AMD Ryzen Threadripper 3990X 64-Core testing with a Gigabyte TRX40 AORUS PRO WIFI (F4p BIOS) and AMD Radeon RX 5700 8GB on Pop 21.10 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 2203314-PTS-ONEDNNON39
onednn onnx threadripper,
"oneDNN 2.6 - Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU",
Lower Results Are Better
"A",2.03994,1.97189,1.86475,1.93228,2.15272,1.99374,1.99141,1.94637,2.01396,2.14446,1.92219,1.98799,2.14471,2.00564,2.03094
"B",1.67943,1.84088,1.94657,2.23096,2.24395,1.6986,2.14825,2.17124,1.66244,2.11922,1.78869,2.04019
"C",2.21213,1.56742,1.72443,2.28012,2.05212,2.09199,2.05404,2.01608,1.91935,2.1988,2.02501,1.59008,2.10311,2.08061,1.99218
"D",1.71051,2.12537,2.00693,1.93472,1.53774,2.12963,1.33786,2.0911,1.65603,2.01338,2.13358,2.1432,2.24709,1.8867,1.79828
"oneDNN 2.6 - Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU",
Lower Results Are Better
"A",5.56284,5.55789,5.51089
"B",6.28415,6.26002,6.26799
"C",6.2468,6.41754,6.19554
"D",6.30118,6.54926,6.37374
"oneDNN 2.6 - Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU",
Lower Results Are Better
"A",2.71045,2.32347,2.34754,1.9983,2.22212,2.25777,2.49221,1.46475,2.22523,2.23005,1.84917,2.09091
"B",2.21794,2.06703,2.78216,2.02515,2.00164,2.05082,2.23223,2.92533,2.11962,2.27517,2.79233,2.85798,2.19493,2.11384,2.61796
"C",2.32795,2.41164,2.4818,1.76928,2.89732,2.18563,2.78732,2.20079,2.9464,2.15391,1.93536,2.39101
"D",2.55122,2.91067,2.17071,2.38494,2.32178,2.23475,2.56447,2.9389,2.46606,2.45689,2.16842,2.89911,2.39936,2.16187,1.69729
"oneDNN 2.6 - Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU",
Lower Results Are Better
"A",1.14222,1.13587,1.13514
"B",1.05213,1.13377,1.11355,1.13846,1.13327,1.12213,1.12455,1.13131,1.11046
"C",1.12063,1.11514,1.12203
"D",1.08594,1.1029,1.1323
"oneDNN 2.6 - Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU",
Lower Results Are Better
"A",
"B",
"C",
"D",
"oneDNN 2.6 - Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU",
Lower Results Are Better
"A",
"B",
"C",
"D",
"oneDNN 2.6 - Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU",
Lower Results Are Better
"A",0.943875,0.958506,0.921416
"B",0.921376,0.856843,0.910705,0.949936,0.936321,0.934491,0.830441,0.927029,0.862437,0.896614,0.888702,0.948278,0.937906,0.873586,0.976177
"C",0.893288,0.896809,0.922232
"D",0.9285,1.01441,0.974124,0.878217,0.890379,0.927497,0.891677,0.950247,0.898488,0.953517,0.908161,0.923159,0.971599,0.91094,0.905138
"oneDNN 2.6 - Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU",
Lower Results Are Better
"A",6.61349,6.65805,6.76861
"B",6.82153,6.75694,6.88652
"C",6.82988,6.81086,6.91042
"D",6.84258,6.9764,6.89922
"oneDNN 2.6 - Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU",
Lower Results Are Better
"A",2.10236,2.11254,2.1036
"B",2.09748,2.11891,2.11436
"C",2.10656,2.10745,2.12234
"D",2.10152,2.12122,2.11165
"oneDNN 2.6 - Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU",
Lower Results Are Better
"A",6.39293,6.42283,6.36714
"B",6.39276,6.44065,6.46649
"C",6.46265,6.44265,6.43537
"D",6.46947,6.433,6.42743
"oneDNN 2.6 - Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU",
Lower Results Are Better
"A",1.51047,1.53524,1.53285
"B",1.04016,1.55399,1.51899,1.53218,1.55059,1.53559,1.55152,1.54749,1.5492,1.54088,1.52198,1.54194
"C",1.56262,1.55888,1.5585
"D",1.45831,1.4893,1.41773
"oneDNN 2.6 - Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU",
Lower Results Are Better
"A",0.978895,0.976297,0.982214
"B",0.992102,0.994817,0.99122
"C",0.978118,0.98915,0.993792
"D",0.98364,0.986261,0.984557
"oneDNN 2.6 - Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU",
Lower Results Are Better
"A",4912.96,5010.48,4956.43
"B",4987.66,5017.18,5007.14
"C",5020.96,4867.03,5005.78
"D",4969.5,5012.53,4488.56,5065.06,4777.41,4237.72,4936.8,5035.68,5014.85,5003.58,5012.64,4937.59,4841.06,5015.21,4886.02
"oneDNN 2.6 - Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU",
Lower Results Are Better
"A",1261.8,1257.24,1261.55
"B",1262.54,1270.93,1220.24
"C",1216.93,1221,1225.43
"D",1228.34,1210.48,1195.5
"oneDNN 2.6 - Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU",
Lower Results Are Better
"A",4653.29,4982.4,5017.48,5013.89,5014.96,4990.78,5040.04,4990.25,4885.99
"B",5082.81,5018.82,5002.87
"C",5015.97,4984.65,5009.51
"D",4978.07,4961.54,4913.29
"oneDNN 2.6 - Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU",
Lower Results Are Better
"A",
"B",
"C",
"D",
"oneDNN 2.6 - Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU",
Lower Results Are Better
"A",
"B",
"C",
"D",
"oneDNN 2.6 - Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU",
Lower Results Are Better
"A",
"B",
"C",
"D",
"oneDNN 2.6 - Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU",
Lower Results Are Better
"A",1221.4,1253.59,1235.26
"B",1235.51,1215.03,1142.49,1208.84,1298.02,1253.3,1235.92,1268,1266.81,1264.95,1285.22,1281.7,1265,1196.93,1273.36
"C",1234.9,1244.31,1236.59
"D",1218.21,1233.56,1218.81
"oneDNN 2.6 - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU",
Lower Results Are Better
"A",8.33663,5.28917,6.3137,7.33055,7.55291,8.30074,9.20096,8.94882,8.338,8.10985,7.9795,7.76129,5.35725,6.34266,8.71268
"B",5.82897,7.77384,5.49884,6.95448,5.55672,7.01952,4.99226,8.55678,8.0972,5.61104,8.75139,5.54443,9.3996,8.78098,5.58584
"C",9.28992,4.60114,8.27241,7.90648,7.59848,7.17661,9.39131,6.09226,6.08994,9.0922,8.74681,7.39811,4.61288,8.43659,8.98601
"D",8.19189,7.44395,6.4743,7.13679,5.72769,4.84669,8.47525,8.71952,5.94306,6.50437,6.43327,8.70089
"oneDNN 2.6 - Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU",
Lower Results Are Better
"A",5032.5,5022.06,5029.62
"B",4979.77,5009.98,5003.72
"C",4991.3,5027.22,5015.96
"D",4041.91,4991.61,5042.63,5019.15,5015.83,5021.43,4558.39,5043.93,5051.31,5035.93,4766.67,4919.77,4995.18
"oneDNN 2.6 - Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU",
Lower Results Are Better
"A",806.682,1254.14,1246.89,1299.11,1241.55,1275.15,1220.27,1234.96,1244.31,1170.36,1257.85,1251
"B",1243.91,1226.55,1256.87
"C",1273.65,1224.32,1254.99
"D",1269.58,1233.91,1259.67
"oneDNN 2.6 - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU",
Lower Results Are Better
"A",12.3319,10.2938,11.1856,11.3923,11.843,11.2814,11.2315,12.364,10.3807,12.5835,11.9916,13.7497,11.013,11.5196,10.8932
"B",11.419,11.4205,11.1953
"C",12.4391,11.2234,13.4052,10.9556,10.9072,9.303,11.0866,13.5947,13.4241,11.0563,12.4178,13.5436,11.2353,12.5002,11.4602
"D",12.8074,11.2001,12.3185,12.4964,12.4562,11.0907,12.4109,12.9069,12.214,12.7154,9.87261,8.53668
"oneDNN 2.6 - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU",
Lower Results Are Better
"A",
"B",
"C",
"D",
"ONNX Runtime 1.11 - Model: GPT-2 - Device: CPU - Executor: Parallel",
Higher Results Are Better
"A",3452,3476,3455.5
"B",3511.5,3512,3513
"C",3516.5,3532,3537.5
"D",3498.5,3500.5,3487
"ONNX Runtime 1.11 - Model: GPT-2 - Device: CPU - Executor: Standard",
Higher Results Are Better
"A",4099.5,4393.5,4074.5,3868,4324.5,4362.5,4363,4368.5,4026,4411,3911.5,4428.5
"B",4656,4711.5,4761.5
"C",4845.5,4788,4835
"D",4202,4485.5,4370.5,4576,4499,4398.5,4427.5,4188.5,4305.5,4594,4616,4623
"ONNX Runtime 1.11 - Model: yolov4 - Device: CPU - Executor: Parallel",
Higher Results Are Better
"A",360,360,361.5
"B",361.5,361,362
"C",363,362,362
"D",361,361.5,361.5
"ONNX Runtime 1.11 - Model: yolov4 - Device: CPU - Executor: Standard",
Higher Results Are Better
"A",283.5,297,295.5,296
"B",294.5,294,290
"C",294.5,298.5,293
"D",298,301.5,300.5
"ONNX Runtime 1.11 - Model: bertsquad-12 - Device: CPU - Executor: Parallel",
Higher Results Are Better
"A",418.5,428.5,425
"B",422.5,427.5,425
"C",428,434,432.5
"D",419.5,422,422.5
"ONNX Runtime 1.11 - Model: bertsquad-12 - Device: CPU - Executor: Standard",
Higher Results Are Better
"A",525,537.5,529
"B",646,646,648
"C",645.5,642,649
"D",639.5,642.5,644
"ONNX Runtime 1.11 - Model: fcn-resnet101-11 - Device: CPU - Executor: Parallel",
Higher Results Are Better
"A",82,81.5,81.5
"B",80,80.5,80.5
"C",80.5,81,81
"D",81,81,80.5
"ONNX Runtime 1.11 - Model: fcn-resnet101-11 - Device: CPU - Executor: Standard",
Higher Results Are Better
"A",154,153,153
"B",155,156,156.5
"C",152,153.5,154.5
"D",156.5,157.5,156
"ONNX Runtime 1.11 - Model: ArcFace ResNet-100 - Device: CPU - Executor: Parallel",
Higher Results Are Better
"A",1095.5,1089,1078
"B",1079.5,1070.5,1064.5
"C",1071,1093.5,1071.5
"D",1078,1075,1083
"ONNX Runtime 1.11 - Model: ArcFace ResNet-100 - Device: CPU - Executor: Standard",
Higher Results Are Better
"A",984,1001.5,999.5
"B",1007,1003.5,1020.5
"C",1018.5,1010.5,1021.5
"D",993.5,977.5,1003
"ONNX Runtime 1.11 - Model: super-resolution-10 - Device: CPU - Executor: Parallel",
Higher Results Are Better
"A",3883.5,3790.5,3771
"B",3818,3769,3753
"C",3757.5,3836,3757.5
"D",3848,3736.5,3654,3686.5
"ONNX Runtime 1.11 - Model: super-resolution-10 - Device: CPU - Executor: Standard",
Higher Results Are Better
"A",7214,7343,7412
"B",4695.5,7518,7487,7473,7580.5,7575,4793,4876,4785,4828,7594,7610
"C",7475,7638.5,7565.5
"D",7291,7379.5,7453.5