new xeon

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
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C++ Boost Tests 2 Tests
Timed Code Compilation 4 Tests
C/C++ Compiler Tests 3 Tests
CPU Massive 7 Tests
Creator Workloads 3 Tests
Database Test Suite 3 Tests
Fortran Tests 2 Tests
HPC - High Performance Computing 3 Tests
Multi-Core 8 Tests
OpenMPI Tests 5 Tests
Programmer / Developer System Benchmarks 4 Tests
Python Tests 2 Tests
Software Defined Radio 2 Tests
Server 3 Tests
Server CPU Tests 5 Tests
Common Workstation Benchmarks 2 Tests

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July 30 2023
  5 Hours, 55 Minutes
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July 31 2023
  5 Hours, 22 Minutes
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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