pytorch icelake

Intel Core i7-1065G7 testing with a Dell 06CDVY (1.0.9 BIOS) and Intel Iris Plus ICL GT2 16GB on Ubuntu 23.04 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 2311164-NE-PYTORCHIC86
Jump To Table - Results

View

Do Not Show Noisy Results
Do Not Show Results With Incomplete Data
Do Not Show Results With Little Change/Spread
List Notable Results

Statistics

Show Overall Harmonic Mean(s)
Show Overall Geometric Mean
Show Wins / Losses Counts (Pie Chart)
Normalize Results
Remove Outliers Before Calculating Averages

Graph Settings

Force Line Graphs Where Applicable
Convert To Scalar Where Applicable
Prefer Vertical Bar Graphs

Multi-Way Comparison

Condense Multi-Option Tests Into Single Result Graphs

Table

Show Detailed System Result Table

Run Management

Highlight
Result
Hide
Result
Result
Identifier
View Logs
Performance Per
Dollar
Date
Run
  Test
  Duration
a
November 16 2023
  1 Hour, 17 Minutes
b
November 16 2023
  1 Hour, 20 Minutes
c
November 16 2023
  1 Hour, 19 Minutes
Invert Hiding All Results Option
  1 Hour, 19 Minutes

Only show results where is faster than
Only show results matching title/arguments (delimit multiple options with a comma):
Do not show results matching title/arguments (delimit multiple options with a comma):


{ "title": "pytorch icelake", "last_modified": "2023-11-16 18:57:46", "description": "Intel Core i7-1065G7 testing with a Dell 06CDVY (1.0.9 BIOS) and Intel Iris Plus ICL GT2 16GB on Ubuntu 23.04 via the Phoronix Test Suite.", "systems": { "a": { "identifier": "a", "hardware": { "Processor": "Intel Core i7-1065G7 @ 3.90GHz (4 Cores \/ 8 Threads)", "Motherboard": "Dell 06CDVY (1.0.9 BIOS)", "Chipset": "Intel Ice Lake-LP DRAM", "Memory": "16GB", "Disk": "Toshiba KBG40ZPZ512G NVMe 512GB", "Graphics": "Intel Iris Plus ICL GT2 16GB (1100MHz)", "Audio": "Realtek ALC289", "Network": "Intel Ice Lake-LP PCH CNVi WiFi" }, "software": { "OS": "Ubuntu 23.04", "Kernel": "6.2.0-35-generic (x86_64)", "Desktop": "GNOME Shell 44.3", "Display Server": "X Server + Wayland", "OpenGL": "4.6 Mesa 23.0.4-0ubuntu1~23.04.1", "OpenCL": "OpenCL 3.0", "Compiler": "GCC 12.3.0", "File-System": "ext4", "Screen Resolution": "1920x1200" }, "user": "phoronix", "timestamp": "2023-11-16 14:26:28", "client_version": "10.8.4", "data": { "cpu-scaling-governor": "intel_pstate powersave (EPP: balance_performance)", "cpu-microcode": "0xbc", "cpu-thermald": "2.5.2", "kernel-extra-details": "Transparent Huge Pages: madvise", "python": "Python 3.11.4", "security": "gather_data_sampling: Mitigation of Microcode + itlb_multihit: KVM: Mitigation of VMX disabled + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Mitigation of Clear buffers; SMT vulnerable + retbleed: Mitigation of Enhanced IBRS + spec_rstack_overflow: Not affected + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy\/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Enhanced IBRS IBPB: conditional RSB filling PBRSB-eIBRS: SW sequence + srbds: Mitigation of Microcode + tsx_async_abort: Not affected" } }, "b": { "identifier": "b", "hardware": { "Processor": "Intel Core i7-1065G7 @ 3.90GHz (4 Cores \/ 8 Threads)", "Motherboard": "Dell 06CDVY (1.0.9 BIOS)", "Chipset": "Intel Ice Lake-LP DRAM", "Memory": "16GB", "Disk": "Toshiba KBG40ZPZ512G NVMe 512GB", "Graphics": "Intel Iris Plus ICL GT2 16GB (1100MHz)", "Audio": "Realtek ALC289", "Network": "Intel Ice Lake-LP PCH CNVi WiFi" }, "software": { "OS": "Ubuntu 23.04", "Kernel": "6.2.0-35-generic (x86_64)", "Desktop": "GNOME Shell 44.3", "Display Server": "X Server + Wayland", "OpenGL": "4.6 Mesa 23.0.4-0ubuntu1~23.04.1", "OpenCL": "OpenCL 3.0", "Compiler": "GCC 12.3.0", "File-System": "ext4", "Screen Resolution": "1920x1200" }, "user": "phoronix", "timestamp": "2023-11-16 15:47:14", "client_version": "10.8.4", "data": { "cpu-scaling-governor": "intel_pstate powersave (EPP: balance_performance)", "cpu-microcode": "0xbc", "cpu-thermald": "2.5.2", "kernel-extra-details": "Transparent Huge Pages: madvise", "python": "Python 3.11.4", "security": "gather_data_sampling: Mitigation of Microcode + itlb_multihit: KVM: Mitigation of VMX disabled + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Mitigation of Clear buffers; SMT vulnerable + retbleed: Mitigation of Enhanced IBRS + spec_rstack_overflow: Not affected + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy\/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Enhanced IBRS IBPB: conditional RSB filling PBRSB-eIBRS: SW sequence + srbds: Mitigation of Microcode + tsx_async_abort: Not affected" } }, "c": { "identifier": "c", "hardware": { "Processor": "Intel Core i7-1065G7 @ 3.90GHz (4 Cores \/ 8 Threads)", "Motherboard": "Dell 06CDVY (1.0.9 BIOS)", "Chipset": "Intel Ice Lake-LP DRAM", "Memory": "16GB", "Disk": "Toshiba KBG40ZPZ512G NVMe 512GB", "Graphics": "Intel Iris Plus ICL GT2 16GB (1100MHz)", "Audio": "Realtek ALC289", "Network": "Intel Ice Lake-LP PCH CNVi WiFi" }, "software": { "OS": "Ubuntu 23.04", "Kernel": "6.2.0-35-generic (x86_64)", "Desktop": "GNOME Shell 44.3", "Display Server": "X Server + Wayland", "OpenGL": "4.6 Mesa 23.0.4-0ubuntu1~23.04.1", "OpenCL": "OpenCL 3.0", "Compiler": "GCC 12.3.0", "File-System": "ext4", "Screen Resolution": "1920x1200" }, "user": "phoronix", "timestamp": "2023-11-16 17:35:54", "client_version": "10.8.4", "data": { "cpu-scaling-governor": "intel_pstate powersave (EPP: balance_performance)", "cpu-microcode": "0xbc", "cpu-thermald": "2.5.2", "kernel-extra-details": "Transparent Huge Pages: madvise", "python": "Python 3.11.4", "security": "gather_data_sampling: Mitigation of Microcode + itlb_multihit: KVM: Mitigation of VMX disabled + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Mitigation of Clear buffers; SMT vulnerable + retbleed: Mitigation of Enhanced IBRS + spec_rstack_overflow: Not affected + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy\/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Enhanced IBRS IBPB: conditional RSB filling PBRSB-eIBRS: SW sequence + srbds: Mitigation of Microcode + tsx_async_abort: Not affected" } } }, "results": { "0f8d8cb3b9eaa2299a391dfeb4ecf8e83c675ab3": { "identifier": "pts\/pytorch-1.0.0", "title": "PyTorch", "app_version": "2.1", "arguments": "cpu 1 resnet152", "description": "Device: CPU - Batch Size: 1 - Model: ResNet-152", "scale": "batches\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 8.4900000000000002131628207280300557613372802734375, "raw_values": [ 8.4888305159417001277688541449606418609619140625 ], "min_result": [ "6.35" ], "max_result": [ "9.32" ], "test_run_times": [ 133.68999999999999772626324556767940521240234375 ] }, "b": { "value": 6.36000000000000031974423109204508364200592041015625, "raw_values": [ 6.3646600120501997110977754346095025539398193359375 ], "min_result": [ "5.69" ], "max_result": [ "9.22" ], "test_run_times": [ 173.44999999999998863131622783839702606201171875 ] }, "c": { "value": 8.5600000000000004973799150320701301097869873046875, "raw_values": [ 8.5616535113042999682875233702361583709716796875 ], "min_result": [ "7.47" ], "max_result": [ "9.14" ], "test_run_times": [ 132.340000000000003410605131648480892181396484375 ] } } }, "594d16c50ef13421d29a77ac009ce481ebc2a82c": { "identifier": "pts\/pytorch-1.0.0", "title": "PyTorch", "app_version": "2.1", "arguments": "cpu 16 resnet50", "description": "Device: CPU - Batch Size: 16 - Model: ResNet-50", "scale": "batches\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 8.8900000000000005684341886080801486968994140625, "raw_values": [ 8.885165113142900139564517303369939327239990234375 ], "min_result": [ "8.06" ], "max_result": [ "10.08" ], "test_run_times": [ 193.740000000000009094947017729282379150390625 ] }, "b": { "value": 7.7599999999999997868371792719699442386627197265625, "raw_values": [ 7.7606329752010001499229474575258791446685791015625 ], "min_result": [ "7.29" ], "max_result": [ "9.67" ], "test_run_times": [ 216.259999999999990905052982270717620849609375 ] }, "c": { "value": 8.9000000000000003552713678800500929355621337890625, "raw_values": [ 8.90195595130339967226973385550081729888916015625 ], "min_result": [ "7.69" ], "max_result": [ "10.29" ], "test_run_times": [ 185.43000000000000682121026329696178436279296875 ] } } }, "4f2db05f6bebd9b371472ed1afa49f37fc27fa2a": { "identifier": "pts\/pytorch-1.0.0", "title": "PyTorch", "app_version": "2.1", "arguments": "cpu 16 resnet152", "description": "Device: CPU - Batch Size: 16 - Model: ResNet-152", "scale": "batches\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 3.640000000000000124344978758017532527446746826171875, "raw_values": [ 3.63846361448610000621783910901285707950592041015625 ], "min_result": [ "3.31" ], "max_result": [ "4.51" ], "test_run_times": [ 473.3999999999999772626324556767940521240234375 ] }, "b": { "value": 3.359999999999999875655021241982467472553253173828125, "raw_values": [ 3.363671011660600118631236910005100071430206298828125 ], "min_result": [ "3.25" ], "max_result": [ "4.17" ], "test_run_times": [ 513.6799999999999499777914024889469146728515625 ] }, "c": { "value": 3.4199999999999999289457264239899814128875732421875, "raw_values": [ 3.417853197538600173999157050275243818759918212890625 ], "min_result": [ "3.26" ], "max_result": [ "4.14" ], "test_run_times": [ 494.81999999999999317878973670303821563720703125 ] } } }, "978835f6419ea069b931b1248ebb55334626a270": { "identifier": "pts\/pytorch-1.0.0", "title": "PyTorch", "app_version": "2.1", "arguments": "cpu 256 resnet50", "description": "Device: CPU - Batch Size: 256 - Model: ResNet-50", "scale": "batches\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 8.5800000000000000710542735760100185871124267578125, "raw_values": [ 8.5836250080869991307963573490269482135772705078125 ], "min_result": [ "6.92" ], "max_result": [ "10.34" ], "test_run_times": [ 199.19999999999998863131622783839702606201171875 ] }, "b": { "value": 8.3900000000000005684341886080801486968994140625, "raw_values": [ 8.38509740164870009948572260327637195587158203125 ], "min_result": [ "7.8" ], "max_result": [ "9.76" ], "test_run_times": [ 207.5 ] }, "c": { "value": 8.1300000000000007815970093361102044582366943359375, "raw_values": [ 8.12862123039860051676441798917949199676513671875 ], "min_result": [ "6.05" ], "max_result": [ "9.53" ], "test_run_times": [ 209.479999999999989768184605054557323455810546875 ] } } }, "0edee3d498a90f64b0d6ee6cfc4c8eee8a3c3a6c": { "identifier": "pts\/pytorch-1.0.0", "title": "PyTorch", "app_version": "2.1", "arguments": "cpu 512 resnet50", "description": "Device: CPU - Batch Size: 512 - Model: ResNet-50", "scale": "batches\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 8, "raw_values": [ 7.9976819038227997538115232600830495357513427734375 ], "min_result": [ "7.22" ], "max_result": [ "8.96" ], "test_run_times": [ 209.039999999999992041921359486877918243408203125 ] }, "b": { "value": 8.339999999999999857891452847979962825775146484375, "raw_values": [ 8.3351788501620003302150507806800305843353271484375 ], "min_result": [ "7.45" ], "max_result": [ "9.54" ], "test_run_times": [ 207.789999999999992041921359486877918243408203125 ] }, "c": { "value": 8.339999999999999857891452847979962825775146484375, "raw_values": [ 8.336409839084499395767124951817095279693603515625 ], "min_result": [ "7.74" ], "max_result": [ "9.75" ], "test_run_times": [ 208.400000000000005684341886080801486968994140625 ] } } }, "06433753eb3461ed54a6c8a439305e4be1795a41": { "identifier": "pts\/pytorch-1.0.0", "title": "PyTorch", "app_version": "2.1", "arguments": "cpu 1 efficientnet_v2_l", "description": "Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l", "scale": "batches\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 4.54999999999999982236431605997495353221893310546875, "raw_values": [ 4.55285142800730024958966168924234807491302490234375 ], "min_result": [ "4.07" ], "max_result": [ "6.31" ], "test_run_times": [ 245.340000000000003410605131648480892181396484375 ] }, "b": { "value": 4.4000000000000003552713678800500929355621337890625, "raw_values": [ 4.39901652339070015074184993864037096500396728515625 ], "min_result": [ "4.2" ], "max_result": [ "6.5" ], "test_run_times": [ 253.509999999999990905052982270717620849609375 ] }, "c": { "value": 4.37999999999999989341858963598497211933135986328125, "raw_values": [ 4.382045830311700029824351076968014240264892578125 ], "min_result": [ "3.81" ], "max_result": [ "6.1" ], "test_run_times": [ 254.1200000000000045474735088646411895751953125 ] } } }, "4c7bf00e1ffdac6120c4e7e06f896a2dcf99c6a6": { "identifier": "pts\/pytorch-1.0.0", "title": "PyTorch", "app_version": "2.1", "arguments": "cpu 1 resnet50", "description": "Device: CPU - Batch Size: 1 - Model: ResNet-50", "scale": "batches\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 20.96000000000000085265128291212022304534912109375, "raw_values": [ 20.95810573928699938051067874766886234283447265625 ], "min_result": [ "16.4" ], "max_result": [ "22.22" ], "test_run_times": [ 56.1700000000000017053025658242404460906982421875 ] }, "b": { "value": 21.760000000000001563194018672220408916473388671875, "raw_values": [ 21.761981570614000958130418439395725727081298828125 ], "min_result": [ "17.9" ], "max_result": [ "22.71" ], "test_run_times": [ 53.96000000000000085265128291212022304534912109375 ] }, "c": { "value": 21.4200000000000017053025658242404460906982421875, "raw_values": [ 21.422675208341001251710622454993426799774169921875 ], "min_result": [ "18.9" ], "max_result": [ "22.08" ], "test_run_times": [ 54.77000000000000312638803734444081783294677734375 ] } } }, "7afb544279ee7a1db49b8aea8c866917dad4b860": { "identifier": "pts\/pytorch-1.0.0", "title": "PyTorch", "app_version": "2.1", "arguments": "cpu 512 resnet152", "description": "Device: CPU - Batch Size: 512 - Model: ResNet-152", "scale": "batches\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 3.5099999999999997868371792719699442386627197265625, "raw_values": [ 3.508049980782100174536708436789922416210174560546875 ], "min_result": [ "2.9" ], "max_result": [ "4.19" ], "test_run_times": [ 493.720000000000027284841053187847137451171875 ] }, "b": { "value": 3.390000000000000124344978758017532527446746826171875, "raw_values": [ 3.386423121935899782641854471876285970211029052734375 ], "min_result": [ "3.19" ], "max_result": [ "4.18" ], "test_run_times": [ 510.279999999999972715158946812152862548828125 ] }, "c": { "value": 3.399999999999999911182158029987476766109466552734375, "raw_values": [ 3.395347957309100106471078106551431119441986083984375 ], "min_result": [ "3.26" ], "max_result": [ "4.12" ], "test_run_times": [ 507.31000000000000227373675443232059478759765625 ] } } }, "13d636d41dec40a015e80ae0ca58358ace25a739": { "identifier": "pts\/pytorch-1.0.0", "title": "PyTorch", "app_version": "2.1", "arguments": "cpu 256 resnet152", "description": "Device: CPU - Batch Size: 256 - Model: ResNet-152", "scale": "batches\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 3.45999999999999996447286321199499070644378662109375, "raw_values": [ 3.463230620110400170830189381376840174198150634765625 ], "min_result": [ "3.03" ], "max_result": [ "4.01" ], "test_run_times": [ 496.3600000000000136424205265939235687255859375 ] }, "b": { "value": 3.359999999999999875655021241982467472553253173828125, "raw_values": [ 3.357758493575300207112377393059432506561279296875 ], "min_result": [ "3.23" ], "max_result": [ "4.14" ], "test_run_times": [ 513.470000000000027284841053187847137451171875 ] }, "c": { "value": 3.390000000000000124344978758017532527446746826171875, "raw_values": [ 3.39214164430909992375973160960711538791656494140625 ], "min_result": [ "3.18" ], "max_result": [ "4.19" ], "test_run_times": [ 511.31000000000000227373675443232059478759765625 ] } } }, "c2e61282c984934f432761184e26030c16efcb9a": { "identifier": "pts\/pytorch-1.0.0", "title": "PyTorch", "app_version": "2.1", "arguments": "cpu 16 efficientnet_v2_l", "description": "Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l", "scale": "batches\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 2.470000000000000195399252334027551114559173583984375, "raw_values": [ 2.465259736081999886181392867001704871654510498046875 ], "min_result": [ "2.2" ], "max_result": [ "3.04" ], "test_run_times": [ 694.3899999999999863575794734060764312744140625 ] }, "b": { "value": 2.410000000000000142108547152020037174224853515625, "raw_values": [ 2.406285802581499932983888356829993426799774169921875 ], "min_result": [ "2.19" ], "max_result": [ "2.95" ], "test_run_times": [ 708.1499999999999772626324556767940521240234375 ] }, "c": { "value": 2.399999999999999911182158029987476766109466552734375, "raw_values": [ 2.404960420525000142077942655305378139019012451171875 ], "min_result": [ "2.22" ], "max_result": [ "3" ], "test_run_times": [ 715.259999999999990905052982270717620849609375 ] } } }, "929074aab4582ef1068030efa348a3181d819818": { "identifier": "pts\/pytorch-1.0.0", "title": "PyTorch", "app_version": "2.1", "arguments": "cpu 512 efficientnet_v2_l", "description": "Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_l", "scale": "batches\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 2.439999999999999946709294817992486059665679931640625, "raw_values": [ 2.43917424931759985184953620773740112781524658203125 ], "min_result": [ "2.24" ], "max_result": [ "3.22" ], "test_run_times": [ 699.3400000000000318323145620524883270263671875 ] }, "b": { "value": 2.439999999999999946709294817992486059665679931640625, "raw_values": [ 2.43528037864219992769676537136547267436981201171875 ], "min_result": [ "2.27" ], "max_result": [ "3.04" ], "test_run_times": [ 707 ] }, "c": { "value": 2.4199999999999999289457264239899814128875732421875, "raw_values": [ 2.42275699402699995488319473224692046642303466796875 ], "min_result": [ "2.18" ], "max_result": [ "3" ], "test_run_times": [ 710.529999999999972715158946812152862548828125 ] } } }, "5c0ab769b00e582c1def688c1d7584f0d38e215e": { "identifier": "pts\/pytorch-1.0.0", "title": "PyTorch", "app_version": "2.1", "arguments": "cpu 256 efficientnet_v2_l", "description": "Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_l", "scale": "batches\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 2.4199999999999999289457264239899814128875732421875, "raw_values": [ 2.423475008473200187353313594940118491649627685546875 ], "min_result": [ "2.23" ], "max_result": [ "2.98" ], "test_run_times": [ 703.779999999999972715158946812152862548828125 ] }, "b": { "value": 2.410000000000000142108547152020037174224853515625, "raw_values": [ 2.407806415199199800980522923055104911327362060546875 ], "min_result": [ "1.7" ], "max_result": [ "2.99" ], "test_run_times": [ 711.6699999999999590727384202182292938232421875 ] }, "c": { "value": 2.410000000000000142108547152020037174224853515625, "raw_values": [ 2.4142368147418000745574317988939583301544189453125 ], "min_result": [ "2.23" ], "max_result": [ "3.06" ], "test_run_times": [ 711.2100000000000363797880709171295166015625 ] } } } } }