pytorch emerald rapids

2 x INTEL XEON PLATINUM 8592+ testing with a Quanta Cloud QuantaGrid D54Q-2U S6Q-MB-MPS (3B05.TEL4P1 BIOS) and ASPEED on Ubuntu 23.10 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 2403262-NE-PYTORCHEM92
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
March 26
  17 Minutes
b
March 26
  17 Minutes
Invert Hiding All Results Option
  17 Minutes
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 emerald rapids", "last_modified": "2024-03-27 00:00:28", "description": "2 x INTEL XEON PLATINUM 8592+ testing with a Quanta Cloud QuantaGrid D54Q-2U S6Q-MB-MPS (3B05.TEL4P1 BIOS) and ASPEED on Ubuntu 23.10 via the Phoronix Test Suite.", "systems": { "a": { "identifier": "a", "hardware": { "Processor": "2 x INTEL XEON PLATINUM 8592+ @ 3.90GHz (128 Cores \/ 256 Threads)", "Motherboard": "Quanta Cloud QuantaGrid D54Q-2U S6Q-MB-MPS (3B05.TEL4P1 BIOS)", "Chipset": "Intel Device 1bce", "Memory": "1008GB", "Disk": "3201GB Micron_7450_MTFDKCC3T2TFS", "Graphics": "ASPEED", "Network": "2 x Intel X710 for 10GBASE-T" }, "software": { "OS": "Ubuntu 23.10", "Kernel": "6.6.0-rc5-phx-patched (x86_64)", "Desktop": "GNOME Shell 45.0", "Display Server": "X Server 1.21.1.7", "Compiler": "GCC 13.2.0", "File-System": "ext4", "Screen Resolution": "1920x1200" }, "user": "phoronix", "timestamp": "2024-03-26 22:49:07", "client_version": "10.8.4", "data": { "cpu-scaling-governor": "intel_pstate performance (EPP: performance)", "cpu-microcode": "0x21000161", "kernel-extra-details": "Transparent Huge Pages: madvise", "python": "Python 3.11.6", "security": "gather_data_sampling: Not affected + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + retbleed: Not affected + 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 \/ Automatic IBRS IBPB: conditional RSB filling PBRSB-eIBRS: SW sequence + srbds: Not affected + tsx_async_abort: Not affected" } }, "b": { "identifier": "b", "hardware": { "Processor": "2 x INTEL XEON PLATINUM 8592+ @ 3.90GHz (128 Cores \/ 256 Threads)", "Motherboard": "Quanta Cloud QuantaGrid D54Q-2U S6Q-MB-MPS (3B05.TEL4P1 BIOS)", "Chipset": "Intel Device 1bce", "Memory": "1008GB", "Disk": "3201GB Micron_7450_MTFDKCC3T2TFS", "Graphics": "ASPEED", "Network": "2 x Intel X710 for 10GBASE-T" }, "software": { "OS": "Ubuntu 23.10", "Kernel": "6.6.0-rc5-phx-patched (x86_64)", "Desktop": "GNOME Shell 45.0", "Display Server": "X Server 1.21.1.7", "Compiler": "GCC 13.2.0", "File-System": "ext4", "Screen Resolution": "1920x1200" }, "user": "phoronix", "timestamp": "2024-03-26 23:38:32", "client_version": "10.8.4", "data": { "cpu-scaling-governor": "intel_pstate performance (EPP: performance)", "cpu-microcode": "0x21000161", "kernel-extra-details": "Transparent Huge Pages: madvise", "python": "Python 3.11.6", "security": "gather_data_sampling: Not affected + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + retbleed: Not affected + 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 \/ Automatic IBRS IBPB: conditional RSB filling PBRSB-eIBRS: SW sequence + srbds: Not affected + tsx_async_abort: Not affected" } } }, "results": { "d0adcab531e05f3db8c970385d100006ac333e7f": { "identifier": "pts\/pytorch-1.1.0", "title": "PyTorch", "app_version": "2.2.1", "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": 49.74000000000000198951966012828052043914794921875, "raw_values": [ 49.7394658900330028927783132530748844146728515625 ], "min_result": [ "20.74" ], "max_result": [ "51.83" ], "test_run_times": [ 25.239999999999998436805981327779591083526611328125 ] }, "b": { "value": 48.46000000000000085265128291212022304534912109375, "raw_values": [ 48.4636199040050001940471702255308628082275390625 ], "min_result": [ "22.49" ], "max_result": [ "51.23" ], "test_run_times": [ 25.75 ] } } }, "2bc391ee0b594811f657300072fb2a46f2a71e6e": { "identifier": "pts\/pytorch-1.1.0", "title": "PyTorch", "app_version": "2.2.1", "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": 19.1700000000000017053025658242404460906982421875, "raw_values": [ 19.17392875287799824945977889001369476318359375 ], "min_result": [ "11.32" ], "max_result": [ "19.93" ], "test_run_times": [ 61.590000000000003410605131648480892181396484375 ] }, "b": { "value": 19.530000000000001136868377216160297393798828125, "raw_values": [ 19.534515593087999008048427640460431575775146484375 ], "min_result": [ "6.21" ], "max_result": [ "20.18" ], "test_run_times": [ 60.13000000000000255795384873636066913604736328125 ] } } }, "1817b719ec4714ac77d1b79b309bdc2361beb3cf": { "identifier": "pts\/pytorch-1.1.0", "title": "PyTorch", "app_version": "2.2.1", "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": 44.47999999999999687361196265555918216705322265625, "raw_values": [ 44.4754265108820021623614593409001827239990234375 ], "min_result": [ "19.94" ], "max_result": [ "45.71" ], "test_run_times": [ 51.17999999999999971578290569595992565155029296875 ] }, "b": { "value": 43.2999999999999971578290569595992565155029296875, "raw_values": [ 43.29668196765499743605687399394810199737548828125 ], "min_result": [ "18.44" ], "max_result": [ "45.93" ], "test_run_times": [ 51.27000000000000312638803734444081783294677734375 ] } } }, "5bb5428bac71de14e9e94ef4b2c074689a36c369": { "identifier": "pts\/pytorch-1.1.0", "title": "PyTorch", "app_version": "2.2.1", "arguments": "cpu 32 resnet50", "description": "Device: CPU - Batch Size: 32 - Model: ResNet-50", "scale": "batches\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 43.13000000000000255795384873636066913604736328125, "raw_values": [ 43.128179709866998337020049802958965301513671875 ], "min_result": [ "21.59" ], "max_result": [ "44.07" ], "test_run_times": [ 51.38000000000000255795384873636066913604736328125 ] }, "b": { "value": 44.86999999999999744204615126363933086395263671875, "raw_values": [ 44.8741124140999971814380842261016368865966796875 ], "min_result": [ "37.26" ], "max_result": [ "46.02" ], "test_run_times": [ 49.72999999999999687361196265555918216705322265625 ] } } }, "e53ed50df6ab47811484cbc3c159c79dc2966b78": { "identifier": "pts\/pytorch-1.1.0", "title": "PyTorch", "app_version": "2.2.1", "arguments": "cpu 64 resnet50", "description": "Device: CPU - Batch Size: 64 - Model: ResNet-50", "scale": "batches\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 41.6099999999999994315658113919198513031005859375, "raw_values": [ 41.60994114396199705652179545722901821136474609375 ], "min_result": [ "21.64" ], "max_result": [ "43.52" ], "test_run_times": [ 53.27000000000000312638803734444081783294677734375 ] }, "b": { "value": 42.97999999999999687361196265555918216705322265625, "raw_values": [ 42.9761269396399967490651761181652545928955078125 ], "min_result": [ "18.82" ], "max_result": [ "44.16" ], "test_run_times": [ 50.85000000000000142108547152020037174224853515625 ] } } }, "75c834417bf0059ea75b1d19b03766a190e2dc13": { "identifier": "pts\/pytorch-1.1.0", "title": "PyTorch", "app_version": "2.2.1", "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": 16.980000000000000426325641456060111522674560546875, "raw_values": [ 16.976312762119999177912177401594817638397216796875 ], "min_result": [ "8.88" ], "max_result": [ "17.73" ], "test_run_times": [ 128.840000000000003410605131648480892181396484375 ] }, "b": { "value": 17.690000000000001278976924368180334568023681640625, "raw_values": [ 17.691766729263999735621837317012250423431396484375 ], "min_result": [ "12.47" ], "max_result": [ "18.03" ], "test_run_times": [ 122.8799999999999954525264911353588104248046875 ] } } }, "c0ebee3de3af3f6bb30ca7bfd912294570db05fa": { "identifier": "pts\/pytorch-1.1.0", "title": "PyTorch", "app_version": "2.2.1", "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": 43.4200000000000017053025658242404460906982421875, "raw_values": [ 43.42414295805399859773388016037642955780029296875 ], "min_result": [ "40.39" ], "max_result": [ "44.48" ], "test_run_times": [ 51.659999999999996589394868351519107818603515625 ] }, "b": { "value": 44.75, "raw_values": [ 44.75164435151000219548222958110272884368896484375 ], "min_result": [ "15.82" ], "max_result": [ "46.04" ], "test_run_times": [ 51.3299999999999982946974341757595539093017578125 ] } } }, "56fa1ea70f6128a460fab8eadbc9d03e19a68f1f": { "identifier": "pts\/pytorch-1.1.0", "title": "PyTorch", "app_version": "2.2.1", "arguments": "cpu 32 resnet152", "description": "Device: CPU - Batch Size: 32 - Model: ResNet-152", "scale": "batches\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 17.309999999999998721023075631819665431976318359375, "raw_values": [ 17.30545032776900171711531584151089191436767578125 ], "min_result": [ "7.61" ], "max_result": [ "17.66" ], "test_run_times": [ 124.5499999999999971578290569595992565155029296875 ] }, "b": { "value": 17.519999999999999573674358543939888477325439453125, "raw_values": [ 17.5192538923379999005192075856029987335205078125 ], "min_result": [ "8.47" ], "max_result": [ "17.81" ], "test_run_times": [ 125.849999999999994315658113919198513031005859375 ] } } }, "0d499a4b318c513750ceefe20d7b87453d9d336f": { "identifier": "pts\/pytorch-1.1.0", "title": "PyTorch", "app_version": "2.2.1", "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": 43.24000000000000198951966012828052043914794921875, "raw_values": [ 43.23526646884899804490487440489232540130615234375 ], "min_result": [ "40.07" ], "max_result": [ "44.05" ], "test_run_times": [ 51.00999999999999801048033987171947956085205078125 ] }, "b": { "value": 44.75999999999999801048033987171947956085205078125, "raw_values": [ 44.759806580542999654426239430904388427734375 ], "min_result": [ "21.38" ], "max_result": [ "45.97" ], "test_run_times": [ 50.6700000000000017053025658242404460906982421875 ] } } }, "16296c993bdfa97a6848d705ddcb761a04402680": { "identifier": "pts\/pytorch-1.1.0", "title": "PyTorch", "app_version": "2.2.1", "arguments": "cpu 64 resnet152", "description": "Device: CPU - Batch Size: 64 - Model: ResNet-152", "scale": "batches\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 17, "raw_values": [ 17.001753218516999055509586469270288944244384765625 ], "min_result": [ "6.37" ], "max_result": [ "17.5" ], "test_run_times": [ 126.43999999999999772626324556767940521240234375 ] }, "b": { "value": 17.589999999999999857891452847979962825775146484375, "raw_values": [ 17.59176805399300036469867336563766002655029296875 ], "min_result": [ "7.39" ], "max_result": [ "17.91" ], "test_run_times": [ 123.3900000000000005684341886080801486968994140625 ] } } }, "e3737a48218cdf9ba397f2538c1ec5ba8ba6bf81": { "identifier": "pts\/pytorch-1.1.0", "title": "PyTorch", "app_version": "2.2.1", "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": 17.3599999999999994315658113919198513031005859375, "raw_values": [ 17.358468956435000762894560466520488262176513671875 ], "min_result": [ "13.87" ], "max_result": [ "17.68" ], "test_run_times": [ 125.18999999999999772626324556767940521240234375 ] }, "b": { "value": 17.379999999999999005240169935859739780426025390625, "raw_values": [ 17.37968127335999923843701253645122051239013671875 ], "min_result": [ "9.17" ], "max_result": [ "17.74" ], "test_run_times": [ 125.5799999999999982946974341757595539093017578125 ] } } }, "1e1b7ee4b6d0a9be046b48132617a756ba63cb19": { "identifier": "pts\/pytorch-1.1.0", "title": "PyTorch", "app_version": "2.2.1", "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": 17.910000000000000142108547152020037174224853515625, "raw_values": [ 17.913237299784999123630768735893070697784423828125 ], "min_result": [ "6.7" ], "max_result": [ "18.43" ], "test_run_times": [ 120.3599999999999994315658113919198513031005859375 ] }, "b": { "value": 17.3900000000000005684341886080801486968994140625, "raw_values": [ 17.3880113020640010290662758052349090576171875 ], "min_result": [ "8.92" ], "max_result": [ "17.83" ], "test_run_times": [ 124.650000000000005684341886080801486968994140625 ] } } } } }