AMD Ryzen Threadripper 7980X 64-Cores testing with a ASUS Pro WS TRX50-SAGE WIFI (0217 BIOS) and AMD Radeon RX 7900 XT 20GB 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 2401079-PTS-BIGBENCH30
big bench,
"TensorFlow 2.12 - Device: CPU - Batch Size: 1 - Model: VGG-16",
Higher Results Are Better
"a",9.72,9.75,9.75
"b",
"c",
"Quicksilver 20230818 - Input: CORAL2 P2",
Higher Results Are Better
"a",19820000,19740000,19770000
"b",19830000,19630000,19740000
"c",
"TensorFlow 2.12 - Device: CPU - Batch Size: 1 - Model: AlexNet",
Higher Results Are Better
"a",25.66,25.77,25.73
"b",
"c",
"TensorFlow 2.12 - Device: CPU - Batch Size: 16 - Model: VGG-16",
Higher Results Are Better
"a",44.4,44.42,44.4
"b",
"c",
"TensorFlow 2.12 - Device: CPU - Batch Size: 32 - Model: VGG-16",
Higher Results Are Better
"a",49.03,48.85,48.78
"b",
"c",
"TensorFlow 2.12 - Device: CPU - Batch Size: 64 - Model: VGG-16",
Higher Results Are Better
"a",53.06,52.87,52.92
"b",
"c",
"TensorFlow 2.12 - Device: CPU - Batch Size: 16 - Model: AlexNet",
Higher Results Are Better
"a",312.92,310.07,312.92
"b",
"c",
"TensorFlow 2.12 - Device: CPU - Batch Size: 256 - Model: VGG-16",
Higher Results Are Better
"a",57.12,57.12,57.19
"b",
"c",
"TensorFlow 2.12 - Device: CPU - Batch Size: 32 - Model: AlexNet",
Higher Results Are Better
"a",511.52,510.32,510.14
"b",
"c",
"TensorFlow 2.12 - Device: CPU - Batch Size: 512 - Model: VGG-16",
Higher Results Are Better
"a",57.99,58,57.93
"b",
"c",
"TensorFlow 2.12 - Device: CPU - Batch Size: 64 - Model: AlexNet",
Higher Results Are Better
"a",741.52,739.94,739.93
"b",
"c",
"TensorFlow 2.12 - Device: CPU - Batch Size: 1 - Model: GoogLeNet",
Higher Results Are Better
"a",22.5,22.39,20.14,20.14,22.54,22.28,22.44,22.45,22.62,22.43,22.61,22.57,22.26,19.92,20.02
"b",
"c",
"TensorFlow 2.12 - Device: CPU - Batch Size: 1 - Model: ResNet-50",
Higher Results Are Better
"a",7.21,7.11,7.26
"b",
"c",
"TensorFlow 2.12 - Device: CPU - Batch Size: 256 - Model: AlexNet",
Higher Results Are Better
"a",1074.63,1070.16,1067.39
"b",
"c",
"TensorFlow 2.12 - Device: CPU - Batch Size: 512 - Model: AlexNet",
Higher Results Are Better
"a",1146.91,1146.15,1143.93
"b",
"c",
"Quicksilver 20230818 - Input: CORAL2 P1",
Higher Results Are Better
"a",26210000,25960000,25910000
"b",25890000,25980000,26160000
"c",
"TensorFlow 2.12 - Device: CPU - Batch Size: 16 - Model: GoogLeNet",
Higher Results Are Better
"a",188.31,186.81,188.63
"b",
"c",
"TensorFlow 2.12 - Device: CPU - Batch Size: 16 - Model: ResNet-50",
Higher Results Are Better
"a",54.57,54.74,54.79
"b",
"c",
"TensorFlow 2.12 - Device: CPU - Batch Size: 32 - Model: GoogLeNet",
Higher Results Are Better
"a",225.12,228.34,227.09
"b",
"c",
"TensorFlow 2.12 - Device: CPU - Batch Size: 32 - Model: ResNet-50",
Higher Results Are Better
"a",69.38,69.49,69.41
"b",
"c",
"TensorFlow 2.12 - Device: CPU - Batch Size: 64 - Model: GoogLeNet",
Higher Results Are Better
"a",273.47,270.37,272.63
"b",
"c",
"TensorFlow 2.12 - Device: CPU - Batch Size: 64 - Model: ResNet-50",
Higher Results Are Better
"a",80.1,80,79.9
"b",
"c",
"TensorFlow 2.12 - Device: CPU - Batch Size: 256 - Model: GoogLeNet",
Higher Results Are Better
"a",311.11,312.01,311.69
"b",
"c",
"TensorFlow 2.12 - Device: CPU - Batch Size: 256 - Model: ResNet-50",
Higher Results Are Better
"a",91.16,91.14,91.06
"b",
"c",
"TensorFlow 2.12 - Device: CPU - Batch Size: 512 - Model: GoogLeNet",
Higher Results Are Better
"a",315.45,315.22,315.81
"b",
"c",
"TensorFlow 2.12 - Device: CPU - Batch Size: 512 - Model: ResNet-50",
Higher Results Are Better
"a",95.07,95.04,95.04
"b",
"c",
"PyTorch 2.1 - Device: CPU - Batch Size: 1 - Model: ResNet-50",
Higher Results Are Better
"a",58.125939712265,59.063252550158,59.935894587582
"b",58.543732976691,59.926277644099,54.278965084086,59.948851348987,59.338770509835,59.329088436952,59.365349992629,59.82112861296,59.84165661359,59.782257656663,59.804707319656,59.546895295288,60.139956578274,59.414447910241
"c",59.691345113659
"PyTorch 2.1 - Device: CPU - Batch Size: 1 - Model: ResNet-152",
Higher Results Are Better
"a",22.099980402968,21.853177560923,21.867926863306
"b",21.923565640436,22.035794244738,21.758492392393
"c",21.802294076027
"PyTorch 2.1 - Device: CPU - Batch Size: 16 - Model: ResNet-50",
Higher Results Are Better
"a",47.875218154678,47.192901987186,47.352396970393
"b",47.260254298496,47.245714809304,47.716270613855
"c",47.598276819909
"PyTorch 2.1 - Device: CPU - Batch Size: 32 - Model: ResNet-50",
Higher Results Are Better
"a",47.438238410967,47.691843996689,47.274778975934
"b",42.235195093821,47.994747821174,47.340535130492,48.251080410549,46.882640949373,47.271932223977,47.69627135915,47.637324989832,47.886663666029,48.359619825512,47.312421728028,47.757679562913,47.463033394215
"c",48.122937030387
"PyTorch 2.1 - Device: CPU - Batch Size: 64 - Model: ResNet-50",
Higher Results Are Better
"a",47.924127458819,47.424348605367,47.352757959527
"b",46.788846557575,47.839636457457,47.017426683642
"c",47.807305877538
"PyTorch 2.1 - Device: CPU - Batch Size: 16 - Model: ResNet-152",
Higher Results Are Better
"a",18.573796768452,18.73824003675,18.722321370183
"b",19.003049749426,18.48558959429,18.473380860219
"c",18.712319736939
"PyTorch 2.1 - Device: CPU - Batch Size: 256 - Model: ResNet-50",
Higher Results Are Better
"a",47.969687582898,45.274055268908,47.859770622367,47.704130547,48.271736894111,47.526750230307
"b",47.959582856093,47.832083733291,45.942312096866
"c",43.070901932406
"PyTorch 2.1 - Device: CPU - Batch Size: 32 - Model: ResNet-152",
Higher Results Are Better
"a",18.55788329311,19.028450541413,18.679905158291
"b",18.673712893419
"c",19.022725251588
"PyTorch 2.1 - Device: CPU - Batch Size: 512 - Model: ResNet-50",
Higher Results Are Better
"a",47.887034823724,47.70229674819,47.043323475452
"b",45.969077212612
"c",46.60141265228
"PyTorch 2.1 - Device: CPU - Batch Size: 64 - Model: ResNet-152",
Higher Results Are Better
"a",18.684894468932,18.744706318402,18.643328992936
"b",18.616040204692
"c",19.379897854294
"PyTorch 2.1 - Device: CPU - Batch Size: 256 - Model: ResNet-152",
Higher Results Are Better
"a",18.985256166748,18.856365211451,19.135160120796
"b",18.801641691115
"c",19.099774562809
"PyTorch 2.1 - Device: CPU - Batch Size: 512 - Model: ResNet-152",
Higher Results Are Better
"a",18.855558386324,18.991019682701,19.018668103944
"b",18.492127580076
"c",18.820093549467
"PyTorch 2.1 - Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l",
Higher Results Are Better
"a",12.194633858053,12.231890015559,12.141031041577
"b",12.286794412467
"c",12.269509057514
"PyTorch 2.1 - Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l",
Higher Results Are Better
"a",7.5277695311254,7.5600573075375,7.4762043250782
"b",7.4608879636877
"c",7.5037719540442
"PyTorch 2.1 - Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_l",
Higher Results Are Better
"a",7.5006979694138,7.4652632068005,7.5136669349895
"b",7.4465419781602
"c",7.5190243556411
"PyTorch 2.1 - Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_l",
Higher Results Are Better
"a",7.4680573558628,7.4855199427343,7.4320489546586
"b",7.4503785801508
"c",7.5207077428849
"PyTorch 2.1 - Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_l",
Higher Results Are Better
"a",7.4591650552962,7.4873262708283,7.4563417103442
"b",7.3992884322901
"c",7.5384773815618
"Quicksilver 20230818 - Input: CTS2",
Higher Results Are Better
"a",20100000,19960000,20010000
"b",20070000,19940000,19910000
"c",
"PyTorch 2.1 - Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_l",
Higher Results Are Better
"a",7.477642152833,7.4924697075965,7.4515824250421
"b",7.5160679153364
"c",7.4788367997619