Intel Core i7-8565U testing with a Dell 0KTW76 (1.17.0 BIOS) and Intel UHD 620 WHL GT2 15GB on Ubuntu 22.04 via the Phoronix Test Suite.
Compare your own system(s) to this result file with the
Phoronix Test Suite by running the command:
phoronix-test-suite benchmark 2308067-NE-DDDXXX42317
dddxxx
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"
"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 (Latency)",HIB,34.81,34.48
"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 (Latency)",HIB,29.65,29.75
"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 (Latency)",HIB,75.4,70.46
"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 (Latency)",HIB,342.21,387.15
"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 (Latency)",HIB,550.47,485.93
"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 (Latency)",HIB,984.7,1026.93
"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
"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
"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
"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
"vkpeak - fp32-scalar (GFLOPS)",HIB,268.33,268.33
"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
"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
"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 Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,65.4250,65.3564
"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 Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,8.3009,8.2832
"Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,30.5673,29.7603
"Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,196.7843,195.4858
"Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,14.6720,14.6069
"Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,2.6677,3.2461
"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 Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,12.5441,12.9502
"Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,17.5124,20.7264
"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: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,21.2985,34.3231
"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 Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,2.0309,2.3883
"QuantLib - (MFLOPS)",HIB,2379.8,2404.8
"Apache Cassandra - Test: Writes (Op/s)",HIB,26422,26438
"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
"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
"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: 500 (point/sec)",HIB,996242.81,1009840.28
"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: 500 (point/sec)",HIB,1333775.59,1298988.31
"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: 500 (point/sec)",HIB,611157.73,637041.62
"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: 500 (point/sec)",HIB,13467917.31,12006039.14
"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: 500 (point/sec)",HIB,8609112.97,9394851.37
"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: 500 (point/sec)",HIB,4948660.19,4629287.79
"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
"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
"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 (ms/batch)",LIB,30.5398,30.5664
"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 (ms/batch)",LIB,244.5617,245.6430
"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 (ms/batch)",LIB,10.2465,10.336
"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 (ms/batch)",LIB,764.2676,614.6793
"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 (ms/batch)",LIB,162.1647,154.2586
"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 (ms/batch)",LIB,669.1809,628.3639
"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 (ms/batch)",LIB,211.9830,179.0772
"Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,979.1797,835.8517
"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
"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
"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