dddxxx

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
Jump To Table - Results

View

Do Not Show Noisy Results
Do Not Show Results With Incomplete Data
Do Not Show Results With Little Change/Spread
List Notable Results

Limit displaying results to tests within:

AV1 2 Tests
Timed Code Compilation 3 Tests
C/C++ Compiler Tests 3 Tests
CPU Massive 4 Tests
Creator Workloads 8 Tests
Database Test Suite 5 Tests
Encoding 4 Tests
Game Development 2 Tests
HPC - High Performance Computing 2 Tests
Machine Learning 2 Tests
Multi-Core 10 Tests
NVIDIA GPU Compute 2 Tests
Intel oneAPI 3 Tests
Programmer / Developer System Benchmarks 3 Tests
Python Tests 4 Tests
Server 5 Tests
Server CPU Tests 4 Tests
Video Encoding 3 Tests
Vulkan Compute 2 Tests

Statistics

Show Overall Harmonic Mean(s)
Show Overall Geometric Mean
Show Geometric Means Per-Suite/Category
Show Wins / Losses Counts (Pie Chart)
Normalize Results
Remove Outliers Before Calculating Averages

Graph Settings

Force Line Graphs Where Applicable
Convert To Scalar Where Applicable
Prefer Vertical Bar Graphs

Multi-Way Comparison

Condense Multi-Option Tests Into Single Result Graphs

Table

Show Detailed System Result Table

Run Management

Highlight
Result
Hide
Result
Result
Identifier
View Logs
Performance Per
Dollar
Date
Run
  Test
  Duration
a
August 05 2023
  15 Hours, 4 Minutes
b
August 06 2023
  15 Hours, 10 Minutes
Invert Hiding All Results Option
  15 Hours, 7 Minutes
Only show results matching title/arguments (delimit multiple options with a comma):
Do not show results matching title/arguments (delimit multiple options with a comma):

dddxxx 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. ,,"a","b" Processor,,Intel Core i7-8565U @ 4.60GHz (4 Cores / 8 Threads),Intel Core i7-8565U @ 4.60GHz (4 Cores / 8 Threads) Motherboard,,Dell 0KTW76 (1.17.0 BIOS),Dell 0KTW76 (1.17.0 BIOS) Chipset,,Intel Cannon Point-LP,Intel Cannon Point-LP Memory,,16GB,16GB Disk,,SK hynix PC401 NVMe 256GB,SK hynix PC401 NVMe 256GB Graphics,,Intel UHD 620 WHL GT2 15GB (1150MHz),Intel UHD 620 WHL GT2 15GB (1150MHz) Audio,,Realtek ALC3271,Realtek ALC3271 Network,,Qualcomm Atheros QCA6174 802.11ac,Qualcomm Atheros QCA6174 802.11ac OS,,Ubuntu 22.04,Ubuntu 22.04 Kernel,,5.19.0-rc6-phx-retbleed (x86_64),5.19.0-rc6-phx-retbleed (x86_64) Desktop,,GNOME Shell 42.2,GNOME Shell 42.2 Display Server,,X Server + Wayland,X Server + Wayland OpenGL,,4.6 Mesa 22.0.1,4.6 Mesa 22.0.1 OpenCL,,OpenCL 3.0,OpenCL 3.0 Vulkan,,1.3.204,1.3.204 Compiler,,GCC 11.3.0,GCC 11.4.0 File-System,,ext4,ext4 Screen Resolution,,1920x1080,1920x1080 ,,"a","b" "SQLite - Threads / Copies: 1 (sec)",LIB,30.327,88.585 "SQLite - Threads / Copies: 2 (sec)",LIB,123.465,170.014 "SQLite - Threads / Copies: 4 (sec)",LIB,125.830,173.045 "vkpeak - fp32-scalar (GFLOPS)",HIB,268.33,268.33 "Xonotic - Resolution: 1920 x 1080 - Effects Quality: Low (FPS)",HIB,205.6062326,205.4486260 "Xonotic - Resolution: 1920 x 1080 - Effects Quality: High (FPS)",HIB,91.1145989,90.9080095 "Xonotic - Resolution: 1920 x 1080 - Effects Quality: Ultra (FPS)",HIB,77.9338736,77.5195887 "Xonotic - Resolution: 1920 x 1080 - Effects Quality: Ultimate (FPS)",HIB,59.1708882,58.9499391 "QuantLib - (MFLOPS)",HIB,2379.8,2404.8 "libxsmm - M N K: 128 (GFLOPS/s)",HIB,79.9,83.7 "libxsmm - M N K: 32 (GFLOPS/s)",HIB,47.7,48.0 "libxsmm - M N K: 64 (GFLOPS/s)",HIB,90.9,90.8 "Z3 Theorem Prover - SMT File: 1.smt2 (sec)",LIB,43.973,43.419 "Z3 Theorem Prover - SMT File: 2.smt2 (sec)",LIB,130.941,131.405 "dav1d - Video Input: Chimera 1080p (FPS)",HIB,244.12,249.62 "dav1d - Video Input: Summer Nature 4K (FPS)",HIB,67.01,66.38 "dav1d - Video Input: Summer Nature 1080p (FPS)",HIB,273.35,310.94 "dav1d - Video Input: Chimera 1080p 10-bit (FPS)",HIB,191.30,190.87 "Embree - Binary: Pathtracer - Model: Crown (FPS)",HIB,3.5218,3.4542 "Embree - Binary: Pathtracer ISPC - Model: Crown (FPS)",HIB,3.7369,3.6962 "Embree - Binary: Pathtracer - Model: Asian Dragon (FPS)",HIB,4.2183,4.1998 "Embree - Binary: Pathtracer - Model: Asian Dragon Obj (FPS)",HIB,3.8229,3.8503 "Embree - Binary: Pathtracer ISPC - Model: Asian Dragon (FPS)",HIB,4.7883,4.7636 "Embree - Binary: Pathtracer ISPC - Model: Asian Dragon Obj (FPS)",HIB,4.1670,3.9923 "SVT-AV1 - Encoder Mode: Preset 4 - Input: Bosphorus 4K (FPS)",HIB,1.092,0.948 "SVT-AV1 - Encoder Mode: Preset 8 - Input: Bosphorus 4K (FPS)",HIB,9.490,9.436 "SVT-AV1 - Encoder Mode: Preset 12 - Input: Bosphorus 4K (FPS)",HIB,35.497,28.978 "SVT-AV1 - Encoder Mode: Preset 13 - Input: Bosphorus 4K (FPS)",HIB,34.049,30.975 "SVT-AV1 - Encoder Mode: Preset 4 - Input: Bosphorus 1080p (FPS)",HIB,4.260,4.773 "SVT-AV1 - Encoder Mode: Preset 8 - Input: Bosphorus 1080p (FPS)",HIB,35.739,31.014 "SVT-AV1 - Encoder Mode: Preset 12 - Input: Bosphorus 1080p (FPS)",HIB,154.597,157.692 "SVT-AV1 - Encoder Mode: Preset 13 - Input: Bosphorus 1080p (FPS)",HIB,202.852,206.769 "VVenC - Video Input: Bosphorus 4K - Video Preset: Fast (FPS)",HIB,1.180,1.170 "VVenC - Video Input: Bosphorus 4K - Video Preset: Faster (FPS)",HIB,2.693,2.672 "VVenC - Video Input: Bosphorus 1080p - Video Preset: Fast (FPS)",HIB,3.905,3.790 "VVenC - Video Input: Bosphorus 1080p - Video Preset: Faster (FPS)",HIB,9.882,9.868 "Intel Open Image Denoise - Run: RT.hdr_alb_nrm.3840x2160 - Device: CPU-Only (Images / Sec)",HIB,0.12,0.12 "Intel Open Image Denoise - Run: RT.ldr_alb_nrm.3840x2160 - Device: CPU-Only (Images / Sec)",HIB,0.12,0.12 "Intel Open Image Denoise - Run: RTLightmap.hdr.4096x4096 - Device: CPU-Only (Images / Sec)",HIB,0.06,0.06 "OSPRay - Benchmark: particle_volume/ao/real_time (Items/sec)",HIB,1.49334,1.3692 "OSPRay - Benchmark: particle_volume/scivis/real_time (Items/sec)",HIB,1.37399,1.34467 "OSPRay - Benchmark: particle_volume/pathtracer/real_time (Items/sec)",HIB,52.5904,50.0829 "OSPRay - Benchmark: gravity_spheres_volume/dim_512/ao/real_time (Items/sec)",HIB,0.745146,0.734783 "OSPRay - Benchmark: gravity_spheres_volume/dim_512/scivis/real_time (Items/sec)",HIB,0.622182,0.709986 "OSPRay - Benchmark: gravity_spheres_volume/dim_512/pathtracer/real_time (Items/sec)",HIB,1.06164,1.012953 "Timed Godot Game Engine Compilation - Time To Compile (sec)",LIB,1372.604,1384.766 "Timed LLVM Compilation - Build System: Ninja (sec)",LIB,2892.088,2908.878 "Timed LLVM Compilation - Build System: Unix Makefiles (sec)",LIB,2967.528,2929.911 "Build2 - Time To Compile (sec)",LIB,555.936,569.609 "Opus Codec Encoding - WAV To Opus Encode (sec)",LIB,35.396,35.426 "Liquid-DSP - Threads: 1 - Buffer Length: 256 - Filter Length: 32 (samples/s)",HIB,46516500,46825000 "Liquid-DSP - Threads: 1 - Buffer Length: 256 - Filter Length: 57 (samples/s)",HIB,43429000,43427500 "Liquid-DSP - Threads: 2 - Buffer Length: 256 - Filter Length: 32 (samples/s)",HIB,85550500,85664000 "Liquid-DSP - Threads: 2 - Buffer Length: 256 - Filter Length: 57 (samples/s)",HIB,73795500,73620500 "Liquid-DSP - Threads: 4 - Buffer Length: 256 - Filter Length: 32 (samples/s)",HIB,140280000,139950000 "Liquid-DSP - Threads: 4 - Buffer Length: 256 - Filter Length: 57 (samples/s)",HIB,119590000,112205000 "Liquid-DSP - Threads: 8 - Buffer Length: 256 - Filter Length: 32 (samples/s)",HIB,177945000,185540000 "Liquid-DSP - Threads: 8 - Buffer Length: 256 - Filter Length: 57 (samples/s)",HIB,140345000,132830000 "Liquid-DSP - Threads: 1 - Buffer Length: 256 - Filter Length: 512 (samples/s)",HIB,7721800,7710850 "Liquid-DSP - Threads: 2 - Buffer Length: 256 - Filter Length: 512 (samples/s)",HIB,15179500,15023500 "Liquid-DSP - Threads: 4 - Buffer Length: 256 - Filter Length: 512 (samples/s)",HIB,25830500,25812500 "Liquid-DSP - Threads: 8 - Buffer Length: 256 - Filter Length: 512 (samples/s)",HIB,35719000,36523000 "Apache IoTDB - Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 200 (point/sec)",HIB,529599,524660.18 "Apache IoTDB - Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 200 (Latency)",HIB,22.62,22.93 "Apache IoTDB - Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 500 (point/sec)",HIB,996242.81,1009840.28 "Apache IoTDB - Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 500 (Latency)",HIB,34.81,34.48 "Apache IoTDB - Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 200 (point/sec)",HIB,816344.2,812170.86 "Apache IoTDB - Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 200 (Latency)",HIB,17.34,16.68 "Apache IoTDB - Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 500 (point/sec)",HIB,1333775.59,1298988.31 "Apache IoTDB - Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 500 (Latency)",HIB,29.65,29.75 "Apache IoTDB - Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 200 (point/sec)",HIB,1036429.93,1040683.13 "Apache IoTDB - Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 200 (Latency)",HIB,15.75,15.11 "Apache IoTDB - Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 500 (point/sec)",HIB,611157.73,637041.62 "Apache IoTDB - Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 500 (Latency)",HIB,75.4,70.46 "Apache IoTDB - Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 200 (point/sec)",HIB,15017343.49,15526243.84 "Apache IoTDB - Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 200 (Latency)",HIB,107.84,104.43 "Apache IoTDB - Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 500 (point/sec)",HIB,13467917.31,12006039.14 "Apache IoTDB - Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 500 (Latency)",HIB,342.21,387.15 "Apache IoTDB - Device Count: 200 - Batch Size Per Write: 100 - Sensor Count: 200 (point/sec)",HIB,12613348.93,12464073 "Apache IoTDB - Device Count: 200 - Batch Size Per Write: 100 - Sensor Count: 200 (Latency)",HIB,144.53,143.24 "Apache IoTDB - Device Count: 200 - Batch Size Per Write: 100 - Sensor Count: 500 (point/sec)",HIB,8609112.97,9394851.37 "Apache IoTDB - Device Count: 200 - Batch Size Per Write: 100 - Sensor Count: 500 (Latency)",HIB,550.47,485.93 "Apache IoTDB - Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 200 (point/sec)",HIB,8900311.03,9246224.42 "Apache IoTDB - Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 200 (Latency)",HIB,212.43,198.41 "Apache IoTDB - Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 500 (point/sec)",HIB,4948660.19,4629287.79 "Apache IoTDB - Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 500 (Latency)",HIB,984.7,1026.93 "Memcached - Set To Get Ratio: 1:5 (Ops/sec)",HIB,525368.39,518308.20 "Memcached - Set To Get Ratio: 1:10 (Ops/sec)",HIB,502050.43,486807.49 "Memcached - Set To Get Ratio: 1:100 (Ops/sec)",HIB,496309.76,485922.63 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,2.3527,2.3352 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,855.6769,864.9932 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,65.4250,65.3564 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,30.5398,30.5664 "Neural Magic DeepSparse - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,28.9185,28.4640 "Neural Magic DeepSparse - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,69.5593,70.8213 "Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,8.3009,8.2832 "Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,244.5617,245.6430 "Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,30.5673,29.7603 "Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,65.9517,68.0003 "Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,196.7843,195.4858 "Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,10.2465,10.336 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,14.6720,14.6069 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,137.9489,139.0475 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,2.6677,3.2461 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,764.2676,614.6793 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,29.6585,28.9622 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,68.0491,69.6181 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,12.5441,12.9502 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,162.1647,154.2586 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,17.5124,20.7264 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,116.2683,98.7143 "Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,2.9815,3.2320 "Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,669.1809,628.3639 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,21.2985,34.3231 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,93.8482,58.2368 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,9.4553,11.1719 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,211.9830,179.0772 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,2.0309,2.3883 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,979.1797,835.8517 "Redis 7.0.12 + memtier_benchmark - Protocol: Redis - Clients: 50 - Set To Get Ratio: 1:5 (Ops/sec)",HIB,1039311.12,1398883.01 "Redis 7.0.12 + memtier_benchmark - Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:5 (Ops/sec)",HIB,1016703.27,1372033.18 "Redis 7.0.12 + memtier_benchmark - Protocol: Redis - Clients: 50 - Set To Get Ratio: 1:10 (Ops/sec)",HIB,1109812.44,1455171.86 "Redis 7.0.12 + memtier_benchmark - Protocol: Redis - Clients: 500 - Set To Get Ratio: 1:5 (Ops/sec)",HIB,890374.23,1248610.70 "Redis 7.0.12 + memtier_benchmark - Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:10 (Ops/sec)",HIB,1028641.19,1377833.48 "Redis 7.0.12 + memtier_benchmark - Protocol: Redis - Clients: 500 - Set To Get Ratio: 1:10 (Ops/sec)",HIB,914462.02,1253523.69 "Stress-NG - Test: Hash (Bogo Ops/s)",HIB,523699.70,638650.51 "Stress-NG - Test: MMAP (Bogo Ops/s)",HIB,20.86,28.46 "Stress-NG - Test: NUMA (Bogo Ops/s)",HIB,53.95,60.49 "Stress-NG - Test: Pipe (Bogo Ops/s)",HIB,1422109.84,1533355.11 "Stress-NG - Test: Poll (Bogo Ops/s)",HIB,284300.80,318466.50 "Stress-NG - Test: Zlib (Bogo Ops/s)",HIB,273.05,335.91 "Stress-NG - Test: Futex (Bogo Ops/s)",HIB,486078.68,624014.17 "Stress-NG - Test: MEMFD (Bogo Ops/s)",HIB,56.59,43.09 "Stress-NG - Test: Mutex (Bogo Ops/s)",HIB,604156.72,694562.47 "Stress-NG - Test: Atomic (Bogo Ops/s)",HIB,224.51,249.99 "Stress-NG - Test: Crypto (Bogo Ops/s)",HIB,4590.53,5684.53 "Stress-NG - Test: Malloc (Bogo Ops/s)",HIB,375933.99,434156.10 "Stress-NG - Test: Cloning (Bogo Ops/s)",HIB,669.41,715.58 "Stress-NG - Test: Forking (Bogo Ops/s)",HIB,9551.79,12140.60 "Stress-NG - Test: Pthread (Bogo Ops/s)",HIB,34974.26,42229.61 "Stress-NG - Test: AVL Tree (Bogo Ops/s)",HIB,16.41,19.3 "Stress-NG - Test: IO_uring (Bogo Ops/s)",HIB,170492.16,182936.68 "Stress-NG - Test: SENDFILE (Bogo Ops/s)",HIB,36779.86,43752.37 "Stress-NG - Test: CPU Cache (Bogo Ops/s)",HIB,929178.43,1153192.75 "Stress-NG - Test: CPU Stress (Bogo Ops/s)",HIB,7310.05,7866.4 "Stress-NG - Test: Semaphores (Bogo Ops/s)",HIB,3218725.51,3241408.21 "Stress-NG - Test: Matrix Math (Bogo Ops/s)",HIB,17318.7,17116.79 "Stress-NG - Test: Vector Math (Bogo Ops/s)",HIB,12666.19,12444.33 "Stress-NG - Test: Function Call (Bogo Ops/s)",HIB,2058.70,2164.20 "Stress-NG - Test: x86_64 RdRand (Bogo Ops/s)",HIB,3267.31,3134.04 "Stress-NG - Test: Floating Point (Bogo Ops/s)",HIB,743.83,762.86 "Stress-NG - Test: Matrix 3D Math (Bogo Ops/s)",HIB,383.49,396.03 "Stress-NG - Test: Memory Copying (Bogo Ops/s)",HIB,1078.73,1088.75 "Stress-NG - Test: Vector Shuffle (Bogo Ops/s)",HIB,2431.22,2431.01 "Stress-NG - Test: Socket Activity (Bogo Ops/s)",HIB,2495.57,2508.63 "Stress-NG - Test: Wide Vector Math (Bogo Ops/s)",HIB,147314.85,148388.79 "Stress-NG - Test: Context Switching (Bogo Ops/s)",HIB,705297.96,733269.79 "Stress-NG - Test: Fused Multiply-Add (Bogo Ops/s)",HIB,2978232.73,2737483.30 "Stress-NG - Test: Vector Floating Point (Bogo Ops/s)",HIB,7289.32,7877.70 "Stress-NG - Test: Glibc C String Functions (Bogo Ops/s)",HIB,2485856.07,2360589.10 "Stress-NG - Test: Glibc Qsort Data Sorting (Bogo Ops/s)",HIB,69.13,74.13 "Stress-NG - Test: System V Message Passing (Bogo Ops/s)",HIB,2652462.62,2519916.10 "NCNN - Target: CPU - Model: mobilenet (ms)",LIB,25.93,27.68 "NCNN - Target: CPU-v2-v2 - Model: mobilenet-v2 (ms)",LIB,6.69,6.15 "NCNN - Target: CPU-v3-v3 - Model: mobilenet-v3 (ms)",LIB,5.14,4.31 "NCNN - Target: CPU - Model: shufflenet-v2 (ms)",LIB,4.29,3.02 "NCNN - Target: CPU - Model: mnasnet (ms)",LIB,6.04,4.49 "NCNN - Target: CPU - Model: efficientnet-b0 (ms)",LIB,9.78,9.43 "NCNN - Target: CPU - Model: blazeface (ms)",LIB,0.91,0.93 "NCNN - Target: CPU - Model: googlenet (ms)",LIB,16.23,16.19 "NCNN - Target: CPU - Model: vgg16 (ms)",LIB,97.42,98.75 "NCNN - Target: CPU - Model: resnet18 (ms)",LIB,12.61,12.62 "NCNN - Target: CPU - Model: alexnet (ms)",LIB,11.68,11.64 "NCNN - Target: CPU - Model: resnet50 (ms)",LIB,36.02,34.33 "NCNN - Target: CPU - Model: yolov4-tiny (ms)",LIB,38.37,39.31 "NCNN - Target: CPU - Model: squeezenet_ssd (ms)",LIB,16.55,15.80 "NCNN - Target: CPU - Model: regnety_400m (ms)",LIB,10.03,10.12 "NCNN - Target: CPU - Model: vision_transformer (ms)",LIB,233.96,232.17 "NCNN - Target: CPU - Model: FastestDet (ms)",LIB,5.39,5.55 "NCNN - Target: Vulkan GPU - Model: mobilenet (ms)",LIB,28.15,27.38 "NCNN - Target: Vulkan GPU-v2-v2 - Model: mobilenet-v2 (ms)",LIB,6.67,6.67 "NCNN - Target: Vulkan GPU-v3-v3 - Model: mobilenet-v3 (ms)",LIB,5.11,5.11 "NCNN - Target: Vulkan GPU - Model: shufflenet-v2 (ms)",LIB,3.66,3.67 "NCNN - Target: Vulkan GPU - Model: mnasnet (ms)",LIB,5.23,5.26 "NCNN - Target: Vulkan GPU - Model: efficientnet-b0 (ms)",LIB,9.22,9.34 "NCNN - Target: Vulkan GPU - Model: blazeface (ms)",LIB,0.94,0.96 "NCNN - Target: Vulkan GPU - Model: googlenet (ms)",LIB,16.29,16.19 "NCNN - Target: Vulkan GPU - Model: vgg16 (ms)",LIB,97.70,96.26 "NCNN - Target: Vulkan GPU - Model: resnet18 (ms)",LIB,12.73,12.56 "NCNN - Target: Vulkan GPU - Model: alexnet (ms)",LIB,11.67,11.69 "NCNN - Target: Vulkan GPU - Model: resnet50 (ms)",LIB,33.18,33.60 "NCNN - Target: Vulkan GPU - Model: yolov4-tiny (ms)",LIB,39.39,39.17 "NCNN - Target: Vulkan GPU - Model: squeezenet_ssd (ms)",LIB,15.97,15.48 "NCNN - Target: Vulkan GPU - Model: regnety_400m (ms)",LIB,9.82,10.41 "NCNN - Target: Vulkan GPU - Model: vision_transformer (ms)",LIB,240.03,235.51 "NCNN - Target: Vulkan GPU - Model: FastestDet (ms)",LIB,5.66,5.47 "Apache Cassandra - Test: Writes (Op/s)",HIB,26422,26438