Intel Core i7-8565U testing with a Dell 0KTW76 (1.17.0 BIOS) and Intel UHD 620 WHL GT2 15GB 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 2308067-NE-DDDXXX42317
dddxxx,
"Apache Cassandra 4.1.3 - Test: Writes",
Higher Results Are Better
"a",26571,26273
"b",26509,26366
"Apache IoTDB 1.1.2 - Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 200",
Higher Results Are Better
"a",
"b",
"Apache IoTDB 1.1.2 - Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 200",
Higher Results Are Better
"a",
"b",
"Apache IoTDB 1.1.2 - Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 500",
Higher Results Are Better
"a",
"b",
"Apache IoTDB 1.1.2 - Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 500",
Higher Results Are Better
"a",
"b",
"Apache IoTDB 1.1.2 - Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 200",
Higher Results Are Better
"a",
"b",
"Apache IoTDB 1.1.2 - Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 200",
Higher Results Are Better
"a",
"b",
"Apache IoTDB 1.1.2 - Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 500",
Higher Results Are Better
"a",
"b",
"Apache IoTDB 1.1.2 - Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 500",
Higher Results Are Better
"a",
"b",
"Apache IoTDB 1.1.2 - Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 200",
Higher Results Are Better
"a",
"b",
"Apache IoTDB 1.1.2 - Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 200",
Higher Results Are Better
"a",
"b",
"Apache IoTDB 1.1.2 - Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 500",
Higher Results Are Better
"a",
"b",
"Apache IoTDB 1.1.2 - Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 500",
Higher Results Are Better
"a",
"b",
"Apache IoTDB 1.1.2 - Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 200",
Higher Results Are Better
"a",
"b",
"Apache IoTDB 1.1.2 - Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 200",
Higher Results Are Better
"a",
"b",
"Apache IoTDB 1.1.2 - Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 500",
Higher Results Are Better
"a",
"b",
"Apache IoTDB 1.1.2 - Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 500",
Higher Results Are Better
"a",
"b",
"Apache IoTDB 1.1.2 - Device Count: 200 - Batch Size Per Write: 100 - Sensor Count: 200",
Higher Results Are Better
"a",
"b",
"Apache IoTDB 1.1.2 - Device Count: 200 - Batch Size Per Write: 100 - Sensor Count: 200",
Higher Results Are Better
"a",
"b",
"Apache IoTDB 1.1.2 - Device Count: 200 - Batch Size Per Write: 100 - Sensor Count: 500",
Higher Results Are Better
"a",
"b",
"Apache IoTDB 1.1.2 - Device Count: 200 - Batch Size Per Write: 100 - Sensor Count: 500",
Higher Results Are Better
"a",
"b",
"Apache IoTDB 1.1.2 - Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 200",
Higher Results Are Better
"a",
"b",
"Apache IoTDB 1.1.2 - Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 200",
Higher Results Are Better
"a",
"b",
"Apache IoTDB 1.1.2 - Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 500",
Higher Results Are Better
"a",
"b",
"Apache IoTDB 1.1.2 - Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 500",
Higher Results Are Better
"a",
"b",
"Build2 0.15 - Time To Compile",
Lower Results Are Better
"a",554.793,557.079
"b",565.391,573.827
"dav1d 1.2.1 - Video Input: Chimera 1080p",
Higher Results Are Better
"a",246.87,241.36
"b",251.57,247.67
"dav1d 1.2.1 - Video Input: Summer Nature 4K",
Higher Results Are Better
"a",66.7,67.32
"b",64.03,68.73
"dav1d 1.2.1 - Video Input: Summer Nature 1080p",
Higher Results Are Better
"a",277.49,269.2
"b",313.48,308.39
"dav1d 1.2.1 - Video Input: Chimera 1080p 10-bit",
Higher Results Are Better
"a",210.71,171.89
"b",203.68,178.06
"Embree 4.1 - Binary: Pathtracer - Model: Crown",
Higher Results Are Better
"a",3.515,3.5285
"b",3.521,3.3874
"Embree 4.1 - Binary: Pathtracer ISPC - Model: Crown",
Higher Results Are Better
"a",3.6709,3.8029
"b",3.8001,3.5922
"Embree 4.1 - Binary: Pathtracer - Model: Asian Dragon",
Higher Results Are Better
"a",4.2597,4.1769
"b",4.2237,4.1759
"Embree 4.1 - Binary: Pathtracer - Model: Asian Dragon Obj",
Higher Results Are Better
"a",3.875,3.7708
"b",3.8381,3.8625
"Embree 4.1 - Binary: Pathtracer ISPC - Model: Asian Dragon",
Higher Results Are Better
"a",4.858,4.7186
"b",4.8,4.7271
"Embree 4.1 - Binary: Pathtracer ISPC - Model: Asian Dragon Obj",
Higher Results Are Better
"a",4.1398,4.1942
"b",4.1804,3.8041
"Intel Open Image Denoise 2.0 - Run: RT.hdr_alb_nrm.3840x2160 - Device: CPU-Only",
Higher Results Are Better
"a",0.11702873406508,0.11764871974663
"b",0.11672547685275,0.1154050775926
"Intel Open Image Denoise 2.0 - Run: RT.ldr_alb_nrm.3840x2160 - Device: CPU-Only",
Higher Results Are Better
"a",0.11745429559723,0.11640874369356
"b",0.11498587973397,0.11544144809753
"Intel Open Image Denoise 2.0 - Run: RTLightmap.hdr.4096x4096 - Device: CPU-Only",
Higher Results Are Better
"a",0.058828028025673,0.058614242088543
"b",0.057217403245371,0.057874724371625
"libxsmm 2-1.17-3645 - M N K: 128",
Higher Results Are Better
"a",84.5,75.3
"b",87.8,79.6
"libxsmm 2-1.17-3645 - M N K: 32",
Higher Results Are Better
"a",47.3,48
"b",47.4,48.5
"libxsmm 2-1.17-3645 - M N K: 64",
Higher Results Are Better
"a",95.3,86.4
"b",95.6,86
"Liquid-DSP 1.6 - Threads: 1 - Buffer Length: 256 - Filter Length: 32",
Higher Results Are Better
"a",46968000,46065000
"b",46748000,46902000
"Liquid-DSP 1.6 - Threads: 1 - Buffer Length: 256 - Filter Length: 57",
Higher Results Are Better
"a",43654000,43204000
"b",43582000,43273000
"Liquid-DSP 1.6 - Threads: 2 - Buffer Length: 256 - Filter Length: 32",
Higher Results Are Better
"a",86424000,84677000
"b",86825000,84503000
"Liquid-DSP 1.6 - Threads: 2 - Buffer Length: 256 - Filter Length: 57",
Higher Results Are Better
"a",73924000,73667000
"b",74241000,73000000
"Liquid-DSP 1.6 - Threads: 4 - Buffer Length: 256 - Filter Length: 32",
Higher Results Are Better
"a",142930000,137630000
"b",142560000,137340000
"Liquid-DSP 1.6 - Threads: 4 - Buffer Length: 256 - Filter Length: 57",
Higher Results Are Better
"a",125950000,113230000
"b",115600000,108810000
"Liquid-DSP 1.6 - Threads: 8 - Buffer Length: 256 - Filter Length: 32",
Higher Results Are Better
"a",189420000,166470000
"b",191020000,180060000
"Liquid-DSP 1.6 - Threads: 8 - Buffer Length: 256 - Filter Length: 57",
Higher Results Are Better
"a",145960000,134730000
"b",140100000,125560000
"Liquid-DSP 1.6 - Threads: 1 - Buffer Length: 256 - Filter Length: 512",
Higher Results Are Better
"a",7713700,7729900
"b",7690000,7731700
"Liquid-DSP 1.6 - Threads: 2 - Buffer Length: 256 - Filter Length: 512",
Higher Results Are Better
"a",15338000,15021000
"b",14874000,15173000
"Liquid-DSP 1.6 - Threads: 4 - Buffer Length: 256 - Filter Length: 512",
Higher Results Are Better
"a",26297000,25364000
"b",26291000,25334000
"Liquid-DSP 1.6 - Threads: 8 - Buffer Length: 256 - Filter Length: 512",
Higher Results Are Better
"a",39187000,32251000
"b",33953000,39093000
"Memcached 1.6.19 - Set To Get Ratio: 1:5",
Higher Results Are Better
"a",537655.32,513081.46
"b",532274.82,504341.58
"Memcached 1.6.19 - Set To Get Ratio: 1:10",
Higher Results Are Better
"a",511598.27,492502.59
"b",482317.32,491297.66
"Memcached 1.6.19 - Set To Get Ratio: 1:100",
Higher Results Are Better
"a",507090.65,485528.86
"b",500704.28,471140.98
"NCNN 20230517 - Target: CPU - Model: mobilenet",
Lower Results Are Better
"a",25.74,26.11
"b",27.7,27.65
"NCNN 20230517 - Target: CPU-v2-v2 - Model: mobilenet-v2",
Lower Results Are Better
"a",5.99,7.39
"b",6.08,6.21
"NCNN 20230517 - Target: CPU-v3-v3 - Model: mobilenet-v3",
Lower Results Are Better
"a",4.34,5.94
"b",4.27,4.34
"NCNN 20230517 - Target: CPU - Model: shufflenet-v2",
Lower Results Are Better
"a",4.26,4.31
"b",3,3.03
"NCNN 20230517 - Target: CPU - Model: mnasnet",
Lower Results Are Better
"a",6.01,6.06
"b",4.46,4.51
"NCNN 20230517 - Target: CPU - Model: efficientnet-b0",
Lower Results Are Better
"a",9.13,10.42
"b",9.4,9.45
"NCNN 20230517 - Target: CPU - Model: blazeface",
Lower Results Are Better
"a",0.9,0.91
"b",0.93,0.93
"NCNN 20230517 - Target: CPU - Model: googlenet",
Lower Results Are Better
"a",16.15,16.3
"b",16.09,16.28
"NCNN 20230517 - Target: CPU - Model: vgg16",
Lower Results Are Better
"a",97.16,97.67
"b",101.08,96.41
"NCNN 20230517 - Target: CPU - Model: resnet18",
Lower Results Are Better
"a",12.57,12.65
"b",12.51,12.73
"NCNN 20230517 - Target: CPU - Model: alexnet",
Lower Results Are Better
"a",11.62,11.73
"b",11.57,11.71
"NCNN 20230517 - Target: CPU - Model: resnet50",
Lower Results Are Better
"a",36.06,35.97
"b",32.37,36.28
"NCNN 20230517 - Target: CPU - Model: yolov4-tiny",
Lower Results Are Better
"a",38.29,38.45
"b",40.13,38.48
"NCNN 20230517 - Target: CPU - Model: squeezenet_ssd",
Lower Results Are Better
"a",16.25,16.84
"b",15.43,16.17
"NCNN 20230517 - Target: CPU - Model: regnety_400m",
Lower Results Are Better
"a",10.17,9.89
"b",10.08,10.15
"NCNN 20230517 - Target: CPU - Model: vision_transformer",
Lower Results Are Better
"a",234.13,233.79
"b",237.2,227.13
"NCNN 20230517 - Target: CPU - Model: FastestDet",
Lower Results Are Better
"a",5.43,5.35
"b",5.7,5.39
"NCNN 20230517 - Target: Vulkan GPU - Model: mobilenet",
Lower Results Are Better
"a",28.34,27.96
"b",27.95,26.8
"NCNN 20230517 - Target: Vulkan GPU-v2-v2 - Model: mobilenet-v2",
Lower Results Are Better
"a",6,7.33
"b",6.02,7.32
"NCNN 20230517 - Target: Vulkan GPU-v3-v3 - Model: mobilenet-v3",
Lower Results Are Better
"a",4.34,5.87
"b",4.31,5.9
"NCNN 20230517 - Target: Vulkan GPU - Model: shufflenet-v2",
Lower Results Are Better
"a",3.03,4.29
"b",3.05,4.28
"NCNN 20230517 - Target: Vulkan GPU - Model: mnasnet",
Lower Results Are Better
"a",4.42,6.04
"b",4.48,6.03
"NCNN 20230517 - Target: Vulkan GPU - Model: efficientnet-b0",
Lower Results Are Better
"a",9.29,9.14
"b",9.36,9.32
"NCNN 20230517 - Target: Vulkan GPU - Model: blazeface",
Lower Results Are Better
"a",0.94,0.93
"b",0.95,0.96
"NCNN 20230517 - Target: Vulkan GPU - Model: googlenet",
Lower Results Are Better
"a",16.29,16.29
"b",16.2,16.18
"NCNN 20230517 - Target: Vulkan GPU - Model: vgg16",
Lower Results Are Better
"a",97.47,97.93
"b",96.15,96.37
"NCNN 20230517 - Target: Vulkan GPU - Model: resnet18",
Lower Results Are Better
"a",12.77,12.68
"b",12.52,12.6
"NCNN 20230517 - Target: Vulkan GPU - Model: alexnet",
Lower Results Are Better
"a",11.64,11.7
"b",11.65,11.72
"NCNN 20230517 - Target: Vulkan GPU - Model: resnet50",
Lower Results Are Better
"a",32.63,33.73
"b",32.24,34.96
"NCNN 20230517 - Target: Vulkan GPU - Model: yolov4-tiny",
Lower Results Are Better
"a",40.41,38.37
"b",40.27,38.06
"NCNN 20230517 - Target: Vulkan GPU - Model: squeezenet_ssd",
Lower Results Are Better
"a",15.5,16.43
"b",14.64,16.32
"NCNN 20230517 - Target: Vulkan GPU - Model: regnety_400m",
Lower Results Are Better
"a",9.81,9.83
"b",10.48,10.34
"NCNN 20230517 - Target: Vulkan GPU - Model: vision_transformer",
Lower Results Are Better
"a",236.83,243.23
"b",232.33,238.68
"NCNN 20230517 - Target: Vulkan GPU - Model: FastestDet",
Lower Results Are Better
"a",5.46,5.86
"b",5.43,5.51
"Neural Magic DeepSparse 1.5 - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",2.558,2.1474
"b",2.5675,2.1029
"Neural Magic DeepSparse 1.5 - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",780.0149,931.3389
"b",778.957,951.0294
"Neural Magic DeepSparse 1.5 - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",65.7971,65.0528
"b",65.6473,65.0654
"Neural Magic DeepSparse 1.5 - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",30.3697,30.7099
"b",30.4348,30.6979
"Neural Magic DeepSparse 1.5 - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",31.2184,26.6185
"b",31.1293,25.7986
"Neural Magic DeepSparse 1.5 - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",64.0224,75.0962
"b",64.1819,77.4607
"Neural Magic DeepSparse 1.5 - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",9.3824,7.2193
"b",9.377,7.1893
"Neural Magic DeepSparse 1.5 - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",212.9987,276.1247
"b",213.1979,278.0881
"Neural Magic DeepSparse 1.5 - Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",33.3984,27.7362
"b",33.0577,26.4628
"Neural Magic DeepSparse 1.5 - Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",59.8602,72.0431
"b",60.4507,75.5498
"Neural Magic DeepSparse 1.5 - Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",216.8403,176.7283
"b",217.2391,173.7324
"Neural Magic DeepSparse 1.5 - Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",9.2016,11.2914
"b",9.185,11.487
"Neural Magic DeepSparse 1.5 - Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",16.2846,13.0593
"b",16.4261,12.7876
"Neural Magic DeepSparse 1.5 - Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",122.7855,153.1123
"b",121.7285,156.3664
"Neural Magic DeepSparse 1.5 - Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",3.0647,2.2706
"b",3.2273,3.2648
"Neural Magic DeepSparse 1.5 - Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",650.5357,877.9995
"b",616.7822,612.5763
"Neural Magic DeepSparse 1.5 - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",32.5325,26.7845
"b",26.2928,31.6315
"Neural Magic DeepSparse 1.5 - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",61.4536,74.6446
"b",76.0385,63.1976
"Neural Magic DeepSparse 1.5 - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",14.2222,10.8659
"b",12.8495,13.0509
"Neural Magic DeepSparse 1.5 - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",140.427,183.9024
"b",155.3094,153.2077
"Neural Magic DeepSparse 1.5 - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",19.8726,15.1521
"b",23.8494,17.6034
"Neural Magic DeepSparse 1.5 - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",100.6122,131.9244
"b",83.8397,113.5888
"Neural Magic DeepSparse 1.5 - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",3.0309,2.932
"b",3.6435,2.8204
"Neural Magic DeepSparse 1.5 - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",656.3411,682.0206
"b",548.8936,707.8341
"Neural Magic DeepSparse 1.5 - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",21.2257,21.3712
"b",34.4544,34.1917
"Neural Magic DeepSparse 1.5 - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",94.1678,93.5286
"b",58.0157,58.4578
"Neural Magic DeepSparse 1.5 - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",8.8508,10.0597
"b",11.4082,10.9355
"Neural Magic DeepSparse 1.5 - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",225.4551,198.5109
"b",175.2894,182.865
"Neural Magic DeepSparse 1.5 - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",2.0224,2.0394
"b",2.421,2.3555
"Neural Magic DeepSparse 1.5 - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",979.468,978.8914
"b",822.6656,849.0378
"Opus Codec Encoding 1.4 - WAV To Opus Encode",
Lower Results Are Better
"a",35.497,35.294
"b",35.365,35.487
"OSPRay 2.12 - Benchmark: particle_volume/ao/real_time",
Higher Results Are Better
"a",1.39648,1.59019
"b",1.3411,1.3972
"OSPRay 2.12 - Benchmark: particle_volume/scivis/real_time",
Higher Results Are Better
"a",1.37483,1.37315
"b",1.34754,1.34179
"OSPRay 2.12 - Benchmark: particle_volume/pathtracer/real_time",
Higher Results Are Better
"a",52.0041,53.1767
"b",50.0668,50.0989
"OSPRay 2.12 - Benchmark: gravity_spheres_volume/dim_512/ao/real_time",
Higher Results Are Better
"a",0.735338,0.754953
"b",0.725822,0.743744
"OSPRay 2.12 - Benchmark: gravity_spheres_volume/dim_512/scivis/real_time",
Higher Results Are Better
"a",0.607886,0.636478
"b",0.684773,0.735198
"OSPRay 2.12 - Benchmark: gravity_spheres_volume/dim_512/pathtracer/real_time",
Higher Results Are Better
"a",1.0498,1.07347
"b",1.02982,0.996086
"QuantLib 1.30 - ",
Higher Results Are Better
"a",2169.6,2590
"b",2170.5,2639
"Redis 7.0.12 + memtier_benchmark 2.0 - Protocol: Redis - Clients: 50 - Set To Get Ratio: 1:5",
Higher Results Are Better
"a",1082776.68,995845.55
"b",1403612.89,1394153.12
"Redis 7.0.12 + memtier_benchmark 2.0 - Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:5",
Higher Results Are Better
"a",1061563.79,971842.74
"b",1411424.72,1332641.63
"Redis 7.0.12 + memtier_benchmark 2.0 - Protocol: Redis - Clients: 50 - Set To Get Ratio: 1:10",
Higher Results Are Better
"a",1090630.95,1128993.92
"b",1473184.51,1437159.21
"Redis 7.0.12 + memtier_benchmark 2.0 - Protocol: Redis - Clients: 500 - Set To Get Ratio: 1:5",
Higher Results Are Better
"a",898748.65,881999.8
"b",1274284.14,1222937.26
"Redis 7.0.12 + memtier_benchmark 2.0 - Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:10",
Higher Results Are Better
"a",1028072.2,1029210.18
"b",1385940.7,1369726.26
"Redis 7.0.12 + memtier_benchmark 2.0 - Protocol: Redis - Clients: 500 - Set To Get Ratio: 1:10",
Higher Results Are Better
"a",959741.15,869182.88
"b",1316823.16,1190224.22
"SQLite 3.41.2 - Threads / Copies: 1",
Lower Results Are Better
"a",29.464,31.189
"b",88.392,88.778
"SQLite 3.41.2 - Threads / Copies: 2",
Lower Results Are Better
"a",98.537,148.393
"b",168.593,171.435
"SQLite 3.41.2 - Threads / Copies: 4",
Lower Results Are Better
"a",108.344,143.315
"b",172.54,173.549
"Stress-NG 0.15.10 - Test: Hash",
Higher Results Are Better
"a",485362.59,562036.81
"b",677341.08,599959.93
"Stress-NG 0.15.10 - Test: MMAP",
Higher Results Are Better
"a",24.05,17.67
"b",32.16,24.76
"Stress-NG 0.15.10 - Test: NUMA",
Higher Results Are Better
"a",59.57,48.32
"b",65.76,55.22
"Stress-NG 0.15.10 - Test: Pipe",
Higher Results Are Better
"a",1452490.32,1391729.36
"b",1665003.28,1401706.94
"Stress-NG 0.15.10 - Test: Poll",
Higher Results Are Better
"a",263822.46,304779.13
"b",342313.98,294619.02
"Stress-NG 0.15.10 - Test: Zlib",
Higher Results Are Better
"a",241.11,304.99
"b",357.4,314.42
"Stress-NG 0.15.10 - Test: Futex",
Higher Results Are Better
"a",449658.79,522498.57
"b",681521.49,566506.85
"Stress-NG 0.15.10 - Test: MEMFD",
Higher Results Are Better
"a",57.37,55.8
"b",43.81,42.37
"Stress-NG 0.15.10 - Test: Mutex",
Higher Results Are Better
"a",680312.12,528001.32
"b",715293.16,673831.77
"Stress-NG 0.15.10 - Test: Atomic",
Higher Results Are Better
"a",235.28,213.73
"b",261.51,238.47
"Stress-NG 0.15.10 - Test: Crypto",
Higher Results Are Better
"a",4542.8,4638.25
"b",5780.63,5588.43
"Stress-NG 0.15.10 - Test: Malloc",
Higher Results Are Better
"a",336296.97,415571
"b",480917.6,387394.59
"Stress-NG 0.15.10 - Test: Cloning",
Higher Results Are Better
"a",640.79,698.02
"b",718.05,713.11
"Stress-NG 0.15.10 - Test: Forking",
Higher Results Are Better
"a",9250.51,9853.07
"b",10809.66,13471.54
"Stress-NG 0.15.10 - Test: Pthread",
Higher Results Are Better
"a",30241.98,39706.53
"b",37853.74,46605.47
"Stress-NG 0.15.10 - Test: AVL Tree",
Higher Results Are Better
"a",16.46,16.36
"b",19.1,19.4
"Stress-NG 0.15.10 - Test: IO_uring",
Higher Results Are Better
"a",167994.9,172989.42
"b",183436.13,182437.22
"Stress-NG 0.15.10 - Test: SENDFILE",
Higher Results Are Better
"a",38449.72,35109.99
"b",44903.98,42600.76
"Stress-NG 0.15.10 - Test: CPU Cache",
Higher Results Are Better
"a",684180.05,1174176.81
"b",1292153.26,1014232.24
"Stress-NG 0.15.10 - Test: CPU Stress",
Higher Results Are Better
"a",6742.23,7877.86
"b",8025,7707.7
"Stress-NG 0.15.10 - Test: Semaphores",
Higher Results Are Better
"a",3506040.62,2931410.39
"b",3311568.51,3171247.91
"Stress-NG 0.15.10 - Test: Matrix Math",
Higher Results Are Better
"a",18343.8,16293.5
"b",17971.42,16262.15
"Stress-NG 0.15.10 - Test: Vector Math",
Higher Results Are Better
"a",13091.83,12240.55
"b",13357.99,11530.66
"Stress-NG 0.15.10 - Test: Function Call",
Higher Results Are Better
"a",2155.07,1962.33
"b",2204.6,2123.79
"Stress-NG 0.15.10 - Test: x86_64 RdRand",
Higher Results Are Better
"a",3280.67,3253.94
"b",3202.49,3065.58
"Stress-NG 0.15.10 - Test: Floating Point",
Higher Results Are Better
"a",718.83,768.83
"b",788.52,737.19
"Stress-NG 0.15.10 - Test: Matrix 3D Math",
Higher Results Are Better
"a",378.13,388.85
"b",389.85,402.2
"Stress-NG 0.15.10 - Test: Memory Copying",
Higher Results Are Better
"a",1119.58,1037.87
"b",1128.8,1048.69
"Stress-NG 0.15.10 - Test: Vector Shuffle",
Higher Results Are Better
"a",2464.61,2397.83
"b",2448.08,2413.94
"Stress-NG 0.15.10 - Test: Socket Activity",
Higher Results Are Better
"a",2745.41,2245.72
"b",2779.7,2237.55
"Stress-NG 0.15.10 - Test: Wide Vector Math",
Higher Results Are Better
"a",150308.6,144321.09
"b",151718.42,145059.16
"Stress-NG 0.15.10 - Test: Context Switching",
Higher Results Are Better
"a",758460.47,652135.45
"b",717034.91,749504.66
"Stress-NG 0.15.10 - Test: Fused Multiply-Add",
Higher Results Are Better
"a",3041463.38,2915002.08
"b",3000044.16,2474922.44
"Stress-NG 0.15.10 - Test: Vector Floating Point",
Higher Results Are Better
"a",7653.43,6925.21
"b",8188.53,7566.87
"Stress-NG 0.15.10 - Test: Glibc C String Functions",
Higher Results Are Better
"a",2577995.44,2393716.7
"b",2518708.67,2202469.53
"Stress-NG 0.15.10 - Test: Glibc Qsort Data Sorting",
Higher Results Are Better
"a",75.03,63.23
"b",77.86,70.39
"Stress-NG 0.15.10 - Test: System V Message Passing",
Higher Results Are Better
"a",2772214.99,2532710.25
"b",2720005.31,2319826.89
"SVT-AV1 1.6 - Encoder Mode: Preset 4 - Input: Bosphorus 4K",
Higher Results Are Better
"a",1.069,1.114
"b",0.945,0.95
"SVT-AV1 1.6 - Encoder Mode: Preset 8 - Input: Bosphorus 4K",
Higher Results Are Better
"a",9.796,9.183
"b",10.596,8.276
"SVT-AV1 1.6 - Encoder Mode: Preset 12 - Input: Bosphorus 4K",
Higher Results Are Better
"a",34.585,36.409
"b",29.786,28.17
"SVT-AV1 1.6 - Encoder Mode: Preset 13 - Input: Bosphorus 4K",
Higher Results Are Better
"a",36.153,31.945
"b",33.684,28.266
"SVT-AV1 1.6 - Encoder Mode: Preset 4 - Input: Bosphorus 1080p",
Higher Results Are Better
"a",4.755,3.765
"b",4.881,4.664
"SVT-AV1 1.6 - Encoder Mode: Preset 8 - Input: Bosphorus 1080p",
Higher Results Are Better
"a",36.607,34.871
"b",28.432,33.596
"SVT-AV1 1.6 - Encoder Mode: Preset 12 - Input: Bosphorus 1080p",
Higher Results Are Better
"a",154.834,154.36
"b",158.387,156.996
"SVT-AV1 1.6 - Encoder Mode: Preset 13 - Input: Bosphorus 1080p",
Higher Results Are Better
"a",203.514,202.189
"b",207.604,205.933
"Timed Godot Game Engine Compilation 4.0 - Time To Compile",
Lower Results Are Better
"a",1372.89,1372.318
"b",1397.722,1371.809
"Timed LLVM Compilation 16.0 - Build System: Ninja",
Lower Results Are Better
"a",2887.333,2896.842
"b",2903.932,2913.823
"Timed LLVM Compilation 16.0 - Build System: Unix Makefiles",
Lower Results Are Better
"a",2921.916,3013.14
"b",2937.52,2922.301
"vkpeak 20230730 - fp32-scalar",
Higher Results Are Better
"a",268.33,268.33
"b",268.34,268.31
"VVenC 1.9 - Video Input: Bosphorus 4K - Video Preset: Fast",
Higher Results Are Better
"a",1.192,1.167
"b",1.182,1.157
"VVenC 1.9 - Video Input: Bosphorus 4K - Video Preset: Faster",
Higher Results Are Better
"a",2.745,2.641
"b",2.716,2.627
"VVenC 1.9 - Video Input: Bosphorus 1080p - Video Preset: Fast",
Higher Results Are Better
"a",3.919,3.89
"b",3.748,3.832
"VVenC 1.9 - Video Input: Bosphorus 1080p - Video Preset: Faster",
Higher Results Are Better
"a",9.792,9.971
"b",10.47,9.265
"Xonotic 0.8.6 - Resolution: 1920 x 1080 - Effects Quality: Low",
Higher Results Are Better
"a",206.3217317,204.8907335
"b",206.0452747,204.8519772
"Xonotic 0.8.6 - Resolution: 1920 x 1080 - Effects Quality: High",
Higher Results Are Better
"a",91.1527989,91.0763988
"b",91.2262973,90.5897217
"Xonotic 0.8.6 - Resolution: 1920 x 1080 - Effects Quality: Ultra",
Higher Results Are Better
"a",78.0963229,77.7714242
"b",77.9255439,77.1136335
"Xonotic 0.8.6 - Resolution: 1920 x 1080 - Effects Quality: Ultimate",
Higher Results Are Better
"a",59.2384913,59.1032851
"b",58.8897832,59.010095
"Z3 Theorem Prover 4.12.1 - SMT File: 1.smt2",
Lower Results Are Better
"a",44.023,43.922
"b",43.595,43.243
"Z3 Theorem Prover 4.12.1 - SMT File: 2.smt2",
Lower Results Are Better
"a",130.83,131.052
"b",131.355,131.454