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 , "BRL-CAD 7.38.2 - VGR Performance Metric", Higher Results Are Better "a", "PRE", "PyTorch 2.2.1 - Device: CPU - Batch Size: 64 - Model: ResNet-152", Higher Results Are Better "PRE",9.2054236364207 "a",8.8158007348404,9.1229361032761,9.3848501979327,8.5961372993163,8.6274028186808,9.502900117317,8.548777274471,9.0835161303167,8.6150619610506,9.0522774957678,8.7745805522167,8.740977244649 "PyTorch 2.2.1 - Device: CPU - Batch Size: 256 - Model: ResNet-152", Higher Results Are Better "PRE",8.9173247997936 "a",8.581020691218,9.279248535183,8.6564652394964,8.8566080789118,9.6487434072098,9.0967242927174,9.4800079712012,9.076768674675,9.5665820710665,8.7336428375787,9.0102379005198,9.0540224020671 "PyTorch 2.2.1 - Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_l", Higher Results Are Better "PRE",2.2871367246133 "a",2.3113583251744,2.3404898496753,2.3322875796039 "PyTorch 2.2.1 - Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_l", Higher Results Are Better "PRE",2.3237855498783 "a",2.3214644405728,2.2976159228404,2.3223147491402 "PyTorch 2.2.1 - Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_l", Higher Results Are Better "PRE",2.3296494516927 "a",2.2985320118488,2.3188534325509,2.3238948070464 "PyTorch 2.2.1 - Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l", Higher Results Are Better "PRE",2.3281633313072 "a",2.3437094042077,2.3503592655722,2.3102222206814 "PyTorch 2.2.1 - Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_l", Higher Results Are Better "PRE",2.310855577463 "a",2.3450861094917,2.3003913874873,2.3376763909131 "PyTorch 2.2.1 - Device: CPU - Batch Size: 1 - Model: ResNet-152", Higher Results Are Better "PRE",9.9658602548322 "a",10.546770553305,10.684950354813,11.105298998846,9.9859668793363,10.944996935038,11.104312224184,10.56256898332,10.703967555217,10.177041905566,9.7716874937445,10.830720992267,10.435192309723,10.495673607821,10.60442302251,10.695300880109 "PyTorch 2.2.1 - Device: CPU - Batch Size: 32 - Model: ResNet-50", Higher Results Are Better "PRE",20.191276470064 "a",20.392266512432,20.581505816338,19.160659132962,20.839363178642,20.827096426824,21.51920145168,20.323975220184,21.115137099711,20.440739647811,20.912222479992,21.556445803163,20.933280872292,21.549491191775,21.43444876574,21.015563614036 "PyTorch 2.2.1 - Device: CPU - Batch Size: 512 - Model: ResNet-50", Higher Results Are Better "PRE",20.429606908806 "a",21.123891140209,20.391155342065,21.969851365977,20.753271375397,20.775917138619,19.867405052598,21.18365127559,20.700567367558,21.106392186715,21.109527299325,21.518009971029,21.673105642613,21.226014340545,21.311613655029,20.408827949026 "PyTorch 2.2.1 - Device: CPU - Batch Size: 16 - Model: ResNet-152", Higher Results Are Better "PRE",8.9336238000037 "a",9.0841869203749,8.8242664155582,9.1169478629097 "PyTorch 2.2.1 - Device: CPU - Batch Size: 32 - Model: ResNet-152", Higher Results Are Better "PRE",8.715250019003 "a",9.4869859545817,9.1984085482283,9.3241761141446 "PyTorch 2.2.1 - Device: CPU - Batch Size: 512 - Model: ResNet-152", Higher Results Are Better "PRE",9.4741326995271 "a",9.5154191967626,9.1856535212848,9.280517742698 "TensorFlow 2.16.1 - Device: CPU - Batch Size: 512 - Model: ResNet-50", Higher Results Are Better "a", "PRE", "PyTorch 2.2.1 - Device: CPU - Batch Size: 1 - Model: ResNet-50", Higher Results Are Better "PRE",23.064308582499 "a",23.984003230953,22.385386961714,23.310900502645,22.658367737685,23.544951095884,24.060067656797,23.726918747093,22.010256630612,23.54857537453,24.010682262674,24.015754907248,22.832300026657,21.576232604633,23.053124368342,23.229685572473 "PyTorch 2.2.1 - Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l", Higher Results Are Better "PRE",6.2889607380305 "a",6.2833664845031,6.515530480481,6.5644180000247 "TensorFlow 2.16.1 - Device: CPU - Batch Size: 256 - Model: ResNet-50", Higher Results Are Better "a", "PRE", "PyTorch 2.2.1 - Device: CPU - Batch Size: 256 - Model: ResNet-50", Higher Results Are Better "PRE",21.204209274393 "a",20.688097501324,20.638495690005,20.972423122903 "PyTorch 2.2.1 - Device: CPU - Batch Size: 16 - Model: ResNet-50", Higher Results Are Better "PRE",20.927246190909 "a",21.667819766291,21.70802618056,21.220377003089 "PyTorch 2.2.1 - Device: CPU - Batch Size: 64 - Model: ResNet-50", Higher Results Are Better "PRE",21.59488848577 "a",21.515379582657,20.95558521318,20.754626919237 "TensorFlow 2.16.1 - Device: CPU - Batch Size: 512 - Model: GoogLeNet", Higher Results Are Better "a", "PRE", "TensorFlow 2.16.1 - Device: CPU - Batch Size: 1 - Model: GoogLeNet", Higher Results Are Better "PRE", "a",13.61,12.77,13.83,12.83,13.46,12.61,13.2,13.99,12.62,13.36,12.45,13.32,12.52,13.84,13.56 "TensorFlow 2.16.1 - Device: CPU - Batch Size: 64 - Model: AlexNet", Higher Results Are Better "PRE", "a",780.6,721.68,756.09,765.17,726.98,723.63,740.46,736.84,760.11,766.45,758.35,755.15,775.99,713.42,760.94 "TensorFlow 2.16.1 - Device: CPU - Batch Size: 64 - Model: ResNet-50", Higher Results Are Better "a", "PRE", "TensorFlow 2.16.1 - Device: CPU - Batch Size: 32 - Model: AlexNet", Higher Results Are Better "PRE", "a",404.49,451.77,463,455.41,462.44,402.31,447.77,465.37,415.28,425.97,460.97,460.42,420.57,403.36,404.66 "TensorFlow 2.16.1 - Device: CPU - Batch Size: 256 - Model: GoogLeNet", Higher Results Are Better "a", "PRE", "TensorFlow 2.16.1 - Device: CPU - Batch Size: 16 - Model: AlexNet", Higher Results Are Better "PRE", "a",238.66,254.98,256.38,229.93,240.97,249.29,251,245.11,258.22,238.88,249.33,254.68,256.19,235.18,254.52 "Blender 4.1 - Blend File: Barbershop - Compute: CPU-Only", Lower Results Are Better "a", "PRE", "TensorFlow 2.16.1 - Device: CPU - Batch Size: 32 - Model: ResNet-50", Higher Results Are Better "a", "PRE", "RocksDB 9.0 - Test: Update Random", Higher Results Are Better "a", "PRE", "RocksDB 9.0 - Test: Overwrite", Higher Results Are Better "a", "PRE", "RocksDB 9.0 - Test: Read Random Write Random", Higher Results Are Better "a", "PRE", "RocksDB 9.0 - Test: Read While Writing", Higher Results Are Better "a", "PRE", "RocksDB 9.0 - Test: Random Read", Higher Results Are Better "a", "PRE", "TensorFlow 2.16.1 - Device: CPU - Batch Size: 1 - Model: AlexNet", Higher Results Are Better "PRE", "a",21.6,20.44,19.53,20.83,21.12,21.56,20.79,19.95,20.25,21.45,21.18,20.46,21.34,20.29,20.9 "TensorFlow 2.16.1 - Device: CPU - Batch Size: 16 - Model: ResNet-50", Higher Results Are Better "PRE", "a", "TensorFlow 2.16.1 - Device: CPU - Batch Size: 1 - Model: ResNet-50", Higher Results Are Better "PRE", "a", "TensorFlow 2.16.1 - Device: CPU - Batch Size: 512 - Model: AlexNet", Higher Results Are Better "PRE", "a", "TensorFlow 2.16.1 - Device: CPU - Batch Size: 64 - Model: GoogLeNet", Higher Results Are Better "a", "PRE", "Timed Mesa Compilation 24.0 - Time To Compile", Lower Results Are Better "PRE", "a",14.758,14.828,14.681 "Blender 4.1 - Blend File: Pabellon Barcelona - Compute: CPU-Only", Lower Results Are Better "a", "PRE", "TensorFlow 2.16.1 - Device: CPU - Batch Size: 32 - Model: GoogLeNet", Higher Results Are Better "PRE", "a", "TensorFlow 2.16.1 - Device: CPU - Batch Size: 256 - Model: AlexNet", Higher Results Are Better "PRE", "a", "TensorFlow 2.16.1 - Device: CPU - Batch Size: 16 - Model: GoogLeNet", Higher Results Are Better "PRE", "a", "Blender 4.1 - Blend File: Classroom - Compute: CPU-Only", Lower Results Are Better "a", "PRE", "Blender 4.1 - Blend File: Junkshop - Compute: CPU-Only", Lower Results Are Better "a", "PRE", "Blender 4.1 - Blend File: Fishy Cat - Compute: CPU-Only", Lower Results Are Better "a", "PRE", "Blender 4.1 - Blend File: BMW27 - Compute: CPU-Only", Lower Results Are Better "a", "PRE",