Intel Core i5-1145G7 testing with a LENOVO 20XW004AUS (N32ET71W 1.47 BIOS) and Intel Xe TGL GT2 3GB on Ubuntu 20.04 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 2203301-NE-ONEDNNTGL27
onednn tgl,
"oneDNN 2.6 - Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU",
Lower Results Are Better
"A",6.94004,7.02682,7.06002
"B",6.9419,6.9979,7.3188,7.79393,7.78461,7.79161,7.77666,7.80886,7.77624,7.77321,7.87525,7.75617
"C",6.9415,6.97344,7.392,7.81504,7.79077,7.78958,7.7721,7.78399,7.74646,7.73885,7.76937,7.75643,7.75648,7.78076,7.75572
"oneDNN 2.6 - Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU",
Lower Results Are Better
"A",2.0787,2.10517,2.14666
"B",2.08019,2.10589,2.33163,2.30762,2.30797,2.32224,2.34542,2.3104,2.31939,2.31281,2.30679,2.32691,2.3106,2.3162,2.32229
"C",2.06896,2.14182,2.38102,2.32615,2.31813,2.34133,2.32431,2.34246,2.30776,2.31762,2.32711,2.32717,2.32688,2.31936
"oneDNN 2.6 - Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU",
Lower Results Are Better
"A",5.63728,5.7484,6.11511,6.19587,6.17034,6.18478,6.11662,6.0875,6.18723,6.19699,6.22447,6.27022,6.14195,6.26792,6.2062
"B",5.49326,6.03135,6.36748,6.40834,6.45542,6.42157,6.32415,6.48791,6.38016,6.38016,6.28329,6.44821,6.37815,6.42036,6.39065
"C",5.59539,6.0319,6.89395,6.39397,6.48503,6.3981,6.41685,6.46372,6.39462,6.37622,6.3651,6.40435
"oneDNN 2.6 - Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU",
Lower Results Are Better
"A",10.0396,10.1939,10.2802
"B",9.832,9.9492,10.0107
"C",9.80809,9.81429,9.85224
"oneDNN 2.6 - Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU",
Lower Results Are Better
"A",2.3781,2.42022,2.43265
"B",2.3276,2.36306,2.38231
"C",2.30847,2.3512,2.36067
"oneDNN 2.6 - Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU",
Lower Results Are Better
"A",5.37921,5.41104,5.43874
"B",5.36143,5.41806,5.43939
"C",5.37666,5.43044,5.54937
"oneDNN 2.6 - Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU",
Lower Results Are Better
"A",38.2648,38.2689,38.2738
"B",38.2758,38.299,38.3593
"C",38.4693,38.4533,38.2739
"oneDNN 2.6 - Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU",
Lower Results Are Better
"A",37.912,38.0234,38.1723
"B",37.9156,37.9938,38.0124
"C",37.9134,37.9864,37.9828
"oneDNN 2.6 - Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU",
Lower Results Are Better
"A",4748.25,4784.67,4769.08
"B",4754.41,4773.45,4777.53
"C",4748.63,4765.94,4764.31
"oneDNN 2.6 - Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU",
Lower Results Are Better
"A",18.7767,20.0722,20.0008,19.9809,20.0056,19.9412,20.0878
"B",18.7905,20.1129,20.0644,19.9298,20.2386,19.8413,19.8696
"C",18.7796,20.2705,19.915,19.969,19.9133,19.8745,19.8888
"oneDNN 2.6 - Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU",
Lower Results Are Better
"A",4750.81,4764.57,4765.23
"B",4750.36,4766.72,4768.94
"C",4747.99,4766.68,4779.7
"oneDNN 2.6 - Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU",
Lower Results Are Better
"A",9313.52,9364.9,9353.49
"B",9315.15,9352.46,9359.69
"C",9319.92,9344.8,9358.42
"oneDNN 2.6 - Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU",
Lower Results Are Better
"A",9315.79,9358.94,9360.23
"B",9326.52,9356.98,9360.24
"C",9317.61,9362.75,9360.11
"oneDNN 2.6 - Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU",
Lower Results Are Better
"A",9328.24,9346.79,9352.66
"B",9314.02,9353.3,9352.78
"C",9309.3,9361.03,9353.24
"oneDNN 2.6 - Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU",
Lower Results Are Better
"A",4743.73,4775.59,4768.52
"B",4754.11,4763.64,4771.08
"C",4746.58,4772.69,4772.43
"oneDNN 2.6 - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU",
Lower Results Are Better
"A",8.20641,12.603,12.5671,13.4171,12.6082,12.5628,12.5581,12.5791,12.5483,12.5467,12.5607,12.5418
"B",8.30893,12.5771,12.5643,12.5523,12.541,12.5418,12.5542,12.5727,12.5204,12.5438,12.5312,12.5781
"C",8.38632,12.5806,12.5451,12.5602,12.5623,12.5578,12.5554,12.5464,12.5318,12.5618,12.5336,12.523
"oneDNN 2.6 - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU",
Lower Results Are Better
"A",1.327,1.73528,1.73887,1.73278,1.73749,1.73289,1.73246,1.73538,1.74013,1.73496,1.73644,1.73718
"B",1.37485,1.79915,1.72891,1.73274,1.73549,1.73861,1.73127,1.73426,1.73662,1.73329,1.73409,1.72836
"C",1.39843,1.72864,1.73853,1.74023,1.73265,1.72939,1.7268,1.73079,1.736,1.80357,1.72812,1.73608
"oneDNN 2.6 - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU",
Lower Results Are Better
"A",2.84322,4.09024,4.08094,4.08679,4.07774,4.0702,4.08084,4.07967,4.07359,4.07986,4.07566,4.073
"B",3.03033,4.07085,4.06565,4.06467,4.05993,4.06374,4.05777,4.06126,4.05599,4.05712,4.06309,4.05887
"C",3.05945,4.0622,4.06473,4.08237,4.0633,4.06129,4.05687,4.05894,4.0648,4.06266,4.0637,4.07114
"oneDNN 2.6 - Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU",
Lower Results Are Better
"A",51.3738,67.2858,64.0831,64.6889,64.2103,64.413,64.6258,64.4797,64.4499,64.5386,64.3825,64.1361
"B",51.3136,64.6988,64.9329,64.7256,64.8776,65.0267,65.1723,65.0949,65.5401,65.3103,65.1079,65.3402
"C",51.0746,65.9584,65.8816,66.0162,65.4886,65.5632,65.7868,65.351,65.7284,65.5381,65.5034,65.6394
"oneDNN 2.6 - Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU",
Lower Results Are Better
"A",2.28206,3.07822,3.0657,3.0804,3.08617,3.08075,3.07019,3.07486,3.07653,3.07404,3.06765,3.07216
"B",2.29596,3.10101,3.09625,3.10705,3.09684,3.10037,3.10503,3.11268,3.09968,3.10957,3.12191,3.10733
"C",2.26428,3.14738,3.14097,3.14353,3.14216,3.15096,3.16336,3.14909,3.16811,3.14446,3.14727,3.14936
"oneDNN 2.6 - Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU",
Lower Results Are Better
"A",14.2834,18.1742,17.9806,18.0477,18.1113,17.886,17.8973,17.8322,18.1775,18.0333,18.1221,17.8328
"B",14.1584,18.2298,18.0239,18.6669,18.1908,17.9732,18.4249,21.4837,18.4563,18.4093,18.8346,18.3703,18.076,18.3208,18.9002
"C",14.093,20.9138,18.6817,18.4612,18.2967,18.3289,18.1429,18.1055,18.4715,18.3393,18.3061,18.3905
"oneDNN 2.6 - Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU",
Lower Results Are Better
"A",8.14426,8.34683,9.02666,9.59828,9.73043,9.56739,9.83848,9.54732,9.57169,9.58655,9.59273,9.63654,9.78674,9.6117,9.61063
"B",7.96373,8.15764,10.2582,10.1613,10.1716,10.1411,10.5374,10.3691,10.2606,10.2352,10.2597,10.2343,10.2406,10.6564,10.3136
"C",7.88553,8.444,10.7151,10.6308,10.6753,10.9944,10.662,10.7343,10.7157,12.2955,10.6769,10.7659,10.9475,10.8281,10.7586
"oneDNN 2.6 - Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU",
Lower Results Are Better
"A",1.66246,1.90128,1.91889,2.06606,1.95994,1.95855,1.96774,1.98769,1.97979,1.97254,1.98351,1.97489,1.97634,2.00555,1.97996
"B",1.67741,2.12211,2.07507,2.06963,2.08944,2.09405,2.1049,2.27374,2.10803,2.10499,2.10018,2.09951,2.13261,2.10709
"C",1.70397,2.14287,2.18273,2.19199,2.20966,2.2435,2.23021,2.21856,2.19979,2.21382,2.21234,2.2266,2.22212
"oneDNN 2.6 - Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU",
Lower Results Are Better
"A",7.17453,9.05137,8.65738,8.49407,8.52077,8.32343,8.36902,8.56118,8.58118,8.5857,8.57902,8.6473
"B",7.18416,9.92029,9.06209,8.79631,8.6386,8.95115,9.10105,9.1772,9.19725,9.14207,9.16141,9.14868
"C",7.1713,9.75886,9.75603,9.58801,9.48819,9.30899,9.69584,10.299,9.723,9.92131,9.71416,9.78333