Intel Xeon Gold 6421N 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 2307311-NE-NEWXEON6232
new xeon,
"Apache Cassandra 4.1.3 - Test: Writes",
Higher Results Are Better
"a",154822,156429
"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",
"Blender 3.6 - Blend File: BMW27 - Compute: CPU-Only",
Lower Results Are Better
"a",47.13,47.17
"b",47.3,47.13
"Blender 3.6 - Blend File: Classroom - Compute: CPU-Only",
Lower Results Are Better
"a",127.83,127.73
"b",127.88,127.63
"Blender 3.6 - Blend File: Fishy Cat - Compute: CPU-Only",
Lower Results Are Better
"a",63.99,64.15
"b",63.8,64.21
"Blender 3.6 - Blend File: Barbershop - Compute: CPU-Only",
Lower Results Are Better
"a",493.66,493.23
"b",494.03,493.18
"Blender 3.6 - Blend File: Pabellon Barcelona - Compute: CPU-Only",
Lower Results Are Better
"a",159.9,159.98
"BRL-CAD 7.36 - VGR Performance Metric",
Higher Results Are Better
"a",462917,470454
"HeFFTe - Highly Efficient FFT for Exascale 2.3 - Test: c2c - Backend: FFTW - Precision: float - X Y Z: 128",
Higher Results Are Better
"a",132.422,130.89
"b",130.368,131.595
"HeFFTe - Highly Efficient FFT for Exascale 2.3 - Test: c2c - Backend: FFTW - Precision: float - X Y Z: 256",
Higher Results Are Better
"a",75.3251,76.7346
"b",75.3849,75.2153
"HeFFTe - Highly Efficient FFT for Exascale 2.3 - Test: c2c - Backend: FFTW - Precision: float - X Y Z: 512",
Higher Results Are Better
"a",78.465,79.1931
"b",79.0207,78.9002
"HeFFTe - Highly Efficient FFT for Exascale 2.3 - Test: r2c - Backend: FFTW - Precision: float - X Y Z: 128",
Higher Results Are Better
"a",206.63,207.858
"b",206.031,206.402
"HeFFTe - Highly Efficient FFT for Exascale 2.3 - Test: r2c - Backend: FFTW - Precision: float - X Y Z: 256",
Higher Results Are Better
"a",146.062,153.588
"b",152.465,155.64
"HeFFTe - Highly Efficient FFT for Exascale 2.3 - Test: r2c - Backend: FFTW - Precision: float - X Y Z: 512",
Higher Results Are Better
"a",140.77,142.04
"b",140.989,141.396
"HeFFTe - Highly Efficient FFT for Exascale 2.3 - Test: c2c - Backend: FFTW - Precision: double - X Y Z: 128",
Higher Results Are Better
"a",61.6956,67.1569
"b",59.8858,64.709
"HeFFTe - Highly Efficient FFT for Exascale 2.3 - Test: c2c - Backend: FFTW - Precision: double - X Y Z: 256",
Higher Results Are Better
"a",39.1773,38.6835
"b",38.6752,38.3611
"HeFFTe - Highly Efficient FFT for Exascale 2.3 - Test: c2c - Backend: FFTW - Precision: double - X Y Z: 512",
Higher Results Are Better
"a",44.0029,43.93
"b",44.028,43.9847
"HeFFTe - Highly Efficient FFT for Exascale 2.3 - Test: c2c - Backend: Stock - Precision: float - X Y Z: 128",
Higher Results Are Better
"a",84.4426,87.0369
"b",84.6079,86.3621
"HeFFTe - Highly Efficient FFT for Exascale 2.3 - Test: c2c - Backend: Stock - Precision: float - X Y Z: 256",
Higher Results Are Better
"a",75.5726,74.6057
"b",74.8243,75.0328
"HeFFTe - Highly Efficient FFT for Exascale 2.3 - Test: c2c - Backend: Stock - Precision: float - X Y Z: 512",
Higher Results Are Better
"a",72.3486,72.7732
"b",72.5395,72.5386
"HeFFTe - Highly Efficient FFT for Exascale 2.3 - Test: r2c - Backend: FFTW - Precision: double - X Y Z: 128",
Higher Results Are Better
"a",122.355,121.232
"b",123.675,121.244
"HeFFTe - Highly Efficient FFT for Exascale 2.3 - Test: r2c - Backend: FFTW - Precision: double - X Y Z: 256",
Higher Results Are Better
"a",72.7318,71.8468
"b",72.3168,72.0794
"HeFFTe - Highly Efficient FFT for Exascale 2.3 - Test: r2c - Backend: FFTW - Precision: double - X Y Z: 512",
Higher Results Are Better
"a",74.9523,73.9944
"b",74.8796,74.5499
"HeFFTe - Highly Efficient FFT for Exascale 2.3 - Test: r2c - Backend: Stock - Precision: float - X Y Z: 128",
Higher Results Are Better
"a",151.862,148.008
"b",153.043,150.563
"HeFFTe - Highly Efficient FFT for Exascale 2.3 - Test: r2c - Backend: Stock - Precision: float - X Y Z: 256",
Higher Results Are Better
"a",151.357,164.376
"b",160.836,167.258
"HeFFTe - Highly Efficient FFT for Exascale 2.3 - Test: r2c - Backend: Stock - Precision: float - X Y Z: 512",
Higher Results Are Better
"a",137.532,137.54
"b",138.071,137.409
"HeFFTe - Highly Efficient FFT for Exascale 2.3 - Test: c2c - Backend: Stock - Precision: double - X Y Z: 128",
Higher Results Are Better
"a",46.8995,46.3718
"b",46.1326,52.9134
"HeFFTe - Highly Efficient FFT for Exascale 2.3 - Test: c2c - Backend: Stock - Precision: double - X Y Z: 256",
Higher Results Are Better
"a",39.0288,38.8938
"b",38.7448,38.6066
"HeFFTe - Highly Efficient FFT for Exascale 2.3 - Test: c2c - Backend: Stock - Precision: double - X Y Z: 512",
Higher Results Are Better
"a",40.7942,40.6933
"b",40.6606,40.6689
"HeFFTe - Highly Efficient FFT for Exascale 2.3 - Test: r2c - Backend: Stock - Precision: double - X Y Z: 128",
Higher Results Are Better
"a",93.3001,91.4944
"b",91.0737,90.8965
"HeFFTe - Highly Efficient FFT for Exascale 2.3 - Test: r2c - Backend: Stock - Precision: double - X Y Z: 256",
Higher Results Are Better
"a",76.506,77.3023
"b",76.3836,77.6853
"HeFFTe - Highly Efficient FFT for Exascale 2.3 - Test: r2c - Backend: Stock - Precision: double - X Y Z: 512",
Higher Results Are Better
"a",76.6017,76.6203
"b",76.49,76.7181
"High Performance Conjugate Gradient 3.1 - X Y Z: 104 104 104 - RT: 60",
Higher Results Are Better
"a",27.7519,27.8097
"b",27.8319,27.849
"High Performance Conjugate Gradient 3.1 - X Y Z: 144 144 144 - RT: 60",
Higher Results Are Better
"a",27.4322,27.4103
"b",27.4511,27.3269
"High Performance Conjugate Gradient 3.1 - X Y Z: 160 160 160 - RT: 60",
Higher Results Are Better
"a",27.4758,27.5414
"b",27.3292,27.4663
"Laghos 3.1 - Test: Triple Point Problem",
Higher Results Are Better
"a",177.9151558422,177.6470622659
"b",176.9433776584,176.8999507195
"Laghos 3.1 - Test: Sedov Blast Wave, ube_922_hex.mesh",
Higher Results Are Better
"a",216.616824542,217.1002521214
"b",217.3699439053,217.0101006793
"libxsmm 2-1.17-3645 - M N K: 128",
Higher Results Are Better
"a",1216.4,1207.2
"b",1226.1,1223.9
"libxsmm 2-1.17-3645 - M N K: 256",
Higher Results Are Better
"a",880.2,878.9
"b",753.1,764.6
"libxsmm 2-1.17-3645 - M N K: 32",
Higher Results Are Better
"a",440.2,439.7
"b",444.4,444.7
"libxsmm 2-1.17-3645 - M N K: 64",
Higher Results Are Better
"a",834.8,832.7
"b",840.1,839.7
"Liquid-DSP 1.6 - Threads: 16 - Buffer Length: 256 - Filter Length: 32",
Higher Results Are Better
"a",555880000,560010000
"b",558050000,559260000
"Liquid-DSP 1.6 - Threads: 16 - Buffer Length: 256 - Filter Length: 57",
Higher Results Are Better
"a",862800000,834070000
"b",862890000,861500000
"Liquid-DSP 1.6 - Threads: 32 - Buffer Length: 256 - Filter Length: 32",
Higher Results Are Better
"a",847060000,847110000
"b",847590000,847760000
"Liquid-DSP 1.6 - Threads: 32 - Buffer Length: 256 - Filter Length: 57",
Higher Results Are Better
"a",1328400000,1327800000
"b",1319500000,1328300000
"Liquid-DSP 1.6 - Threads: 64 - Buffer Length: 256 - Filter Length: 32",
Higher Results Are Better
"a",1577000000,1577600000
"b",1576400000,1577300000
"Liquid-DSP 1.6 - Threads: 64 - Buffer Length: 256 - Filter Length: 57",
Higher Results Are Better
"a",1728300000,1729400000
"b",1732800000,1734600000
"Liquid-DSP 1.6 - Threads: 16 - Buffer Length: 256 - Filter Length: 512",
Higher Results Are Better
"a",241990000,245890000
"b",245650000,251990000
"Liquid-DSP 1.6 - Threads: 32 - Buffer Length: 256 - Filter Length: 512",
Higher Results Are Better
"a",381600000,385510000
"b",383570000,373730000
"Liquid-DSP 1.6 - Threads: 64 - Buffer Length: 256 - Filter Length: 512",
Higher Results Are Better
"a",513520000,512750000
"b",512240000,513840000
"Neural Magic DeepSparse 1.5 - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",34.4719,34.5902
"b",34.6769,34.4308
"Neural Magic DeepSparse 1.5 - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",461.1994,460.3641
"b",458.3145,463.2031
"Neural Magic DeepSparse 1.5 - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",1075.3895,1074.2541
"b",1076.9623,1074.9519
"Neural Magic DeepSparse 1.5 - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",14.8545,14.8654
"b",14.8333,14.8613
"Neural Magic DeepSparse 1.5 - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",391.9153,389.8999
"b",392.0349,391.79
"Neural Magic DeepSparse 1.5 - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",40.8051,41.0167
"b",40.7933,40.8188
"Neural Magic DeepSparse 1.5 - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",121.6409,121.7377
"b",121.82,122.2533
"Neural Magic DeepSparse 1.5 - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",131.4965,131.4029
"b",131.2832,130.8495
"Neural Magic DeepSparse 1.5 - Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",479.6654,479.9097
"b",479.9776,481.0669
"Neural Magic DeepSparse 1.5 - Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",33.3369,33.3187
"b",33.316,33.2401
"Neural Magic DeepSparse 1.5 - Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",3218.6925,3235.4982
"b",3237.4687,3230.4489
"Neural Magic DeepSparse 1.5 - Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",4.9544,4.9288
"b",4.9255,4.9368
"Neural Magic DeepSparse 1.5 - Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",208.8019,208.993
"b",208.945,209.0366
"Neural Magic DeepSparse 1.5 - Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",76.5946,76.5248
"b",76.4254,76.5114
"Neural Magic DeepSparse 1.5 - Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",35.1442,35.1615
"b",36.9411,37.7094
"Neural Magic DeepSparse 1.5 - Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",453.2205,453.7399
"b",433.0815,424.2574
"Neural Magic DeepSparse 1.5 - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",478.9566,478.8649
"b",479.2405,479.2077
"Neural Magic DeepSparse 1.5 - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",33.3861,33.3927
"b",33.3666,33.3693
"Neural Magic DeepSparse 1.5 - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",208.5118,209.1823
"b",211.1048,211.3492
"Neural Magic DeepSparse 1.5 - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",76.7065,76.4548
"b",75.7657,75.6778
"Neural Magic DeepSparse 1.5 - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",295.7816,295.8774
"b",299.4136,300.4417
"Neural Magic DeepSparse 1.5 - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",54.0763,54.0585
"b",53.4206,53.2375
"Neural Magic DeepSparse 1.5 - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",46.3484,46.313
"b",46.7465,46.3516
"Neural Magic DeepSparse 1.5 - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",345.0015,345.2967
"b",341.8917,345.1422
"Neural Magic DeepSparse 1.5 - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",504.4356,504.7871
"b",505.0133,505.2484
"Neural Magic DeepSparse 1.5 - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",31.6807,31.6692
"b",31.6562,31.6414
"Neural Magic DeepSparse 1.5 - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",133.2759,141.48
"b",144.2352,142.6421
"Neural Magic DeepSparse 1.5 - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",119.824,112.9281
"b",110.9064,112.0888
"Neural Magic DeepSparse 1.5 - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",33.8627,34.0088
"b",34.5105,34.5788
"Neural Magic DeepSparse 1.5 - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",470.2687,467.3405
"b",460.8699,460.4715
"OpenFOAM 10 - Input: drivaerFastback, Small Mesh Size - Mesh Time",
Lower Results Are Better
"a",27.94937,27.981057
"b",27.895792,28.001641
"OpenFOAM 10 - Input: drivaerFastback, Small Mesh Size - Execution Time",
Lower Results Are Better
"a",67.801397,67.613264
"b",67.45108,67.675246
"OpenFOAM 10 - Input: drivaerFastback, Medium Mesh Size - Mesh Time",
Lower Results Are Better
"a",144.70439,144.68853
"b",144.8586,145.01487
"OpenFOAM 10 - Input: drivaerFastback, Medium Mesh Size - Execution Time",
Lower Results Are Better
"a",615.56868,616.4128
"b",615.43051,615.48985
"Palabos 2.3 - Grid Size: 100",
Higher Results Are Better
"a",235.171,235.201
"b",234.532,235.216
"Palabos 2.3 - Grid Size: 400",
Higher Results Are Better
"a",287.756,286.78
"b",284.217,287.304
"Palabos 2.3 - Grid Size: 500",
Higher Results Are Better
"a",301.902,298.649
"b",302.023,299.687
"Redis 7.0.12 + memtier_benchmark 2.0 - Protocol: Redis - Clients: 50 - Set To Get Ratio: 1:5",
Higher Results Are Better
"a",2179789.85,2243487.45
"b",2256196.16,2178188.08
"Redis 7.0.12 + memtier_benchmark 2.0 - Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:5",
Higher Results Are Better
"a",2291996.8,2279995.54
"b",2223161.64,2231142.4
"Redis 7.0.12 + memtier_benchmark 2.0 - Protocol: Redis - Clients: 50 - Set To Get Ratio: 1:10",
Higher Results Are Better
"a",2329892.02,2302670.5
"b",2288918.68,2298016.55
"Redis 7.0.12 + memtier_benchmark 2.0 - Protocol: Redis - Clients: 500 - Set To Get Ratio: 1:5",
"a",
"b",
"Redis 7.0.12 + memtier_benchmark 2.0 - Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:10",
Higher Results Are Better
"a",2561484.78,2332699.24
"b",2291755.1,2317705.27
"Redis 7.0.12 + memtier_benchmark 2.0 - Protocol: Redis - Clients: 500 - Set To Get Ratio: 1:10",
"a",
"b",
"srsRAN Project 23.5 - Test: Downlink Processor Benchmark",
Higher Results Are Better
"a",710.9,700.6
"b",712.5,709.3
"srsRAN Project 23.5 - Test: PUSCH Processor Benchmark, Throughput Total",
Higher Results Are Better
"a",5516.2,5229.6
"b",5639.1,5448.3
"srsRAN Project 23.5 - Test: PUSCH Processor Benchmark, Throughput Thread",
Higher Results Are Better
"a",243.9,236.8
"b",236.4,236.2
"Stress-NG 0.15.10 - Test: Hash",
Higher Results Are Better
"a",5580419.27,5574085.37
"b",5586843.38,5581112.89
"Stress-NG 0.15.10 - Test: MMAP",
Higher Results Are Better
"a",864.59,857.96
"b",858.2,854.08
"Stress-NG 0.15.10 - Test: NUMA",
Higher Results Are Better
"a",389.98,391.75
"b",392.12,392.03
"Stress-NG 0.15.10 - Test: Pipe",
Higher Results Are Better
"a",34732461.75,36942961.94
"b",36932422.22,36773160.02
"Stress-NG 0.15.10 - Test: Poll",
Higher Results Are Better
"a",3666744.93,3671818.45
"b",3669664.43,3673571.51
"Stress-NG 0.15.10 - Test: Zlib",
Higher Results Are Better
"a",2647.86,2647.75
"b",2649.46,2648.15
"Stress-NG 0.15.10 - Test: Futex",
Higher Results Are Better
"a",1598306.78,1485045.93
"b",1447593.88,1538365.03
"Stress-NG 0.15.10 - Test: MEMFD",
Higher Results Are Better
"a",551.25,548.63
"b",550.75,548.34
"Stress-NG 0.15.10 - Test: Mutex",
Higher Results Are Better
"a",15123504.04,15171384.97
"b",15195757.06,15190028.11
"Stress-NG 0.15.10 - Test: Atomic",
Higher Results Are Better
"a",134.88,132.78
"b",132.41,132.8
"Stress-NG 0.15.10 - Test: Crypto",
Higher Results Are Better
"a",50236.44,50243.74
"b",50225.35,50261.6
"Stress-NG 0.15.10 - Test: Malloc",
Higher Results Are Better
"a",99243720.29,99503228.33
"b",99167297.96,99335156.59
"Stress-NG 0.15.10 - Test: Cloning",
Higher Results Are Better
"a",9854.9,9626.24
"b",9426.25,9225.93
"Stress-NG 0.15.10 - Test: Forking",
Higher Results Are Better
"a",90387.4,89449.01
"b",90387.52,89545.05
"Stress-NG 0.15.10 - Test: Pthread",
Higher Results Are Better
"a",135874.23,137817.79
"b",136607.74,136811.87
"Stress-NG 0.15.10 - Test: AVL Tree",
Higher Results Are Better
"a",294.58,293.94
"b",293.81,295.51
"Stress-NG 0.15.10 - Test: IO_uring",
Higher Results Are Better
"a",1507183.64,1552148.31
"b",1508853.73,1498393.85
"Stress-NG 0.15.10 - Test: SENDFILE",
Higher Results Are Better
"a",575924.89,589524.37
"b",597929.59,598417.53
"Stress-NG 0.15.10 - Test: CPU Cache",
Higher Results Are Better
"a",1505816.25,1568406.15
"b",2120782.17,1650884.05
"Stress-NG 0.15.10 - Test: CPU Stress",
Higher Results Are Better
"a",64098.38,64123.83
"b",64079.91,64157.82
"Stress-NG 0.15.10 - Test: Semaphores",
Higher Results Are Better
"a",64203732.62,60049159.79
"b",62118078.66,61184892.2
"Stress-NG 0.15.10 - Test: Matrix Math",
Higher Results Are Better
"a",157785.87,163521.01
"b",156335.97,157000.89
"Stress-NG 0.15.10 - Test: Vector Math",
Higher Results Are Better
"a",151339.14,151433.47
"b",151425.17,151437.13
"Stress-NG 0.15.10 - Test: Function Call",
Higher Results Are Better
"a",22108.06,21948
"b",22032.4,22180.58
"Stress-NG 0.15.10 - Test: x86_64 RdRand",
Higher Results Are Better
"a",331414.17,331418.86
"b",331421.89,331424.18
"Stress-NG 0.15.10 - Test: Floating Point",
Higher Results Are Better
"a",10586.41,10588.54
"b",10618.86,10583.33
"Stress-NG 0.15.10 - Test: Matrix 3D Math",
Higher Results Are Better
"a",9634.38,9565.47
"b",9601.22,9609.37
"Stress-NG 0.15.10 - Test: Memory Copying",
Higher Results Are Better
"a",7184.89,7167.48
"b",7191.47,7169.39
"Stress-NG 0.15.10 - Test: Vector Shuffle",
Higher Results Are Better
"a",167197.57,167210.84
"b",167196.02,167208.11
"Stress-NG 0.15.10 - Test: Socket Activity",
Higher Results Are Better
"a",24874.57,25019.71
"b",25549.69,25014.92
"Stress-NG 0.15.10 - Test: Wide Vector Math",
Higher Results Are Better
"a",1745947.34,1744111.19
"b",1754143.06,1745863.8
"Stress-NG 0.15.10 - Test: Context Switching",
Higher Results Are Better
"a",2573480.32,2572123.18
"b",2571696.86,2570488.52
"Stress-NG 0.15.10 - Test: Fused Multiply-Add",
Higher Results Are Better
"a",34060074.15,34335337.11
"b",34050383.6,34050954.85
"Stress-NG 0.15.10 - Test: Vector Floating Point",
Higher Results Are Better
"a",58274.09,58212.66
"b",58228.59,58236.81
"Stress-NG 0.15.10 - Test: Glibc C String Functions",
Higher Results Are Better
"a",26217977.85,25916743.35
"b",26055885.02,26194544.65
"Stress-NG 0.15.10 - Test: Glibc Qsort Data Sorting",
Higher Results Are Better
"a",696.25,697.05
"b",696.45,697.38
"Stress-NG 0.15.10 - Test: System V Message Passing",
Higher Results Are Better
"a",5845106.73,5859456.69
"b",5844398.83,5864004.72
"Timed GDB GNU Debugger Compilation 10.2 - Time To Compile",
Lower Results Are Better
"a",41.964,41.846
"b",41.888,42.124
"Timed Linux Kernel Compilation 6.1 - Build: defconfig",
Lower Results Are Better
"a",41.162,39.713
"b",41.14,39.762
"Timed Linux Kernel Compilation 6.1 - Build: allmodconfig",
Lower Results Are Better
"a",446.849,443.921
"b",446.515,444.245
"Timed LLVM Compilation 16.0 - Build System: Ninja",
Lower Results Are Better
"a",263.301,263.006
"b",263.038,262.729
"Timed LLVM Compilation 16.0 - Build System: Unix Makefiles",
Lower Results Are Better
"a",318.774,328.938
"b",313.976,325.728
"Timed PHP Compilation 8.1.9 - Time To Compile",
Lower Results Are Better
"a",42.693,42.009
"b",42.859,41.904
"VVenC 1.9 - Video Input: Bosphorus 4K - Video Preset: Fast",
Higher Results Are Better
"a",5.768,5.916
"b",5.902,5.931
"VVenC 1.9 - Video Input: Bosphorus 4K - Video Preset: Faster",
Higher Results Are Better
"a",11.015,11.024
"b",10.963,11.021
"VVenC 1.9 - Video Input: Bosphorus 1080p - Video Preset: Fast",
Higher Results Are Better
"a",15.934,16.265
"b",16.224,16.273
"VVenC 1.9 - Video Input: Bosphorus 1080p - Video Preset: Faster",
Higher Results Are Better
"a",30.89,31.001
"b",30.883,30.971