Intel Core i9-12900K testing with a ASUS ROG STRIX Z690-E GAMING WIFI (1003 BIOS) and Gigabyte AMD Radeon RX 6800 XT 16GB on Ubuntu 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 2203319-NE-ONEDNNONN20
onednn onnx alderlake,
"ONNX Runtime 1.11 - Model: GPT-2 - Device: CPU - Executor: Parallel",
Higher Results Are Better
"A",8094,7978,7893.5
"B",8206,8064.5,8060.5
"C",8071.5,7967,8098.5
"D",8143.5,8075,8065.5
"ONNX Runtime 1.11 - Model: GPT-2 - Device: CPU - Executor: Standard",
Higher Results Are Better
"A",11008.5,11039,11056
"B",11097.5,11059.5,11073.5
"C",11057,11076.5,11103
"D",11092.5,11204.5,11072.5
"ONNX Runtime 1.11 - Model: yolov4 - Device: CPU - Executor: Parallel",
Higher Results Are Better
"A",622,630.5,630
"B",632.5,628.5,635.5
"C",630.5,635,628
"D",634.5,631,634.5
"ONNX Runtime 1.11 - Model: yolov4 - Device: CPU - Executor: Standard",
Higher Results Are Better
"A",673.5,665.5,670.5
"B",660.5,672.5,659.5
"C",670.5,665.5,661
"D",662.5,670.5,674.5
"ONNX Runtime 1.11 - Model: bertsquad-12 - Device: CPU - Executor: Parallel",
Higher Results Are Better
"A",911,925.5,939
"B",956,922,904.5,953.5,948.5
"C",908,949.5,940
"D",950.5,971.5,969
"ONNX Runtime 1.11 - Model: bertsquad-12 - Device: CPU - Executor: Standard",
Higher Results Are Better
"A",925.5,926.5,941
"B",920.5,908.5,920
"C",900,925,929.5
"D",921,926,930
"ONNX Runtime 1.11 - Model: fcn-resnet101-11 - Device: CPU - Executor: Parallel",
Higher Results Are Better
"A",109.5,111,111.5
"B",111,110.5,111.5
"C",110.5,111,111
"D",111.5,111.5,111
"ONNX Runtime 1.11 - Model: fcn-resnet101-11 - Device: CPU - Executor: Standard",
Higher Results Are Better
"A",95.5,96.5,95.5
"B",94.5,95.5,94.5
"C",95,95,94
"D",93.5,93.5,96.5
"ONNX Runtime 1.11 - Model: ArcFace ResNet-100 - Device: CPU - Executor: Parallel",
Higher Results Are Better
"A",362.5,363,363
"B",364,362.5,364
"C",362.5,362,362
"D",363,364,363.5
"ONNX Runtime 1.11 - Model: ArcFace ResNet-100 - Device: CPU - Executor: Standard",
Higher Results Are Better
"A",1914.5,1912.5,1897.5
"B",1912,1908.5,1918.5
"C",1911,1905,1873
"D",1900.5,1886.5,1892
"ONNX Runtime 1.11 - Model: super-resolution-10 - Device: CPU - Executor: Parallel",
Higher Results Are Better
"A",4477.5,4624.5,4451
"B",4457,4629,4425.5
"C",4399.5,4498,4434
"D",4509,4405,4604
"ONNX Runtime 1.11 - Model: super-resolution-10 - Device: CPU - Executor: Standard",
Higher Results Are Better
"A",5291,5345,5360
"B",5307,5329,5411
"C",5356,5357,5271
"D",5356.5,5309,5486
"oneDNN 2.6 - Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU",
Lower Results Are Better
"A",2.63757,2.64645,2.6277
"B",2.63276,2.64038,2.68092
"C",2.6238,2.63898,2.63261
"D",2.6357,2.63566,2.62726
"oneDNN 2.6 - Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU",
Lower Results Are Better
"A",3.9112,3.91162,3.91356
"B",3.82444,3.81943,3.81275
"C",3.88873,3.8812,3.88468
"D",3.89839,3.88428,3.87885
"oneDNN 2.6 - Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU",
Lower Results Are Better
"A",1.05305,1.06756,1.22908,1.16846,1.07145,1.05449,1.04545,1.04292,1.05682,1.03987,1.06873,1.20038,1.04233,1.04477,1.05654
"B",1.03845,1.0643,1.0646
"C",1.05191,1.07487,1.04583
"D",1.05984,1.05521,1.06265
"oneDNN 2.6 - Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU",
Lower Results Are Better
"A",0.881036,0.882572,0.882637
"B",0.844201,0.854517,0.856163
"C",0.87902,0.879792,0.886584
"D",0.879114,0.877714,0.898342
"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",5.90799,5.91701,5.90094
"B",5.90642,5.90784,5.89533
"C",5.9041,5.9131,5.90015
"D",5.90583,5.91266,5.9094
"oneDNN 2.6 - Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU",
Lower Results Are Better
"A",8.32329,8.15127,7.79006,8.10477,8.76683,8.86377,8.51605,8.8327,8.19867,7.8465,8.2217,8.85499,7.90151,7.91136,7.86852
"B",8.74759,9.33862,8.85673,8.75241,8.19149,8.07491,8.41032,8.34781,8.1631,8.21891,7.79329,8.72145,7.96395,7.79698
"C",7.80444,8.34408,9.0409,7.85712,7.82737,8.32046,7.81587,8.6328,7.96992,8.45562,7.86863,8.71027,8.02918,8.17939,8.35616
"D",8.10059,7.93727,8.11465
"oneDNN 2.6 - Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU",
Lower Results Are Better
"A",5.2492,5.24541,5.24148
"B",5.24118,5.25008,5.24677
"C",5.24767,5.24861,5.24315
"D",5.2533,5.24908,5.24241
"oneDNN 2.6 - Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU",
Lower Results Are Better
"A",6.05121,6.0614,6.05047
"B",6.04881,6.04729,6.04234
"C",6.0518,6.05859,6.05946
"D",6.04853,6.05198,6.04344
"oneDNN 2.6 - Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU",
Lower Results Are Better
"A",1.3299,1.38642,1.34496
"B",1.38037,1.331,1.34859
"C",1.34028,1.33287,1.34869
"D",1.33147,1.35792,1.33948
"oneDNN 2.6 - Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU",
Lower Results Are Better
"A",2.21698,2.21408,2.22104
"B",2.22196,2.2194,2.21888
"C",2.22362,2.2197,2.21893
"D",2.22534,2.22056,2.212
"oneDNN 2.6 - Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU",
Lower Results Are Better
"A",2889.74,2881.36,2878.27
"B",2883.82,2871.25,2872.71
"C",2885.01,2877.41,2882.87
"D",2873.01,2883.07,2883.69
"oneDNN 2.6 - Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU",
Lower Results Are Better
"A",1612.46,1612.5,1618.65
"B",1613.99,1607.57,1613.83
"C",1613.88,1611.49,1613.55
"D",1612.95,1613.67,1617.73
"oneDNN 2.6 - Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU",
Lower Results Are Better
"A",2871.35,2892.45,2882.07
"B",2890.35,2884.02,2883.51
"C",2885,2882.22,2880.63
"D",2884.37,2881.19,2875.5
"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",1627.56,1611.46,1613.63
"B",1615.6,1613.28,1609.51
"C",1611.72,1613.79,1608.29
"D",1613.97,1613.29,1615.54
"oneDNN 2.6 - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU",
Lower Results Are Better
"A",1.2707,1.2423,1.22402
"B",1.23192,1.24885,1.28454
"C",1.30468,1.2618,1.28647
"D",1.26062,1.27067,1.44168,1.54955,1.33617,1.2631,1.26812,1.31396,1.28125,1.27553,1.29838,1.27614,1.2877,1.31055,1.22872
"oneDNN 2.6 - Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU",
Lower Results Are Better
"A",2880.53,2894.07,2880.23
"B",2873.42,2883.12,2888.07
"C",2880.8,2883.08,2874.72
"D",2882.04,2883.99,2880.64
"oneDNN 2.6 - Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU",
Lower Results Are Better
"A",1610.6,1613.53,1621.12
"B",1674.77,1607.13,1609.69
"C",1616.7,1614,1625.65
"D",1620.41,1614.43,1613.5
"oneDNN 2.6 - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU",
Lower Results Are Better
"A",0.838316,1.1688,1.15045,1.03258,0.86114,0.858388,1.12128,1.89123,0.870793,0.82356,0.831457,0.95585
"B",0.825509,0.829912,0.823595
"C",0.836859,0.836194,0.855941
"D",1.25102,1.04336,0.822461,0.835157,0.856657,0.836316,0.848581,1.87979,0.827125,0.817618,0.863387,1.25222
"oneDNN 2.6 - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU",
Lower Results Are Better
"A",
"B",
"C",
"D",