_l (batches/sec)",HIB,2.29,2.33 "PyTorch - Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_l (batches/sec)",HIB,2.31,2.33 "Blender - Blend File: BMW27 - Compute: CPU-Only (sec)",LIB,7.55,7.55 "Blender - Blend File: Junkshop - Compute: CPU-Only (sec)",LIB,11.4,11.44 "Blender - Blend File: Classroom - Compute: CPU-Only (sec)",LIB,18.03,18.08 "Blender - Blend File: Fishy Cat - Compute: CPU-Only (sec)",LIB,9.96,9.85 "Blender - Blend File: Barbershop - Compute: CPU-Only (sec)",LIB,67.38,67.66 "Blender - Blend File: Pabellon Barcelona - Compute: CPU-Only (sec)",LIB,22.99,23.1 "Timed Mesa Compilation - Time To Compile (sec)",LIB,14.66,14.756 "RocksDB - Test: Overwrite (Op/s)",HIB,421049,421616 "RocksDB - Test: Random Read (Op/s)",HIB,1105306233,1108892776 "RocksDB - Test: Update Random (Op/s)",HIB,421266,425687 "RocksDB - Test: Read While Writing (Op/s)",HIB,27130363,26406662 "RocksDB - Test: Read Random Write Random (Op/s)",HIB,3619142,3643263