Intel Xeon Platinum 8490H testing with a Quanta Cloud S6Q-MB-MPS (3A10.uh BIOS) and ASPEED on Ubuntu 22.04 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 2307296-NE-8490H1S1663
8490h 1s
Intel Xeon Platinum 8490H testing with a Quanta Cloud S6Q-MB-MPS (3A10.uh BIOS) and ASPEED on Ubuntu 22.04 via the Phoronix Test Suite.
,,"a","b","c","d","e"
Processor,,Intel Xeon Platinum 8490H @ 3.50GHz (60 Cores / 120 Threads),Intel Xeon Platinum 8490H @ 3.50GHz (60 Cores / 120 Threads),Intel Xeon Platinum 8490H @ 3.50GHz (60 Cores / 120 Threads),Intel Xeon Platinum 8490H @ 3.50GHz (60 Cores / 120 Threads),Intel Xeon Platinum 8490H @ 3.50GHz (60 Cores / 120 Threads)
Motherboard,,Quanta Cloud S6Q-MB-MPS (3A10.uh BIOS),Quanta Cloud S6Q-MB-MPS (3A10.uh BIOS),Quanta Cloud S6Q-MB-MPS (3A10.uh BIOS),Quanta Cloud S6Q-MB-MPS (3A10.uh BIOS),Quanta Cloud S6Q-MB-MPS (3A10.uh BIOS)
Chipset,,Intel Device 1bce,Intel Device 1bce,Intel Device 1bce,Intel Device 1bce,Intel Device 1bce
Memory,,512GB,512GB,512GB,512GB,512GB
Disk,,3 x 3841GB Micron_9300_MTFDHAL3T8TDP,3 x 3841GB Micron_9300_MTFDHAL3T8TDP,3 x 3841GB Micron_9300_MTFDHAL3T8TDP,3 x 3841GB Micron_9300_MTFDHAL3T8TDP,3 x 3841GB Micron_9300_MTFDHAL3T8TDP
Graphics,,ASPEED,ASPEED,ASPEED,ASPEED,ASPEED
Network,,4 x Intel E810-C for QSFP,4 x Intel E810-C for QSFP,4 x Intel E810-C for QSFP,4 x Intel E810-C for QSFP,4 x Intel E810-C for QSFP
OS,,Ubuntu 22.04,Ubuntu 22.04,Ubuntu 22.04,Ubuntu 22.04,Ubuntu 22.04
Kernel,,5.15.0-47-generic (x86_64),5.15.0-47-generic (x86_64),5.15.0-47-generic (x86_64),5.15.0-47-generic (x86_64),5.15.0-47-generic (x86_64)
Desktop,,GNOME Shell 42.4,GNOME Shell 42.4,GNOME Shell 42.4,GNOME Shell 42.4,GNOME Shell 42.4
Display Server,,X Server 1.21.1.3,X Server 1.21.1.3,X Server 1.21.1.3,X Server 1.21.1.3,X Server 1.21.1.3
Vulkan,,1.2.204,1.2.204,1.2.204,1.2.204,1.2.204
Compiler,,GCC 11.2.0,GCC 11.2.0,GCC 11.2.0,GCC 11.2.0,GCC 11.2.0
File-System,,ext4,ext4,ext4,ext4,ext4
Screen Resolution,,1024x768,1024x768,1024x768,1024x768,1024x768
,,"a","b","c","d","e"
"Apache Cassandra - Test: Writes (Op/s)",HIB,134694,134932,121708,140934,137849
"Blender - Blend File: BMW27 - Compute: CPU-Only (sec)",LIB,25.74,25.63,25.69,25.75,25.71
"Blender - Blend File: Classroom - Compute: CPU-Only (sec)",LIB,68.93,69.16,70.03,69.16,69.25
"Blender - Blend File: Fishy Cat - Compute: CPU-Only (sec)",LIB,35.75,35.26,35.03,35.16,35.5
"Blender - Blend File: Barbershop - Compute: CPU-Only (sec)",LIB,272.91,273.14,272.64,272.41,272.44
"Blender - Blend File: Pabellon Barcelona - Compute: CPU-Only (sec)",LIB,88.73,88.5,88.83,88.18,88.09
"BRL-CAD - VGR Performance Metric (VGR Performance Metric)",HIB,825917,823602,820410,812806,822977
"Crypto++ - Test: All Algorithms (MiB/s)",HIB,1663.920955,,,,
"Crypto++ - Test: Keyed Algorithms (MiB/s)",HIB,595.365201,,,,
"Crypto++ - Test: Unkeyed Algorithms (MiB/s)",HIB,452.343734,,,,
"Dragonflydb - Clients Per Thread: 10 - Set To Get Ratio: 1:5 (Ops/sec)",HIB,14247982.45,14476834.10,14235868.61,14750102.52,14392511.79
"Dragonflydb - Clients Per Thread: 20 - Set To Get Ratio: 1:5 ()",,,,,,
"Dragonflydb - Clients Per Thread: 10 - Set To Get Ratio: 1:10 (Ops/sec)",HIB,14662941.42,14292262.52,14204640.25,14205235.71,14390358.99
"Dragonflydb - Clients Per Thread: 20 - Set To Get Ratio: 1:10 ()",,,,,,
"Dragonflydb - Clients Per Thread: 10 - Set To Get Ratio: 1:100 (Ops/sec)",HIB,14338166.15,14432034.21,14307949.03,14571297.77,14492478.36
"Dragonflydb - Clients Per Thread: 20 - Set To Get Ratio: 1:100 ()",,,,,,
"Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,56.1193,57.7892,57.9253,58.2508,57.2121
"Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,534.5014,517.1634,517.8375,511.4186,520.2239
"Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream (items/sec)",HIB,35.4911,35.3613,35.6223,35.5925,35.0944
"Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream (ms/batch)",LIB,28.1702,28.2735,28.0659,28.0901,28.488
"Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,1905.9808,1891.0565,1891.327,1890.5705,1905.1491
"Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,15.703,15.8316,15.8257,15.838,15.7168
"Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream (items/sec)",HIB,208.9561,209.1021,208.4283,209.6555,205.2428
"Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,4.7834,4.7802,4.7956,4.7675,4.8699
"Neural Magic DeepSparse - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,648.3175,644.0495,649.2977,648.3864,633.4455
"Neural Magic DeepSparse - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,46.2475,46.5440,46.1781,46.2436,47.3169
"Neural Magic DeepSparse - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-Stream (items/sec)",HIB,180.141,179.6816,178.7189,180.4386,177.0105
"Neural Magic DeepSparse - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-Stream (ms/batch)",LIB,5.5454,5.5592,5.5886,5.5358,5.642
"Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,192.5033,191.9935,191.6104,190.8181,191.4971
"Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,155.8013,156.2109,156.4615,157.1756,156.5562
"Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream (items/sec)",HIB,86.93,87.1582,86.786,87.3401,85.159
"Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,11.4928,11.4653,11.5118,11.4418,11.7345
"Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,780.4996,782.0400,780.5294,780.3141,783.198
"Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,38.4127,38.3371,38.4104,38.422,38.2796
"Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream (items/sec)",HIB,272.7991,270.2295,268.146,270.3481,266.3444
"Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream (ms/batch)",LIB,3.6608,3.6961,3.7248,3.6938,3.7499
"Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,5672.7993,5678.5152,5677.035,5665.4699,5679.7805
"Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,5.268,5.2627,5.2632,5.2743,5.2615
"Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream (items/sec)",HIB,759.4847,746.7073,758.3359,759.4253,709.1549
"Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,1.3144,1.3371,1.3167,1.3146,1.4076
"Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,346.9388,347.6552,348.0117,346.5606,347.7604
"Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,86.4291,86.2508,86.1641,86.5253,86.1663
"Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream (items/sec)",HIB,184.3867,185.7453,184.1966,184.9319,181.1993
"Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream (ms/batch)",LIB,5.4182,5.3786,5.4239,5.402,5.5134
"Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,62.575,61.5956,61.4143,62.1748,61.0696
"Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,479.125,486.1851,488.2455,482.4354,488.2347
"Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream (items/sec)",HIB,21.1647,21.1611,21.0871,21.0613,20.736
"Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream (ms/batch)",LIB,47.2403,47.2504,47.4151,47.4732,48.2186
"Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,780.3982,781.3024,780.3312,778.7629,782.5439
"Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,38.3944,38.3727,38.4054,38.4911,38.312
"Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream (items/sec)",HIB,267.9943,270.8536,271.6229,271.7797,264.3062
"Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream (ms/batch)",LIB,3.7271,3.6871,3.677,3.6743,3.7788
"Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,348.5304,348.7206,347.5917,347.5128,348.1392
"Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,86.0393,85.9332,86.2759,86.2939,86.1261
"Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream (items/sec)",HIB,183.6332,183.9268,183.819,184.047,181.1289
"Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,5.4431,5.4340,5.4372,5.4307,5.5183
"Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,475.7181,487.4646,487.1764,485.7154,488.2494
"Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,63.0065,61.5102,61.546,61.6935,61.4194
"Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream (items/sec)",HIB,121.8864,123.6781,124.6224,123.4543,120.4409
"Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream (ms/batch)",LIB,8.1989,8.0804,8.019,8.0945,8.2973
"Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,75.6034,75.8818,75.613,75.5523,75.7124
"Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,396.734,395.2099,396.6828,396.9936,396.1651
"Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream (items/sec)",HIB,39.9427,39.9583,39.9318,39.8698,39.7368
"Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream (ms/batch)",LIB,25.0015,24.9917,25.0071,25.0454,25.132
"Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,868.4975,869.4702,870.7567,871.2052,870.6664
"Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,34.4904,34.4634,34.4193,34.4017,34.4226
"Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream (items/sec)",HIB,117.2266,114.6147,116.3483,117.2235,113.2419
"Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,8.5248,8.7193,8.5903,8.5255,8.8247
"Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,218.1115,224.1648,220.4888,229.4472,217.7184
"Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,137.5084,133.779,135.9984,130.5153,137.7614
"Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream (items/sec)",HIB,61.6472,61.8913,61.6088,61.8283,61.8458
"Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,16.2145,16.1504,16.2241,16.1666,16.1614
"Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,57.076,57.5702,57.9918,58.2129,57.5236
"Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,525.1385,520.8695,516.0418,514.8982,519.2686
"Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream (items/sec)",HIB,35.764,35.7199,35.8555,35.7569,35.8816
"Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,27.9552,27.9897,27.884,27.9607,27.8637
"Redis 7.0.12 + memtier_benchmark - Protocol: Redis - Clients: 50 - Set To Get Ratio: 1:1 (Ops/sec)",HIB,2462536.66,2470315.91,2377430.14,2383147.07,2414601.07
"Redis 7.0.12 + memtier_benchmark - Protocol: Redis - Clients: 50 - Set To Get Ratio: 1:5 (Ops/sec)",HIB,2613416.65,2546591.86,2516320.69,2452685.99,2444450.11
"Redis 7.0.12 + memtier_benchmark - Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:1 (Ops/sec)",HIB,2503378.33,2630752.03,2460387.75,2493158.97,2474272.95
"Redis 7.0.12 + memtier_benchmark - Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:5 (Ops/sec)",HIB,2627400.98,2710324.97,2554516.65,2549066.22,2541163.68
"Redis 7.0.12 + memtier_benchmark - Protocol: Redis - Clients: 50 - Set To Get Ratio: 1:10 (Ops/sec)",HIB,2646929.55,2544897.58,2508415.59,2487096.51,2540848.35
"Redis 7.0.12 + memtier_benchmark - Protocol: Redis - Clients: 500 - Set To Get Ratio: 1:1 ()",,,,,,
"Redis 7.0.12 + memtier_benchmark - Protocol: Redis - Clients: 500 - Set To Get Ratio: 1:5 ()",,,,,,
"Redis 7.0.12 + memtier_benchmark - Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:10 (Ops/sec)",HIB,2929613.17,2583878.29,2611551.59,2593991.1,2755166.14
"Redis 7.0.12 + memtier_benchmark - Protocol: Redis - Clients: 500 - Set To Get Ratio: 1:10 ()",,,,,,