new-tests

Tests for a future article. AMD EPYC 8324P 32-Core testing with a AMD Cinnabar (RCB1009C BIOS) and ASPEED on Ubuntu 23.10 via the Phoronix Test Suite.

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Zen 1 - EPYC 7601
January 07
  46 Minutes
b
January 10
  12 Minutes
c
January 10
  12 Minutes
32
January 11
  2 Hours, 56 Minutes
32 z
January 11
  2 Hours, 56 Minutes
32 c
January 11
  3 Hours, 14 Minutes
32 d
January 11
  2 Hours, 55 Minutes
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new-tests Tests for a future article. AMD EPYC 8324P 32-Core testing with a AMD Cinnabar (RCB1009C BIOS) and ASPEED on Ubuntu 23.10 via the Phoronix Test Suite. ,,"Zen 1 - EPYC 7601","b","c","32","32 z","32 c","32 d" Processor,,AMD EPYC 7601 32-Core @ 2.20GHz (32 Cores / 64 Threads),AMD EPYC 8534PN 64-Core @ 2.00GHz (64 Cores / 128 Threads),AMD EPYC 8534PN 64-Core @ 2.00GHz (64 Cores / 128 Threads),AMD EPYC 8534PN 32-Core @ 2.05GHz (32 Cores / 64 Threads),AMD EPYC 8534PN 32-Core @ 2.05GHz (32 Cores / 64 Threads),AMD EPYC 8324P 32-Core @ 2.65GHz (32 Cores / 64 Threads),AMD EPYC 8324P 32-Core @ 2.65GHz (32 Cores / 64 Threads) Motherboard,,TYAN B8026T70AE24HR (V1.02.B10 BIOS),AMD Cinnabar (RCB1009C BIOS),AMD Cinnabar (RCB1009C BIOS),AMD Cinnabar (RCB1009C BIOS),AMD Cinnabar (RCB1009C BIOS),AMD Cinnabar (RCB1009C BIOS),AMD Cinnabar (RCB1009C BIOS) Chipset,,AMD 17h,AMD Device 14a4,AMD Device 14a4,AMD Device 14a4,AMD Device 14a4,AMD Device 14a4,AMD Device 14a4 Memory,,128GB,6 x 32 GB DRAM-4800MT/s Samsung M321R4GA0BB0-CQKMG,6 x 32 GB DRAM-4800MT/s Samsung M321R4GA0BB0-CQKMG,6 x 32 GB DRAM-4800MT/s Samsung M321R4GA0BB0-CQKMG,6 x 32 GB DRAM-4800MT/s Samsung M321R4GA0BB0-CQKMG,6 x 32 GB DRAM-4800MT/s Samsung M321R4GA0BB0-CQKMG,6 x 32 GB DRAM-4800MT/s Samsung M321R4GA0BB0-CQKMG Disk,,280GB INTEL SSDPE21D280GA + 1000GB INTEL SSDPE2KX010T8,1000GB INTEL SSDPE2KX010T8,1000GB INTEL SSDPE2KX010T8,1000GB INTEL SSDPE2KX010T8,1000GB INTEL SSDPE2KX010T8,1000GB INTEL SSDPE2KX010T8,1000GB INTEL SSDPE2KX010T8 Graphics,,llvmpipe,llvmpipe,llvmpipe,ASPEED,ASPEED,ASPEED,ASPEED Monitor,,VE228,,,,,, Network,,2 x Broadcom NetXtreme BCM5720 PCIe,2 x Broadcom NetXtreme BCM5720 PCIe,2 x Broadcom NetXtreme BCM5720 PCIe,2 x Broadcom NetXtreme BCM5720 PCIe,2 x Broadcom NetXtreme BCM5720 PCIe,2 x Broadcom NetXtreme BCM5720 PCIe,2 x Broadcom NetXtreme BCM5720 PCIe OS,,Ubuntu 23.10,Ubuntu 23.10,Ubuntu 23.10,Ubuntu 23.10,Ubuntu 23.10,Ubuntu 23.10,Ubuntu 23.10 Kernel,,6.6.9-060609-generic (x86_64),6.6.9-060609-generic (x86_64),6.6.9-060609-generic (x86_64),6.6.9-060609-generic (x86_64),6.6.9-060609-generic (x86_64),6.6.9-060609-generic (x86_64),6.6.9-060609-generic (x86_64) Desktop,,GNOME Shell 45.0,GNOME Shell 45.0,GNOME Shell 45.0,GNOME Shell 45.0,GNOME Shell 45.0,GNOME Shell 45.0,GNOME Shell 45.0 Display Server,,X Server 1.21.1.7,X Server 1.21.1.7,X Server 1.21.1.7,X Server 1.21.1.7,X Server 1.21.1.7,X Server 1.21.1.7,X Server 1.21.1.7 OpenGL,,4.5 Mesa 23.2.1-1ubuntu3.1 (LLVM 15.0.7 256 bits),,,,,, Compiler,,GCC 13.2.0,GCC 13.2.0,GCC 13.2.0,GCC 13.2.0,GCC 13.2.0,GCC 13.2.0,GCC 13.2.0 File-System,,ext4,ext4,ext4,ext4,ext4,ext4,ext4 Screen Resolution,,1920x1080,1920x1200,1920x1200,1920x1200,1920x1200,1920x1200,1920x1200 ,,"Zen 1 - EPYC 7601","b","c","32","32 z","32 c","32 d" "CPU Power Consumption Monitor - Phoronix Test Suite System Monitoring (Watts)",,585.92,,,,,, "PyTorch - Device: CPU - Batch Size: 1 - Model: ResNet-50 (batches/sec)",HIB,,,,52.44,52.78,53.00,53.30 "PyTorch - Device: CPU - Batch Size: 1 - Model: ResNet-152 (batches/sec)",HIB,,,,19.04,18.92,18.86,18.86 "PyTorch - Device: CPU - Batch Size: 16 - Model: ResNet-50 (batches/sec)",HIB,,,,40.19,39.96,40.32,40.31 "PyTorch - Device: CPU - Batch Size: 16 - Model: ResNet-152 (batches/sec)",HIB,,,,15.61,15.51,15.32,15.35 "PyTorch - Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l (batches/sec)",HIB,,,,9.85,9.82,10.04,10.21 "PyTorch - Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l (batches/sec)",HIB,,,,7.17,7.11,7.18,7.15 "Quicksilver - Input: CORAL2 P1 (Figure Of Merit/Watt)",HIB,22248.554,,,,,, "Quicksilver - Input: CORAL2 P2 (Figure Of Merit/Watt)",HIB,27116.867,,,,,, "Quicksilver - Input: CTS2 (Figure Of Merit/Watt)",HIB,18307.655,,,,,, "Quicksilver - Input: CORAL2 P1 (Figure Of Merit)",HIB,12996667,21180000,21250000,18790000,18760000,1040000,18840000 "Quicksilver - Input: CORAL2 P2 (Figure Of Merit)",HIB,15013333,16140000,16150000,15350000,15230000,15180000,15100000 "Quicksilver - Input: CTS2 (Figure Of Merit)",HIB,11426667,16270000,16260000,14320000,14290000,14430000,14280000 "FFmpeg - Encoder: libx265 - Scenario: Live (FPS)",HIB,,,,109.84,110.37,110.02,110.29 "FFmpeg - Encoder: libx265 - Scenario: Upload (FPS)",HIB,,,,22.28,22.20,22.21,22.22 "FFmpeg - Encoder: libx265 - Scenario: Platform (FPS)",HIB,,,,45.13,45.05,45.13,44.97 "FFmpeg - Encoder: libx265 - Scenario: Video On Demand (FPS)",HIB,,,,45.18,45.08,44.95,45.10 "OpenVINO - Model: Face Detection FP16 - Device: CPU (FPS)",HIB,,,,17.17,17.18,16.51,16.54 "OpenVINO - Model: Person Detection FP16 - Device: CPU (FPS)",HIB,,,,151.45,150.06,150.07,151.25 "OpenVINO - Model: Person Detection FP32 - Device: CPU (FPS)",HIB,,,,150.8,150.37,150.84,150.25 "OpenVINO - Model: Vehicle Detection FP16 - Device: CPU (FPS)",HIB,,,,1190.42,1197.46,1166.56,1166.83 "OpenVINO - Model: Face Detection FP16-INT8 - Device: CPU (FPS)",HIB,,,,32.82,32.81,31.22,31.2 "OpenVINO - Model: Face Detection Retail FP16 - Device: CPU (FPS)",HIB,,,,3921.5,3924.86,3877.91,3869.7 "OpenVINO - Model: Road Segmentation ADAS FP16 - Device: CPU (FPS)",HIB,,,,576.18,579.41,554.68,553.65 "OpenVINO - Model: Vehicle Detection FP16-INT8 - Device: CPU (FPS)",HIB,,,,1960.18,1964.99,1860.99,1862.24 "OpenVINO - Model: Weld Porosity Detection FP16 - Device: CPU (FPS)",HIB,,,,1704.26,1704.02,1627.93,1628.91 "OpenVINO - Model: Face Detection Retail FP16-INT8 - Device: CPU (FPS)",HIB,,,,5747.65,5751.58,5416.31,5423.13 "OpenVINO - Model: Road Segmentation ADAS FP16-INT8 - Device: CPU (FPS)",HIB,,,,666.22,666.3,632.92,634.5 "OpenVINO - Model: Machine Translation EN To DE FP16 - Device: CPU (FPS)",HIB,,,,199.9,201.15,194.21,195.05 "OpenVINO - Model: Weld Porosity Detection FP16-INT8 - Device: CPU (FPS)",HIB,,,,3300.99,3299.93,3099.2,3100.95 "OpenVINO - Model: Person Vehicle Bike Detection FP16 - Device: CPU (FPS)",HIB,,,,1741.57,1735.64,1694.01,1696.5 "OpenVINO - Model: Handwritten English Recognition FP16 - Device: CPU (FPS)",HIB,,,,898.6,896.69,853.38,848.62 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU (FPS)",HIB,,,,40123.62,40101.8,39562.87,39843.05 "OpenVINO - Model: Handwritten English Recognition FP16-INT8 - Device: CPU (FPS)",HIB,,,,745,730.82,692.02,690.24 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU (FPS)",HIB,,,,52441.94,52475.39,52382.31,52344.6 "Embree - Binary: Pathtracer - Model: Crown (FPS)",HIB,,,,36.9584,37.2545,35.9147,36.2812 "Embree - Binary: Pathtracer ISPC - Model: Crown (FPS)",HIB,,,,37.2967,37.6791,36.9967,36.9369 "Embree - Binary: Pathtracer - Model: Asian Dragon (FPS)",HIB,,,,41.5958,41.8198,41.5696,41.557 "Embree - Binary: Pathtracer - Model: Asian Dragon Obj (FPS)",HIB,,,,37.284,36.8586,37.4405,37.4056 "Embree - Binary: Pathtracer ISPC - Model: Asian Dragon (FPS)",HIB,,,,45.9374,46.3088,45.4648,45.6482 "Embree - Binary: Pathtracer ISPC - Model: Asian Dragon Obj (FPS)",HIB,,,,38.9378,39.107,39.0046,39.1421 "SVT-AV1 - Encoder Mode: Preset 4 - Input: Bosphorus 4K (FPS)",HIB,,,,5.801,5.899,5.829,5.977 "SVT-AV1 - Encoder Mode: Preset 8 - Input: Bosphorus 4K (FPS)",HIB,,,,48.451,58.715,47.253,58.642 "SVT-AV1 - Encoder Mode: Preset 12 - Input: Bosphorus 4K (FPS)",HIB,,,,186.625,185.562,180.955,186.368 "SVT-AV1 - Encoder Mode: Preset 13 - Input: Bosphorus 4K (FPS)",HIB,,,,185.665,184.981,183.899,184.099 "Xmrig - Variant: KawPow - Hash Count: 1M (H/s)",HIB,,,,18777.2,18961.3,18947.3,18901.1 "Xmrig - Variant: Monero - Hash Count: 1M (H/s)",HIB,,,,18845.5,18763.8,18897.5,18866.1 "Xmrig - Variant: Wownero - Hash Count: 1M (H/s)",HIB,,,,25814.4,25943.7,25385.9,25396.8 "Xmrig - Variant: GhostRider - Hash Count: 1M (H/s)",HIB,,,,4067.4,4038.6,4136.3,4095.7 "Xmrig - Variant: CryptoNight-Heavy - Hash Count: 1M (H/s)",HIB,,,,19004.5,18936.5,18783.9,18924 "Xmrig - Variant: CryptoNight-Femto UPX2 - Hash Count: 1M (H/s)",HIB,,,,18860.1,18909,18887.5,18818.6 "TensorFlow - Device: CPU - Batch Size: 1 - Model: VGG-16 (images/sec)",HIB,,,,9.73,9.75,9.77,9.75 "TensorFlow - Device: CPU - Batch Size: 1 - Model: AlexNet (images/sec)",HIB,,,,32.12,31.92,33.14,33.02 "TensorFlow - Device: CPU - Batch Size: 16 - Model: VGG-16 (images/sec)",HIB,,,,25.15,25.2,24.47,24.51 "TensorFlow - Device: CPU - Batch Size: 16 - Model: AlexNet (images/sec)",HIB,,,,272.93,274.97,274.97,276.19 "TensorFlow - Device: CPU - Batch Size: 1 - Model: GoogLeNet (images/sec)",HIB,,,,28.99,28.71,27.73,28.79 "TensorFlow - Device: CPU - Batch Size: 1 - Model: ResNet-50 (images/sec)",HIB,,,,8.74,8.77,8.61,8.59 "TensorFlow - Device: CPU - Batch Size: 16 - Model: GoogLeNet (images/sec)",HIB,,,,158.47,155.77,157.6,158.08 "TensorFlow - Device: CPU - Batch Size: 16 - Model: ResNet-50 (images/sec)",HIB,,,,51.34,51.57,51.56,51.49 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,,,,21.2933,21.2711,20.8729,21.0932 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,,,,836.4214,835.262,816.2785,815.9768 "Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,,,,266.8574,266.9761,266.8428,266.5343 "Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,,,,2208.1537,2199.4941,2195.9198,2189.0655 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,,,,123.0157,122.955,122.3307,121.8001 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,,,,26.0566,26.0768,25.8175,25.7874 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,,,,266.8799,267.8417,266.034,266.2776 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,,,,123.8817,123.785,123.1469,122.9312 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,,,,182.5085,182.7643,181.1043,181.1155 "Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,,,,40.1688,39.9467,38.7708,38.8343 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,,,,383.9746,385.6481,384.3164,381.7839 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,,,,21.2278,21.289,21.0419,21.0667 "CacheBench - Test: Read (MB/s)",HIB,,,,7616.087334,7616.334142,7615.948086,7615.833145 "CacheBench - Test: Write (MB/s)",HIB,,,,45646.091353,45646.816107,45645.091133,45643.038713 "CacheBench - Test: Read / Modify / Write (MB/s)",HIB,,,,87227.587713,87218.210974,87238.013197,87854.117672 "QuantLib - Configuration: Multi-Threaded (MFLOPS)",HIB,,,,107079.2,107381.6,98916.2,98618.7 "7-Zip Compression - Test: Compression Rating (MIPS)",HIB,,,,241545,242399,240287,241191 "7-Zip Compression - Test: Decompression Rating (MIPS)",HIB,,,,212209,211584,211815,211383 "RocksDB - Test: Random Read (Op/s)",HIB,,,,176770468,177167636,160665305,160707812 "RocksDB - Test: Update Random (Op/s)",HIB,,,,630575,636242,633688,630478 "RocksDB - Test: Read While Writing (Op/s)",HIB,,,,4284691,4364996,4419497,4244478 "RocksDB - Test: Read Random Write Random (Op/s)",HIB,,,,2373654,2361270,2327800,2351568 "Speedb - Test: Random Read (Op/s)",HIB,,,,179685954,179434924,163202721,163512432 "Speedb - Test: Update Random (Op/s)",HIB,,,,314123,314114,317758,313683 "Speedb - Test: Read While Writing (Op/s)",HIB,,,,7457600,7210235,7746346,7105602 "Speedb - Test: Read Random Write Random (Op/s)",HIB,,,,2231403,2259344,2229494,2215896 "Meta Performance Per Watts - Performance Per Watts (Performance/Watts)",HIB,13064001.6555,,,,,, "Llama.cpp - Model: llama-2-7b.Q4_0.gguf (Tokens/sec)",HIB,,,,29.75,29.9,29.74,29.85 "Llama.cpp - Model: llama-2-13b.Q4_0.gguf (Tokens/sec)",HIB,,,,17.94,17.87,17.87,18.08 "Llama.cpp - Model: llama-2-70b-chat.Q5_0.gguf (Tokens/sec)",HIB,,,,3.42,3.41,3.42,3.42 "OSPRay Studio - Camera: 1 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,,,,3404,3406,3493,3499 "OSPRay Studio - Camera: 2 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,,,,3451,3446,3515,3522 "OSPRay Studio - Camera: 3 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,,,,4049,4048,4157,4132 "OSPRay Studio - Camera: 1 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,,,,60673,61430,62802,63336 "OSPRay Studio - Camera: 1 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,,,,116377,115669,118221,118802 "OSPRay Studio - Camera: 2 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,,,,61987,62113,63402,62787 "OSPRay Studio - Camera: 2 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,,,,116566,116972,118980,119783 "OSPRay Studio - Camera: 3 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,,,,71361,71495,73024,73329 "OSPRay Studio - Camera: 3 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,,,,136464,136312,139685,139445 "OpenVINO - Model: Face Detection FP16 - Device: CPU (ms)",LIB,,,,929.23,927.57,964.2,965.35 "OpenVINO - Model: Person Detection FP16 - Device: CPU (ms)",LIB,,,,105.48,106.44,106.43,105.64 "OpenVINO - Model: Person Detection FP32 - Device: CPU (ms)",LIB,,,,105.97,106.24,105.91,106.32 "OpenVINO - Model: Vehicle Detection FP16 - Device: CPU (ms)",LIB,,,,13.36,13.29,13.65,13.65 "OpenVINO - Model: Face Detection FP16-INT8 - Device: CPU (ms)",LIB,,,,486.65,486.03,510.79,510.9 "OpenVINO - Model: Face Detection Retail FP16 - Device: CPU (ms)",LIB,,,,3.91,3.9,4.03,4.03 "OpenVINO - Model: Road Segmentation ADAS FP16 - Device: CPU (ms)",LIB,,,,27.69,27.53,28.77,28.82 "OpenVINO - Model: Vehicle Detection FP16-INT8 - Device: CPU (ms)",LIB,,,,8.07,8.05,8.52,8.52 "OpenVINO - Model: Weld Porosity Detection FP16 - Device: CPU (ms)",LIB,,,,18.69,18.69,19.58,19.56 "OpenVINO - Model: Face Detection Retail FP16-INT8 - Device: CPU (ms)",LIB,,,,5.41,5.42,5.78,5.78 "OpenVINO - Model: Road Segmentation ADAS FP16-INT8 - Device: CPU (ms)",LIB,,,,23.95,23.95,25.22,25.16 "OpenVINO - Model: Machine Translation EN To DE FP16 - Device: CPU (ms)",LIB,,,,79.82,79.39,82.18,81.87 "OpenVINO - Model: Weld Porosity Detection FP16-INT8 - Device: CPU (ms)",LIB,,,,9.56,9.56,10.22,10.21 "OpenVINO - Model: Person Vehicle Bike Detection FP16 - Device: CPU (ms)",LIB,,,,9.12,9.16,9.39,9.37 "OpenVINO - Model: Handwritten English Recognition FP16 - Device: CPU (ms)",LIB,,,,35.51,35.59,37.4,37.61 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU (ms)",LIB,,,,0.65,0.66,0.67,0.67 "OpenVINO - Model: Handwritten English Recognition FP16-INT8 - Device: CPU (ms)",LIB,,,,42.87,43.71,46.17,46.28 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU (ms)",LIB,,,,0.48,0.47,0.48,0.48 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,,,,747.0674,745.1806,753.1229,751.2117 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,,,,19.1107,19.1338,19.5831,19.5858 "Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,,,,59.8638,59.8673,59.9016,59.9698 "Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,,,,7.2332,7.261,7.2738,7.2896 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,,,,129.8749,129.8035,130.4755,130.7937 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,,,,607.935,608.1326,611.6026,611.4439 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,,,,59.8833,59.6674,60.0613,60.0284 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,,,,128.8158,128.845,129.5421,129.8101 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,,,,87.4908,87.325,88.1952,88.2278 "Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,,,,396.2914,397.9593,411.3435,410.3267 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,,,,41.6314,41.4377,41.5889,41.8438 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,,,,747.314,746.128,751.9259,750.3997 "DaCapo Benchmark - Java Test: Jython (msec)",LIB,,,,6703,6773,6865,6769 "DaCapo Benchmark - Java Test: Eclipse (msec)",LIB,,,,12656,12735,12826,12768 "DaCapo Benchmark - Java Test: GraphChi (msec)",LIB,,,,3536,3630,3538,3656 "DaCapo Benchmark - Java Test: Tradesoap (msec)",LIB,,,,5403,5168,5366,5149 "DaCapo Benchmark - Java Test: Tradebeans (msec)",LIB,,,,8561,8600,8520,8380 "DaCapo Benchmark - Java Test: Spring Boot (msec)",LIB,,,,2444,2460,2533,2452 "DaCapo Benchmark - Java Test: Apache Kafka (msec)",LIB,,,,5110,5121,5111,5114 "DaCapo Benchmark - Java Test: Apache Tomcat (msec)",LIB,,,,2107,2082,2094,2112 "DaCapo Benchmark - Java Test: jMonkeyEngine (msec)",LIB,,,,6914,6917,6917,6916 "DaCapo Benchmark - Java Test: Apache Cassandra (msec)",LIB,,,,5946,5938,5955,5927 "DaCapo Benchmark - Java Test: Apache Xalan XSLT (msec)",LIB,,,,871,859,852,861 "DaCapo Benchmark - Java Test: Batik SVG Toolkit (msec)",LIB,,,,1733,1723,1718,1738 "DaCapo Benchmark - Java Test: H2 Database Engine (msec)",LIB,,,,2675,2655,2773,2634 "DaCapo Benchmark - Java Test: FOP Print Formatter (msec)",LIB,,,,751,696,764,758 "DaCapo Benchmark - Java Test: PMD Source Code Analyzer (msec)",LIB,,,,1784,1820,1966,1833 "DaCapo Benchmark - Java Test: Apache Lucene Search Index (msec)",LIB,,,,4613,4589,4580,4602 "DaCapo Benchmark - Java Test: Apache Lucene Search Engine (msec)",LIB,,,,1402,1425,1379,1433 "DaCapo Benchmark - Java Test: Avrora AVR Simulation Framework (msec)",LIB,,,,5613,5441,5561,5572 "DaCapo Benchmark - Java Test: BioJava Biological Data Framework (msec)",LIB,,,,7874,7858,7904,7907 "DaCapo Benchmark - Java Test: Zxing 1D/2D Barcode Image Processing (msec)",LIB,,,,609,599,569,599 "DaCapo Benchmark - Java Test: H2O In-Memory Platform For Machine Learning (msec)",LIB,,,,3974,3868,3979,3755 "Y-Cruncher - Pi Digits To Calculate: 500M (sec)",LIB,15.693,5.202,5.213,5.656,5.685,5.783,5.751 "Y-Cruncher - Pi Digits To Calculate: 1B (sec)",LIB,33.923,10.416,10.476,11.676,11.595,11.902,11.975 "OpenFOAM - Input: drivaerFastback, Small Mesh Size - Mesh Time (sec)",LIB,,,,28.372583,30.75472,30.537591,30.724194 "OpenFOAM - Input: drivaerFastback, Small Mesh Size - Execution Time (sec)",LIB,,,,72.807288,71.201285,72.384007,72.305836 "Timed FFmpeg Compilation - Time To Compile (sec)",LIB,,,,23.557,23.759,24.446,24.3 "Timed Gem5 Compilation - Time To Compile (sec)",LIB,,,,254.01,272.61,258.307,258.934 "Timed Linux Kernel Compilation - Build: defconfig (sec)",LIB,,,,52.133,52.012,53.615,53.632 "Timed Linux Kernel Compilation - Build: allmodconfig (sec)",LIB,,,,433.789,434.187,453.693,452.606 "Blender - Blend File: BMW27 - Compute: CPU-Only (sec)",LIB,,,,44.73,44.48,47.52,47.41 "Blender - Blend File: Classroom - Compute: CPU-Only (sec)",LIB,,,,112.03,112.09,119.72,119.57 "Blender - Blend File: Fishy Cat - Compute: CPU-Only (sec)",LIB,,,,55.65,55.54,59.58,59.79 "Blender - Blend File: Barbershop - Compute: CPU-Only (sec)",LIB,,,,410.61,410.43,426.3,426.37 "Blender - Blend File: Pabellon Barcelona - Compute: CPU-Only (sec)",LIB,,,,139.09,138.6,148.74,148.56 "Quicksilver - CPU Power Consumption Monitor (Watts)",LIB,584.16,,,,,, "Quicksilver - CPU Power Consumption Monitor (Watts)",LIB,553.65,,,,,, "Quicksilver - CPU Power Consumption Monitor (Watts)",LIB,624.15,,,,,, "Y-Cruncher - CPU Power Consumption Monitor (Watts)",LIB,542.54,,,,,, "Y-Cruncher - CPU Power Consumption Monitor (Watts)",LIB,602.05,,,,,,