threadripper eo 2022

Tests for a future article. AMD Ryzen Threadripper 3960X 24-Core testing with a MSI Creator TRX40 (MS-7C59) v1.0 (1.12N1 BIOS) and Gigabyte AMD Radeon RX 5500 XT 8GB 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 2212279-NE-THREADRIP52
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Audio Encoding 2 Tests
AV1 4 Tests
C++ Boost Tests 2 Tests
Timed Code Compilation 6 Tests
C/C++ Compiler Tests 9 Tests
CPU Massive 17 Tests
Creator Workloads 19 Tests
Cryptography 2 Tests
Database Test Suite 7 Tests
Encoding 7 Tests
Game Development 3 Tests
HPC - High Performance Computing 12 Tests
Imaging 6 Tests
Common Kernel Benchmarks 3 Tests
Machine Learning 9 Tests
Multi-Core 19 Tests
NVIDIA GPU Compute 2 Tests
Intel oneAPI 3 Tests
OpenMPI Tests 3 Tests
Programmer / Developer System Benchmarks 6 Tests
Python 2 Tests
Renderers 2 Tests
Rust Tests 2 Tests
Server 8 Tests
Server CPU Tests 11 Tests
Single-Threaded 3 Tests
Video Encoding 5 Tests
Common Workstation Benchmarks 2 Tests

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December 26 2022
  7 Hours, 56 Minutes
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December 26 2022
  8 Hours
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threadripper eo 2022 Tests for a future article. AMD Ryzen Threadripper 3960X 24-Core testing with a MSI Creator TRX40 (MS-7C59) v1.0 (1.12N1 BIOS) and Gigabyte AMD Radeon RX 5500 XT 8GB on Ubuntu 22.04 via the Phoronix Test Suite. ,,"a","b" Processor,,AMD Ryzen Threadripper 3960X 24-Core @ 3.80GHz (24 Cores / 48 Threads),AMD Ryzen Threadripper 3960X 24-Core @ 3.80GHz (24 Cores / 48 Threads) Motherboard,,MSI Creator TRX40 (MS-7C59) v1.0 (1.12N1 BIOS),MSI Creator TRX40 (MS-7C59) v1.0 (1.12N1 BIOS) Chipset,,AMD Starship/Matisse,AMD Starship/Matisse Memory,,32GB,32GB Disk,,1000GB Sabrent Rocket 4.0 1TB,1000GB Sabrent Rocket 4.0 1TB Graphics,,Gigabyte AMD Radeon RX 5500 XT 8GB (1900/875MHz),Gigabyte AMD Radeon RX 5500 XT 8GB (1900/875MHz) Audio,,AMD Navi 10 HDMI Audio,AMD Navi 10 HDMI Audio Monitor,,VA2431,VA2431 Network,,Aquantia AQC107 NBase-T/IEEE + Intel I211 + Intel Wi-Fi 6 AX200,Aquantia AQC107 NBase-T/IEEE + Intel I211 + Intel Wi-Fi 6 AX200 OS,,Ubuntu 22.04,Ubuntu 22.04 Kernel,,5.19.0-051900rc7-generic (x86_64),5.19.0-051900rc7-generic (x86_64) Desktop,,GNOME Shell 42.2,GNOME Shell 42.2 Display Server,,X Server,X Server OpenGL,,4.6 Mesa 22.0.1 (LLVM 13.0.1 DRM 3.47),4.6 Mesa 22.0.1 (LLVM 13.0.1 DRM 3.47) Vulkan,,1.3.204,1.3.204 Compiler,,GCC 11.2.0,GCC 11.2.0 File-System,,ext4,ext4 Screen Resolution,,1920x1080,1920x1080 ,,"a","b" "7-Zip Compression - Test: Compression Rating (MIPS)",HIB,167681,168680 "7-Zip Compression - Test: Decompression Rating (MIPS)",HIB,177640,170171 "AOM AV1 - Encoder Mode: Speed 0 Two-Pass - Input: Bosphorus 4K (FPS)",HIB,0.34,0.34 "AOM AV1 - Encoder Mode: Speed 4 Two-Pass - Input: Bosphorus 4K (FPS)",HIB,7.91,7.88 "AOM AV1 - Encoder Mode: Speed 6 Realtime - Input: Bosphorus 4K (FPS)",HIB,25.31,24.61 "AOM AV1 - Encoder Mode: Speed 6 Two-Pass - Input: Bosphorus 4K (FPS)",HIB,13.54,13.57 "AOM AV1 - Encoder Mode: Speed 8 Realtime - Input: Bosphorus 4K (FPS)",HIB,31.97,32.16 "AOM AV1 - Encoder Mode: Speed 9 Realtime - Input: Bosphorus 4K (FPS)",HIB,40.37,40.93 "AOM AV1 - Encoder Mode: Speed 10 Realtime - Input: Bosphorus 4K (FPS)",HIB,40.29,40.76 "AOM AV1 - Encoder Mode: Speed 0 Two-Pass - Input: Bosphorus 1080p (FPS)",HIB,0.96,0.97 "AOM AV1 - Encoder Mode: Speed 4 Two-Pass - Input: Bosphorus 1080p (FPS)",HIB,14.13,14.23 "AOM AV1 - Encoder Mode: Speed 6 Realtime - Input: Bosphorus 1080p (FPS)",HIB,43.08,43.34 "AOM AV1 - Encoder Mode: Speed 6 Two-Pass - Input: Bosphorus 1080p (FPS)",HIB,34.6,34.15 "AOM AV1 - Encoder Mode: Speed 8 Realtime - Input: Bosphorus 1080p (FPS)",HIB,60.72,62.08 "AOM AV1 - Encoder Mode: Speed 9 Realtime - Input: Bosphorus 1080p (FPS)",HIB,72.59,73.28 "AOM AV1 - Encoder Mode: Speed 10 Realtime - Input: Bosphorus 1080p (FPS)",HIB,72.52,73.76 "Apache Spark - Row Count: 1000000 - Partitions: 100 - SHA-512 Benchmark Time (sec)",LIB,3.40,3.59872799 "Apache Spark - Row Count: 1000000 - Partitions: 100 - Calculate Pi Benchmark (sec)",LIB,68.960273121,69.93 "Apache Spark - Row Count: 1000000 - Partitions: 100 - Calculate Pi Benchmark Using Dataframe (sec)",LIB,4.20,4.21 "Apache Spark - Row Count: 1000000 - Partitions: 100 - Group By Test Time (sec)",LIB,4.67,4.78 "Apache Spark - Row Count: 1000000 - Partitions: 100 - Repartition Test Time (sec)",LIB,1.76,1.84 "Apache Spark - Row Count: 1000000 - Partitions: 100 - Inner Join Test Time (sec)",LIB,1.67,1.63 "Apache Spark - Row Count: 1000000 - Partitions: 100 - Broadcast Inner Join Test Time (sec)",LIB,1.28,1.34 "ASTC Encoder - Preset: Fast (MT/s)",HIB,366.3281,364.3208 "ASTC Encoder - Preset: Medium (MT/s)",HIB,120.3254,120.0841 "ASTC Encoder - Preset: Thorough (MT/s)",HIB,14.4059,14.3759 "ASTC Encoder - Preset: Exhaustive (MT/s)",HIB,1.5739,1.5693 "Blender - Blend File: BMW27 - Compute: CPU-Only (sec)",LIB,53.91,53.95 "Blender - Blend File: Classroom - Compute: CPU-Only (sec)",LIB,148.83,149.1 "Blender - Blend File: Fishy Cat - Compute: CPU-Only (sec)",LIB,69.44,69.59 "Blender - Blend File: Barbershop - Compute: CPU-Only (sec)",LIB,583.81,586 "Blender - Blend File: Pabellon Barcelona - Compute: CPU-Only (sec)",LIB,176.4,177.27 "BRL-CAD - VGR Performance Metric (VGR Performance Metric)",HIB,405453,403757 "ClickHouse - 100M Rows Web Analytics Dataset, First Run / Cold Cache (Queries/min, Geo Mean)",HIB,196.74,201.86 "ClickHouse - 100M Rows Web Analytics Dataset, Second Run (Queries/min, Geo Mean)",HIB,236.78,234.64 "ClickHouse - 100M Rows Web Analytics Dataset, Third Run (Queries/min, Geo Mean)",HIB,240.17,235.82 "CockroachDB - Workload: MoVR - Concurrency: 128 (ops/s)",HIB,477.5,483.1 "CockroachDB - Workload: MoVR - Concurrency: 256 (ops/s)",HIB,476.5,480.7 "CockroachDB - Workload: MoVR - Concurrency: 512 (ops/s)",HIB,476.6,481.1 "CockroachDB - Workload: MoVR - Concurrency: 1024 (ops/s)",HIB,474.9,480.5 "CockroachDB - Workload: KV, 10% Reads - Concurrency: 128 (ops/s)",HIB,50479.2,50108.8 "CockroachDB - Workload: KV, 10% Reads - Concurrency: 256 (ops/s)",HIB,61211.1,61278.6 "CockroachDB - Workload: KV, 10% Reads - Concurrency: 512 (ops/s)",HIB,61112,60710 "CockroachDB - Workload: KV, 50% Reads - Concurrency: 128 (ops/s)",HIB,70558.3,69948.7 "CockroachDB - Workload: KV, 50% Reads - Concurrency: 256 (ops/s)",HIB,77546.7,76768.6 "CockroachDB - Workload: KV, 50% Reads - Concurrency: 512 (ops/s)",HIB,75643.3,75289.1 "CockroachDB - Workload: KV, 60% Reads - Concurrency: 128 (ops/s)",HIB,77055.3,77363.2 "CockroachDB - Workload: KV, 60% Reads - Concurrency: 256 (ops/s)",HIB,82286.1,82467 "CockroachDB - Workload: KV, 60% Reads - Concurrency: 512 (ops/s)",HIB,80024,79220.4 "CockroachDB - Workload: KV, 95% Reads - Concurrency: 128 (ops/s)",HIB,103073.5,103111.7 "CockroachDB - Workload: KV, 95% Reads - Concurrency: 256 (ops/s)",HIB,100761.5,100166.8 "CockroachDB - Workload: KV, 95% Reads - Concurrency: 512 (ops/s)",HIB,98031.9,97822.4 "CockroachDB - Workload: KV, 10% Reads - Concurrency: 1024 (ops/s)",HIB,59301.4,59175.4 "CockroachDB - Workload: KV, 50% Reads - Concurrency: 1024 (ops/s)",HIB,71952.1,72222 "CockroachDB - Workload: KV, 60% Reads - Concurrency: 1024 (ops/s)",HIB,76564,76335.4 "CockroachDB - Workload: KV, 95% Reads - Concurrency: 1024 (ops/s)",HIB,94157.8,93822.1 "Dragonflydb - Clients: 50 - Set To Get Ratio: 1:1 (Ops/sec)",HIB,3341824.71,3320752.26 "Dragonflydb - Clients: 50 - Set To Get Ratio: 1:5 (Ops/sec)",HIB,3519377.56,3515736.22 "Dragonflydb - Clients: 50 - Set To Get Ratio: 5:1 (Ops/sec)",HIB,3193072.56,3164997.08 "Dragonflydb - Clients: 200 - Set To Get Ratio: 1:1 (Ops/sec)",HIB,3388128.93,3388656.73 "Dragonflydb - Clients: 200 - Set To Get Ratio: 1:5 (Ops/sec)",HIB,3609156.11,3593257.69 "Dragonflydb - Clients: 200 - Set To Get Ratio: 5:1 (Ops/sec)",HIB,3273370.07,3263145.89 "EnCodec - Target Bandwidth: 3 kbps (sec)",LIB,38.187,38.433 "EnCodec - Target Bandwidth: 6 kbps (sec)",LIB,37.604,37.903 "EnCodec - Target Bandwidth: 24 kbps (sec)",LIB,42.075,42.307 "EnCodec - Target Bandwidth: 1.5 kbps (sec)",LIB,36.013,36.372 "Facebook RocksDB - Test: Random Fill (Op/s)",HIB,811216,800273 "Facebook RocksDB - Test: Random Read (Op/s)",HIB,98049490,100100766 "Facebook RocksDB - Test: Update Random (Op/s)",HIB,684330,651892 "Facebook RocksDB - Test: Sequential Fill (Op/s)",HIB,922415,876834 "Facebook RocksDB - Test: Random Fill Sync (Op/s)",HIB,25136,25297 "Facebook RocksDB - Test: Read While Writing (Op/s)",HIB,4506140,4452658 "Facebook RocksDB - Test: Read Random Write Random (Op/s)",HIB,2683538,2589021 "FFmpeg - Encoder: libx264 - Scenario: Live (sec)",LIB,25.43,25.19 "FFmpeg - Encoder: libx264 - Scenario: Live (FPS)",HIB,198.58,200.46 "FFmpeg - Encoder: libx265 - Scenario: Live (sec)",LIB,72.95,73.03 "FFmpeg - Encoder: libx265 - Scenario: Live (FPS)",HIB,69.22,69.15 "FFmpeg - Encoder: libx264 - Scenario: Upload (sec)",LIB,204.11375323,205.24 "FFmpeg - Encoder: libx264 - Scenario: Upload (FPS)",HIB,12.37,12.30 "FFmpeg - Encoder: libx265 - Scenario: Upload (sec)",LIB,183.908496366,181.78 "FFmpeg - Encoder: libx265 - Scenario: Upload (FPS)",HIB,13.73,13.89 "FFmpeg - Encoder: libx264 - Scenario: Platform (sec)",LIB,160.71,161.32 "FFmpeg - Encoder: libx264 - Scenario: Platform (FPS)",HIB,47.13,46.96 "FFmpeg - Encoder: libx265 - Scenario: Platform (sec)",LIB,264.66,264.16 "FFmpeg - Encoder: libx265 - Scenario: Platform (FPS)",HIB,28.62,28.68 "FFmpeg - Encoder: libx264 - Scenario: Video On Demand (sec)",LIB,160.60,161.04 "FFmpeg - Encoder: libx264 - Scenario: Video On Demand (FPS)",HIB,47.17,47.04 "FFmpeg - Encoder: libx265 - Scenario: Video On Demand (sec)",LIB,264.63,263.53 "FFmpeg - Encoder: libx265 - Scenario: Video On Demand (FPS)",HIB,28.63,28.74 "FLAC Audio Encoding - WAV To FLAC (sec)",LIB,17.802,17.963 "GraphicsMagick - Operation: Swirl (Iterations/min)",HIB,1178,1193 "GraphicsMagick - Operation: Rotate (Iterations/min)",HIB,638,649 "GraphicsMagick - Operation: Sharpen (Iterations/min)",HIB,376,376 "GraphicsMagick - Operation: Enhanced (Iterations/min)",HIB,568,570 "GraphicsMagick - Operation: Resizing (Iterations/min)",HIB,2129,2132 "GraphicsMagick - Operation: Noise-Gaussian (Iterations/min)",HIB,514,535 "GraphicsMagick - Operation: HWB Color Space (Iterations/min)",HIB,1043,1089 "JPEG XL Decoding libjxl - CPU Threads: 1 (MP/s)",HIB,44.6,45.67 "JPEG XL Decoding libjxl - CPU Threads: All (MP/s)",HIB,245.68,251.91 "JPEG XL libjxl - Input: PNG - Quality: 80 (MP/s)",HIB,8.95,9.02 "JPEG XL libjxl - Input: PNG - Quality: 90 (MP/s)",HIB,8.89,8.99 "JPEG XL libjxl - Input: JPEG - Quality: 80 (MP/s)",HIB,8.59,8.7 "JPEG XL libjxl - Input: JPEG - Quality: 90 (MP/s)",HIB,8.51,8.58 "JPEG XL libjxl - Input: PNG - Quality: 100 (MP/s)",HIB,0.68,0.68 "JPEG XL libjxl - Input: JPEG - Quality: 100 (MP/s)",HIB,0.66,0.66 "libavif avifenc - Encoder Speed: 0 (sec)",LIB,91.039,90.561 "libavif avifenc - Encoder Speed: 2 (sec)",LIB,47.559,47.639 "libavif avifenc - Encoder Speed: 6 (sec)",LIB,4.012,4.008 "libavif avifenc - Encoder Speed: 6, Lossless (sec)",LIB,7.607,7.571 "libavif avifenc - Encoder Speed: 10, Lossless (sec)",LIB,5.153,5.078 "Mobile Neural Network - Model: nasnet (ms)",LIB,14.311,14.962 "Mobile Neural Network - Model: mobilenetV3 (ms)",LIB,2.282,2.455 "Mobile Neural Network - Model: squeezenetv1.1 (ms)",LIB,4.311,4.191 "Mobile Neural Network - Model: resnet-v2-50 (ms)",LIB,21.564,22.828 "Mobile Neural Network - Model: SqueezeNetV1.0 (ms)",LIB,6.491,6.547 "Mobile Neural Network - Model: MobileNetV2_224 (ms)",LIB,4.361,4.607 "Mobile Neural Network - Model: mobilenet-v1-1.0 (ms)",LIB,3.054,3.021 "Mobile Neural Network - Model: inception-v3 (ms)",LIB,23.254,25.51 "Natron - Input: Spaceship (FPS)",HIB,4.2,4.2 "NCNN - Target: CPU - Model: mobilenet (ms)",LIB,15.69,16.39 "NCNN - Target: CPU-v2-v2 - Model: mobilenet-v2 (ms)",LIB,6.92,7.13 "NCNN - Target: CPU-v3-v3 - Model: mobilenet-v3 (ms)",LIB,6.44,6.62 "NCNN - Target: CPU - Model: shufflenet-v2 (ms)",LIB,7.89,8.14 "NCNN - Target: CPU - Model: mnasnet (ms)",LIB,6.29,6.54 "NCNN - Target: CPU - Model: efficientnet-b0 (ms)",LIB,9.26,9.56 "NCNN - Target: CPU - Model: blazeface (ms)",LIB,3.67,3.75 "NCNN - Target: CPU - Model: googlenet (ms)",LIB,18.44,19.69 "NCNN - Target: CPU - Model: vgg16 (ms)",LIB,33.71,34.14 "NCNN - Target: CPU - Model: resnet18 (ms)",LIB,12.14,12.78 "NCNN - Target: CPU - Model: alexnet (ms)",LIB,8.95,9.45 "NCNN - Target: CPU - Model: resnet50 (ms)",LIB,21.43,23.05 "NCNN - Target: CPU - Model: yolov4-tiny (ms)",LIB,25.55,26.29 "NCNN - Target: CPU - Model: squeezenet_ssd (ms)",LIB,21.24,22.44 "NCNN - Target: CPU - Model: regnety_400m (ms)",LIB,26.61,26.65 "NCNN - Target: CPU - Model: vision_transformer (ms)",LIB,133.48,137.59 "NCNN - Target: CPU - Model: FastestDet (ms)",LIB,8.91,8.9 "nekRS - Input: TurboPipe Periodic (FLOP/s)",HIB,67648400000,67381400000 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,19.6428,19.5842 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,608.4782,610.7512 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream (items/sec)",HIB,13.3862,13.3177 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream (ms/batch)",LIB,74.6969,75.0804 "Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,69.4812,67.6453 "Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,172.6456,177.2167 "Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream (items/sec)",HIB,26.7006,27.4093 "Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,37.4433,36.475 "Neural Magic DeepSparse - Model: CV Detection,YOLOv5s COCO - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,107.4771,106.8673 "Neural Magic DeepSparse - Model: CV Detection,YOLOv5s COCO - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,111.3907,112.0146 "Neural Magic DeepSparse - Model: CV Detection,YOLOv5s COCO - Scenario: Synchronous Single-Stream (items/sec)",HIB,59.8698,59.5355 "Neural Magic DeepSparse - Model: CV Detection,YOLOv5s COCO - Scenario: Synchronous Single-Stream (ms/batch)",LIB,16.6914,16.7848 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,222.1611,221.8827 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,53.9562,54.0192 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream (items/sec)",HIB,112.6594,113.5482 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream (ms/batch)",LIB,8.8688,8.7988 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,154.8854,154.7593 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,77.4472,77.5099 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream (items/sec)",HIB,83.6707,83.5943 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream (ms/batch)",LIB,11.9444,11.9559 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,78.7202,78.4402 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,152.2314,152.9447 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream (items/sec)",HIB,43.1635,41.0795 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,23.1605,24.336 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,19.5601,19.5237 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,611.8922,610.9332 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream (items/sec)",HIB,13.3472,13.326 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,74.9151,75.0343 "nginx - Connections: 1 (Reqs/sec)",HIB,, "nginx - Connections: 20 (Reqs/sec)",HIB,, "nginx - Connections: 100 (Reqs/sec)",HIB,148017.08,145523.99 "nginx - Connections: 200 (Reqs/sec)",HIB,148446.23,148101.85 "nginx - Connections: 500 (Reqs/sec)",HIB,133262.41,137072.73 "nginx - Connections: 1000 (Reqs/sec)",HIB,125263.22,126633 "nginx - Connections: 4000 (Reqs/sec)",HIB,, "Numenta Anomaly Benchmark - Detector: KNN CAD (sec)",LIB,129.147,133.475 "Numenta Anomaly Benchmark - Detector: Relative Entropy (sec)",LIB,13.21,13.106 "Numenta Anomaly Benchmark - Detector: Windowed Gaussian (sec)",LIB,6.199,6.327 "Numenta Anomaly Benchmark - Detector: Earthgecko Skyline (sec)",LIB,77.22,77.408 "Numenta Anomaly Benchmark - Detector: Bayesian Changepoint (sec)",LIB,20.148,20.56 "Numenta Anomaly Benchmark - Detector: Contextual Anomaly Detector OSE (sec)",LIB,38.983,39.16 "oneDNN - Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU (ms)",LIB,1.50644,1.54322 "oneDNN - Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU (ms)",LIB,5.89944,5.91961 "oneDNN - Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,1.43493,1.47047 "oneDNN - Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,0.652318,0.7778 "oneDNN - Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,, "oneDNN - Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,, "oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU (ms)",LIB,9.02473,9.42618 "oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU (ms)",LIB,6.06539,6.16553 "oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU (ms)",LIB,2.63187,2.69706 "oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,9.16751,9.15733 "oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,1.68952,1.74252 "oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,2.06629,2.09863 "oneDNN - Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU (ms)",LIB,2614.46,2759.2 "oneDNN - Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU (ms)",LIB,1295.49,1451.46 "oneDNN - Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,2559.09,2719.77 "oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,, "oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,, "oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,, "oneDNN - Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,1366.14,1446.12 "oneDNN - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU (ms)",LIB,4.48456,4.27034 "oneDNN - Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,2604.63,2772.01 "oneDNN - Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,1361.72,1441.78 "oneDNN - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,6.54191,4.7447 "oneDNN - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,, "OpenFOAM - Input: motorBike - Mesh Time (sec)",LIB,40.3729,40.5873 "OpenFOAM - Input: motorBike - Execution Time (sec)",LIB,71.6006,71.6125 "OpenFOAM - Input: drivaerFastback, Small Mesh Size - Mesh Time (sec)",LIB,26.871733,27.275142 "OpenFOAM - Input: drivaerFastback, Small Mesh Size - Execution Time (sec)",LIB,121.45626,123.71063 "OpenRadioss - Model: Bumper Beam (sec)",LIB,98.86,100.6 "OpenRadioss - Model: Cell Phone Drop Test (sec)",LIB,54.86,55.19 "OpenRadioss - Model: Bird Strike on Windshield (sec)",LIB,173.48,174.41 "OpenRadioss - Model: Rubber O-Ring Seal Installation (sec)",LIB,78.18,79.08 "OpenRadioss - Model: INIVOL and Fluid Structure Interaction Drop Container (sec)",LIB,287.95,290.2 "OpenVINO - Model: Face Detection FP16 - Device: CPU (FPS)",HIB,6.82,7.02 "OpenVINO - Model: Face Detection FP16 - Device: CPU (ms)",LIB,1735.28,1690.53 "OpenVINO - Model: Person Detection FP16 - Device: CPU (FPS)",HIB,4.3,4.34 "OpenVINO - Model: Person Detection FP16 - Device: CPU (ms)",LIB,2734.68,2714.24 "OpenVINO - Model: Person Detection FP32 - Device: CPU (FPS)",HIB,4.33,4.31 "OpenVINO - Model: Person Detection FP32 - Device: CPU (ms)",LIB,2750.06,2734.21 "OpenVINO - Model: Vehicle Detection FP16 - Device: CPU (FPS)",HIB,380.28,376.7 "OpenVINO - Model: Vehicle Detection FP16 - Device: CPU (ms)",LIB,31.53,31.82 "OpenVINO - Model: Face Detection FP16-INT8 - Device: CPU (FPS)",HIB,8.46,8.48 "OpenVINO - Model: Face Detection FP16-INT8 - Device: CPU (ms)",LIB,1410.67,1408.6 "OpenVINO - Model: Vehicle Detection FP16-INT8 - Device: CPU (FPS)",HIB,715.98,712.18 "OpenVINO - Model: Vehicle Detection FP16-INT8 - Device: CPU (ms)",LIB,16.75,16.84 "OpenVINO - Model: Weld Porosity Detection FP16 - Device: CPU (FPS)",HIB,661.93,653.16 "OpenVINO - Model: Weld Porosity Detection FP16 - Device: CPU (ms)",LIB,18.11,18.36 "OpenVINO - Model: Machine Translation EN To DE FP16 - Device: CPU (FPS)",HIB,63.47,62.04 "OpenVINO - Model: Machine Translation EN To DE FP16 - Device: CPU (ms)",LIB,188.92,193.23 "OpenVINO - Model: Weld Porosity Detection FP16-INT8 - Device: CPU (FPS)",HIB,843.69,844.82 "OpenVINO - Model: Weld Porosity Detection FP16-INT8 - Device: CPU (ms)",LIB,28.43,28.4 "OpenVINO - Model: Person Vehicle Bike Detection FP16 - Device: CPU (FPS)",HIB,686.15,692.14 "OpenVINO - Model: Person Vehicle Bike Detection FP16 - Device: CPU (ms)",LIB,17.47,17.32 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU (FPS)",HIB,21060.95,20837.59 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU (ms)",LIB,1.13,1.14 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU (FPS)",HIB,22553.27,22417.03 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU (ms)",LIB,1.06,1.06 "OpenVKL - Benchmark: vklBenchmark ISPC (Items / Sec)",HIB,288,287 "OpenVKL - Benchmark: vklBenchmark Scalar (Items / Sec)",HIB,184,183 "PostgreSQL - Scaling Factor: 100 - Clients: 50 - Mode: Read Only (TPS)",HIB,671452,663640 "PostgreSQL - Scaling Factor: 100 - Clients: 50 - Mode: Read Only - Average Latency (ms)",LIB,0.074,0.075 "PostgreSQL - Scaling Factor: 100 - Clients: 100 - Mode: Read Only (TPS)",HIB,653213,602288 "PostgreSQL - Scaling Factor: 100 - Clients: 100 - Mode: Read Only - Average Latency (ms)",LIB,0.153,0.166 "PostgreSQL - Scaling Factor: 100 - Clients: 250 - Mode: Read Only (TPS)",HIB,646832,618774 "PostgreSQL - Scaling Factor: 100 - Clients: 250 - Mode: Read Only - Average Latency (ms)",LIB,0.386,0.404 "PostgreSQL - Scaling Factor: 100 - Clients: 50 - Mode: Read Write (TPS)",HIB,29212,29318 "PostgreSQL - Scaling Factor: 100 - Clients: 50 - Mode: Read Write - Average Latency (ms)",LIB,1.712,1.705 "PostgreSQL - Scaling Factor: 100 - Clients: 100 - Mode: Read Write (TPS)",HIB,40535,33658 "PostgreSQL - Scaling Factor: 100 - Clients: 100 - Mode: Read Write - Average Latency (ms)",LIB,2.467,2.971 "PostgreSQL - Scaling Factor: 100 - Clients: 250 - Mode: Read Write (TPS)",HIB,45407,25548 "PostgreSQL - Scaling Factor: 100 - Clients: 250 - Mode: Read Write - Average Latency (ms)",LIB,5.506,9.786 "rav1e - Speed: 1 (FPS)",HIB,0.78,0.778 "rav1e - Speed: 5 (FPS)",HIB,2.741,2.748 "rav1e - Speed: 6 (FPS)",HIB,3.656,3.641 "rav1e - Speed: 10 (FPS)",HIB,8.018,7.997 "Redis - Test: GET - Parallel Connections: 50 (Reqs/sec)",HIB,2481064.75,2015000.12 "Redis - Test: SET - Parallel Connections: 50 (Reqs/sec)",HIB,1746939.25,1590308.75 "Redis - Test: GET - Parallel Connections: 500 (Reqs/sec)",HIB,2271735.25,2214977.25 "Redis - Test: SET - Parallel Connections: 500 (Reqs/sec)",HIB,1784408,1797429.5 "Scikit-Learn - Benchmark: MNIST Dataset (sec)",LIB,103.896,104.931 "Scikit-Learn - Benchmark: TSNE MNIST Dataset (sec)",LIB,31.586,31.926 "Scikit-Learn - Benchmark: Sparse Random Projections, 100 Iterations (sec)",LIB,162.422,162.588 "SMHasher - Hash: wyhash (MiB/sec)",HIB,23068.13,22791.83 "SMHasher - Hash: wyhash (cycles/hash)",LIB,26.329,26.387 "SMHasher - Hash: SHA3-256 (MiB/sec)",HIB,146.7,146.47 "SMHasher - Hash: SHA3-256 (cycles/hash)",LIB,2649.376,2649.302 "SMHasher - Hash: Spooky32 (MiB/sec)",HIB,14254.7,14206.73 "SMHasher - Hash: Spooky32 (cycles/hash)",LIB,51.661,51.627 "SMHasher - Hash: fasthash32 (MiB/sec)",HIB,6591.27,6553.7 "SMHasher - Hash: fasthash32 (cycles/hash)",LIB,37.175,37.264 "SMHasher - Hash: FarmHash128 (MiB/sec)",HIB,15823.93,15708.02 "SMHasher - Hash: FarmHash128 (cycles/hash)",LIB,63.803,63.864 "SMHasher - Hash: t1ha2_atonce (MiB/sec)",HIB,15569.87,15465.65 "SMHasher - Hash: t1ha2_atonce (cycles/hash)",LIB,34.763,35.019 "SMHasher - Hash: FarmHash32 x86_64 AVX (MiB/sec)",HIB,27249.95,26861.97 "SMHasher - Hash: FarmHash32 x86_64 AVX (cycles/hash)",LIB,43.284,43.29 "SMHasher - Hash: t1ha0_aes_avx2 x86_64 (MiB/sec)",HIB,66642.16,65731.67 "SMHasher - Hash: t1ha0_aes_avx2 x86_64 (cycles/hash)",LIB,34.721,35.155 "SMHasher - Hash: MeowHash x86_64 AES-NI (MiB/sec)",HIB,37533.1,37302 "SMHasher - Hash: MeowHash x86_64 AES-NI (cycles/hash)",LIB,57.723,57.928 "spaCy - Model: en_core_web_lg (tokens/sec)",HIB,12454,12319 "spaCy - Model: en_core_web_trf (tokens/sec)",HIB,1465,1463 "srsRAN - Test: OFDM_Test (Samples / Second)",HIB,140100000,144000000 "srsRAN - Test: 4G PHY_DL_Test 100 PRB MIMO 64-QAM (eNb Mb/s)",HIB,376.6,375.1 "srsRAN - Test: 4G PHY_DL_Test 100 PRB MIMO 64-QAM (UE Mb/s)",HIB,150.6,150.1 "srsRAN - Test: 4G PHY_DL_Test 100 PRB SISO 64-QAM (eNb Mb/s)",HIB,376.6,375.9 "srsRAN - Test: 4G PHY_DL_Test 100 PRB SISO 64-QAM (UE Mb/s)",HIB,162.9,162.2 "srsRAN - Test: 4G PHY_DL_Test 100 PRB MIMO 256-QAM (eNb Mb/s)",HIB,402.5,403.8 "srsRAN - Test: 4G PHY_DL_Test 100 PRB MIMO 256-QAM (UE Mb/s)",HIB,160.2,160.7 "srsRAN - Test: 4G PHY_DL_Test 100 PRB SISO 256-QAM (eNb Mb/s)",HIB,413.1,401.2 "srsRAN - Test: 4G PHY_DL_Test 100 PRB SISO 256-QAM (UE Mb/s)",HIB,175.5,170 "srsRAN - Test: 5G PHY_DL_NR Test 52 PRB SISO 64-QAM (eNb Mb/s)",HIB,106.8,107.1 "srsRAN - Test: 5G PHY_DL_NR Test 52 PRB SISO 64-QAM (UE Mb/s)",HIB,60.2,60.2 "Stargate Digital Audio Workstation - Sample Rate: 44100 - Buffer Size: 512 (Render Ratio)",HIB,3.892921,2.678564 "Stargate Digital Audio Workstation - Sample Rate: 96000 - Buffer Size: 512 (Render Ratio)",HIB,1.911816,1.947376 "Stargate Digital Audio Workstation - Sample Rate: 192000 - Buffer Size: 512 (Render Ratio)",HIB,1.244804,1.314658 "Stargate Digital Audio Workstation - Sample Rate: 44100 - Buffer Size: 1024 (Render Ratio)",HIB,5.223813,5.307245 "Stargate Digital Audio Workstation - Sample Rate: 480000 - Buffer Size: 512 (Render Ratio)",HIB,4.122029,2.722708 "Stargate Digital Audio Workstation - Sample Rate: 96000 - Buffer Size: 1024 (Render Ratio)",HIB,3.681535,3.845343 "Stargate Digital Audio Workstation - Sample Rate: 192000 - Buffer Size: 1024 (Render Ratio)",HIB,2.436915,2.268748 "Stargate Digital Audio Workstation - Sample Rate: 480000 - Buffer Size: 1024 (Render Ratio)",HIB,4.917282,5.196953 "Stream - Type: Copy (MB/s)",HIB,54649.6,55314 "Stream - Type: Scale (MB/s)",HIB,33038,33173.5 "Stream - Type: Triad (MB/s)",HIB,36787.1,37021.8 "Stream - Type: Add (MB/s)",HIB,36849.4,37116 "Stress-NG - Test: MMAP (Bogo Ops/s)",HIB,383.32,382.19 "Stress-NG - Test: NUMA (Bogo Ops/s)",HIB,604.44,591.09 "Stress-NG - Test: Futex (Bogo Ops/s)",HIB,3430545.94,3166174.89 "Stress-NG - Test: MEMFD (Bogo Ops/s)",HIB,891.86,887.76 "Stress-NG - Test: Mutex (Bogo Ops/s)",HIB,12785778.73,12810594.01 "Stress-NG - Test: Atomic (Bogo Ops/s)",HIB,421253.99,421495.46 "Stress-NG - Test: Crypto (Bogo Ops/s)",HIB,44751.55,44653.67 "Stress-NG - Test: Malloc (Bogo Ops/s)",HIB,50825608.57,50473178.15 "Stress-NG - Test: Forking (Bogo Ops/s)",HIB,53674.14,52953.36 "Stress-NG - Test: IO_uring (Bogo Ops/s)",HIB,22416.27,17193.98 "Stress-NG - Test: SENDFILE (Bogo Ops/s)",HIB,414239.19,411184.4 "Stress-NG - Test: CPU Cache (Bogo Ops/s)",HIB,174.95,186.95 "Stress-NG - Test: CPU Stress (Bogo Ops/s)",HIB,63007.36,65071.9 "Stress-NG - Test: Semaphores (Bogo Ops/s)",HIB,4869829.14,4868774.73 "Stress-NG - Test: Matrix Math (Bogo Ops/s)",HIB,140597.25,138081.21 "Stress-NG - Test: Vector Math (Bogo Ops/s)",HIB,178859.7,178430.01 "Stress-NG - Test: x86_64 RdRand ()",,, "Stress-NG - Test: Memory Copying (Bogo Ops/s)",HIB,4863.37,4839.31 "Stress-NG - Test: Socket Activity (Bogo Ops/s)",HIB,15355.92,15707.68 "Stress-NG - Test: Context Switching (Bogo Ops/s)",HIB,9618965.78,9774697.61 "Stress-NG - Test: Glibc C String Functions (Bogo Ops/s)",HIB,4187483.04,4118591.13 "Stress-NG - Test: Glibc Qsort Data Sorting (Bogo Ops/s)",HIB,385.8,385.67 "Stress-NG - Test: System V Message Passing (Bogo Ops/s)",HIB,9343696.19,9276368.69 "SVT-AV1 - Encoder Mode: Preset 4 - Input: Bosphorus 4K (FPS)",HIB,3.5,3.521 "SVT-AV1 - Encoder Mode: Preset 8 - Input: Bosphorus 4K (FPS)",HIB,56.689,56.487 "SVT-AV1 - Encoder Mode: Preset 12 - Input: Bosphorus 4K (FPS)",HIB,147.341,129.515 "SVT-AV1 - Encoder Mode: Preset 13 - Input: Bosphorus 4K (FPS)",HIB,145.752,148.194 "SVT-AV1 - Encoder Mode: Preset 4 - Input: Bosphorus 1080p (FPS)",HIB,8.046,7.877 "SVT-AV1 - Encoder Mode: Preset 8 - Input: Bosphorus 1080p (FPS)",HIB,111.927,113.858 "SVT-AV1 - Encoder Mode: Preset 12 - Input: Bosphorus 1080p (FPS)",HIB,400.811,403.432 "SVT-AV1 - Encoder Mode: Preset 13 - Input: Bosphorus 1080p (FPS)",HIB,359.56,359.18 "TensorFlow - Device: CPU - Batch Size: 16 - Model: AlexNet (images/sec)",HIB,62.47,63.15 "TensorFlow - Device: CPU - Batch Size: 16 - Model: GoogLeNet (images/sec)",HIB,44.45,44.39 "Timed CPython Compilation - Build Configuration: Default (sec)",LIB,15.429,15.41 "Timed CPython Compilation - Build Configuration: Released Build, PGO + LTO Optimized (sec)",LIB,257.57,257.945 "Timed Erlang/OTP Compilation - Time To Compile (sec)",LIB,90.928,90.249 "Timed Linux Kernel Compilation - Build: defconfig (sec)",LIB,43.944,44.003 "Timed Linux Kernel Compilation - Build: allmodconfig (sec)",LIB,476.667,478.887 "Timed Node.js Compilation - Time To Compile (sec)",LIB,232.345,233.722 "Timed PHP Compilation - Time To Compile (sec)",LIB,43.61,43.699 "Timed Wasmer Compilation - Time To Compile (sec)",LIB,46.857,46.955 "Unpacking The Linux Kernel - linux-5.19.tar.xz (sec)",LIB,7.006,7.096 "Unvanquished - Resolution: 1920 x 1080 - Effects Quality: High (FPS)",HIB,257,263.3 "Unvanquished - Resolution: 1920 x 1080 - Effects Quality: Ultra (FPS)",HIB,248.6,286.5 "Unvanquished - Resolution: 1920 x 1080 - Effects Quality: Medium (FPS)",HIB,260,262.8 "WebP Image Encode - Encode Settings: Default (MP/s)",HIB,17.87,17.78 "WebP Image Encode - Encode Settings: Quality 100 (MP/s)",HIB,11.10,11.04 "WebP Image Encode - Encode Settings: Quality 100, Lossless (MP/s)",HIB,1.50,1.52 "WebP Image Encode - Encode Settings: Quality 100, Highest Compression (MP/s)",HIB,3.44,3.41 "WebP Image Encode - Encode Settings: Quality 100, Lossless, Highest Compression (MP/s)",HIB,0.61,0.61 "WebP2 Image Encode - Encode Settings: Default (MP/s)",HIB,9.6,10.27 "WebP2 Image Encode - Encode Settings: Quality 75, Compression Effort 7 (MP/s)",HIB,0.30,0.30 "WebP2 Image Encode - Encode Settings: Quality 95, Compression Effort 7 (MP/s)",HIB,0.15,0.15 "WebP2 Image Encode - Encode Settings: Quality 100, Compression Effort 5 (MP/s)",HIB,8.39,8.43 "Xmrig - Variant: Monero - Hash Count: 1M (H/s)",HIB,15342.8,15334.8 "Xmrig - Variant: Wownero - Hash Count: 1M (H/s)",HIB,21153.3,21018.6 "Y-Cruncher - Pi Digits To Calculate: 1B (sec)",LIB,22.825,22.916 "Y-Cruncher - Pi Digits To Calculate: 500M (sec)",LIB,11.143,11.222