AMD Ryzen 9 5900HX testing with a ASUS ROG Strix G513QY_G513QY G513QY v1.0 (G513QY.318 BIOS) and ASUS AMD Cezanne 512MB on Ubuntu 22.10 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 2401107-PTS-FGHJ244998
fghj,
"PyTorch 2.1 - Device: CPU - Batch Size: 1 - Model: ResNet-50",
Higher Results Are Better
"a",34.038904189134
"b",31.411797107013,32.423512744717,33.327564330625,34.557278778658,35.534705871118,34.145531955775,34.319385057889,33.914284226554,34.328772573625,35.012790130432,37.105565373861,37.162636827845
"c",35.458468432699,36.18516589048,34.071710416745,32.506773243547,32.246189771165,34.968838709413,33.369296875071,33.431754979614,32.576572220994,33.620812645281,35.420418718554,34.943261757467,35.183426330556,35.414906383181,32.479083250731
"PyTorch 2.1 - Device: CPU - Batch Size: 1 - Model: ResNet-152",
Higher Results Are Better
"a",15.153978778588
"b",15.307634723318,15.047894456984,15.460679760039
"c",15.120672666866,15.143607087324,15.367364168351
"PyTorch 2.1 - Device: CPU - Batch Size: 16 - Model: ResNet-50",
Higher Results Are Better
"a",20.220273482296
"b",18.949872077287,19.786839843149,20.536952002814,19.520178143039,19.462228654645,17.250528755834,19.942138951942,19.05123688356,17.328241857338,19.358451234918,18.829148806029,19.545769445579,18.594314975391,18.745193973136,17.413887780909
"c",19.722617295684,20.150905789961,19.413553811822
"PyTorch 2.1 - Device: CPU - Batch Size: 16 - Model: ResNet-152",
Higher Results Are Better
"a",9.0906025697537
"b",9.3013759770743,9.4705628962991,8.5494436461911,8.9223803536367,9.1733347513166,9.192192314596,8.9389778179812,9.2143980688686,9.1904102073154,9.0788411646003,8.7184899689197,9.0820361018391
"c",8.8817213667592,9.2028433398583,9.5227006098778,9.1088969246299,8.9880619811776,8.8137926660804,9.4285036210317,9.2182219405603,8.9630644258333,9.4115054399303,8.6568483126194,9.4425471127511
"PyTorch 2.1 - Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l",
Higher Results Are Better
"a",9.5822977206863
"b",9.5237220263524,9.3996779549413,9.4528289478002
"c",9.5075945657941,9.4821569042839,9.4476493397813
"PyTorch 2.1 - Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l",
Higher Results Are Better
"a",6.2163341475417
"b",6.3456892146004,6.2408927394333,6.2520498951576
"c",6.2159910182578,6.2377466707408,6.4380831384978
"Quicksilver 20230818 - Input: CTS2",
Higher Results Are Better
"a",
"b",11370000,11400000,11450000
"c",11430000,11390000,11430000
"Quicksilver 20230818 - Input: CORAL2 P1",
Higher Results Are Better
"a",
"b",12000000,11940000,11960000
"c",11960000,11970000,11930000
"Quicksilver 20230818 - Input: CORAL2 P2",
Higher Results Are Better
"a",
"b",22830000,22800000,22510000
"c",22560000,22460000,22590000
"Speedb 2.7 - Test: Random Fill",
Higher Results Are Better
"a",
"b",822307,818224,823170
"c",813936,820333,821750
"Speedb 2.7 - Test: Random Read",
Higher Results Are Better
"a",
"b",50961795,51127925,51097913
"c",51228655,50959771,51138237
"Speedb 2.7 - Test: Update Random",
Higher Results Are Better
"a",
"b",472577,470780,473670
"c",475589,476748,470839
"Speedb 2.7 - Test: Sequential Fill",
Higher Results Are Better
"a",
"b",940088,936836,929939
"c",949826,938167,946707
"Speedb 2.7 - Test: Random Fill Sync",
Higher Results Are Better
"a",
"b",11453,5568,5755,3820,6059,7305,4131,6467,6212,5547,6336,6071,6058,5936,6065
"c",11224,6288,3594,5615,6052,5216,5558,3738,5774,3303,4020,4216,3521,3783,4000
"Speedb 2.7 - Test: Read While Writing",
Higher Results Are Better
"a",
"b",2861194,2793117,2872462
"c",2975832,3023563,2883121
"Speedb 2.7 - Test: Read Random Write Random",
Higher Results Are Better
"a",
"b",1774322,1782991,1771179
"c",1779026,1779263,1777417
"TensorFlow 2.12 - Device: CPU - Batch Size: 1 - Model: VGG-16",
Higher Results Are Better
"a",
"b",1.45,1.45,1.44
"c",1.46,1.46,1.46
"TensorFlow 2.12 - Device: CPU - Batch Size: 1 - Model: AlexNet",
Higher Results Are Better
"a",
"b",4.69,4.68,4.69
"c",4.7,4.72,4.7
"TensorFlow 2.12 - Device: CPU - Batch Size: 16 - Model: VGG-16",
Higher Results Are Better
"a",
"b",3.52,3.52,3.53
"c",3.53,3.54,3.54
"TensorFlow 2.12 - Device: CPU - Batch Size: 16 - Model: AlexNet",
Higher Results Are Better
"a",
"b",40.36,40.29,40.36
"c",40.36,40.44,40.39
"TensorFlow 2.12 - Device: CPU - Batch Size: 1 - Model: GoogLeNet",
Higher Results Are Better
"a",
"b",12.15,12.23,12.27
"c",11.7,12.2,12.24
"TensorFlow 2.12 - Device: CPU - Batch Size: 1 - Model: ResNet-50",
Higher Results Are Better
"a",
"b",5.13,5.14,5.13
"c",5.16,5.15,5.15
"TensorFlow 2.12 - Device: CPU - Batch Size: 16 - Model: GoogLeNet",
Higher Results Are Better
"a",
"b",21.16,21.21,21.22
"c",21.23,21.33,21.18
"TensorFlow 2.12 - Device: CPU - Batch Size: 16 - Model: ResNet-50",
Higher Results Are Better
"a",
"b",7.65,7.65,7.66
"c",7.69,7.7,7.71
"Y-Cruncher 0.8.3 - Pi Digits To Calculate: 1B",
Lower Results Are Better
"a",
"b",49.825,49.84,49.819
"c",49.811,49.74,49.862
"Y-Cruncher 0.8.3 - Pi Digits To Calculate: 500M",
Lower Results Are Better
"a",
"b",23.051,23.032,23.149
"c",22.876,22.876,22.929