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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.
HTML result view exported from: https://openbenchmarking.org/result/2308067-NE-DDDXXX42317&grw&sor.
Z3 Theorem Prover
SMT File: 1.smt2
Stress-NG
Test: Hash
Z3 Theorem Prover
SMT File: 2.smt2
Stress-NG
Test: MMAP
libxsmm
M N K: 64
libxsmm
M N K: 128
libxsmm
M N K: 32
Stress-NG
Test: NUMA
Stress-NG
Test: Pipe
Stress-NG
Test: Poll
Stress-NG
Test: Zlib
Stress-NG
Test: Futex
Stress-NG
Test: MEMFD
Stress-NG
Test: Mutex
Stress-NG
Test: Atomic
Stress-NG
Test: Crypto
Stress-NG
Test: Malloc
Stress-NG
Test: Cloning
Stress-NG
Test: Forking
Stress-NG
Test: Pthread
Stress-NG
Test: AVL Tree
Stress-NG
Test: IO_uring
Stress-NG
Test: SENDFILE
Stress-NG
Test: CPU Cache
Stress-NG
Test: CPU Stress
Stress-NG
Test: Semaphores
Stress-NG
Test: Matrix Math
Stress-NG
Test: Vector Math
Stress-NG
Test: Function Call
Stress-NG
Test: x86_64 RdRand
Stress-NG
Test: Floating Point
Stress-NG
Test: Matrix 3D Math
Stress-NG
Test: Memory Copying
Stress-NG
Test: Vector Shuffle
Stress-NG
Test: Socket Activity
Stress-NG
Test: Wide Vector Math
Stress-NG
Test: Context Switching
Stress-NG
Test: Fused Multiply-Add
Stress-NG
Test: Vector Floating Point
Stress-NG
Test: Glibc C String Functions
Stress-NG
Test: Glibc Qsort Data Sorting
Stress-NG
Test: System V Message Passing
Opus Codec Encoding
WAV To Opus Encode
Xonotic
Resolution: 1920 x 1080 - Effects Quality: Low
Xonotic
Resolution: 1920 x 1080 - Effects Quality: High
Xonotic
Resolution: 1920 x 1080 - Effects Quality: Ultra
Xonotic
Resolution: 1920 x 1080 - Effects Quality: Ultimate
QuantLib
Neural Magic DeepSparse
Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream
NCNN
Target: CPU - Model: mobilenet
NCNN
Target: CPU-v2-v2 - Model: mobilenet-v2
NCNN
Target: CPU-v3-v3 - Model: mobilenet-v3
NCNN
Target: CPU - Model: shufflenet-v2
NCNN
Target: CPU - Model: mnasnet
NCNN
Target: CPU - Model: efficientnet-b0
NCNN
Target: CPU - Model: blazeface
NCNN
Target: CPU - Model: googlenet
NCNN
Target: CPU - Model: vgg16
NCNN
Target: CPU - Model: resnet18
NCNN
Target: CPU - Model: alexnet
NCNN
Target: CPU - Model: resnet50
NCNN
Target: CPU - Model: yolov4-tiny
NCNN
Target: CPU - Model: squeezenet_ssd
NCNN
Target: CPU - Model: regnety_400m
NCNN
Target: CPU - Model: vision_transformer
NCNN
Target: CPU - Model: FastestDet
NCNN
Target: Vulkan GPU - Model: mobilenet
NCNN
Target: Vulkan GPU-v2-v2 - Model: mobilenet-v2
NCNN
Target: Vulkan GPU-v3-v3 - Model: mobilenet-v3
NCNN
Target: Vulkan GPU - Model: shufflenet-v2
NCNN
Target: Vulkan GPU - Model: mnasnet
NCNN
Target: Vulkan GPU - Model: efficientnet-b0
NCNN
Target: Vulkan GPU - Model: blazeface
NCNN
Target: Vulkan GPU - Model: googlenet
NCNN
Target: Vulkan GPU - Model: vgg16
NCNN
Target: Vulkan GPU - Model: resnet18
NCNN
Target: Vulkan GPU - Model: alexnet
NCNN
Target: Vulkan GPU - Model: resnet50
NCNN
Target: Vulkan GPU - Model: yolov4-tiny
NCNN
Target: Vulkan GPU - Model: squeezenet_ssd
NCNN
Target: Vulkan GPU - Model: regnety_400m
NCNN
Target: Vulkan GPU - Model: vision_transformer
NCNN
Target: Vulkan GPU - Model: FastestDet
Timed LLVM Compilation
Build System: Ninja
Timed LLVM Compilation
Build System: Unix Makefiles
dav1d
Video Input: Chimera 1080p
dav1d
Video Input: Summer Nature 4K
dav1d
Video Input: Summer Nature 1080p
dav1d
Video Input: Chimera 1080p 10-bit
SVT-AV1
Encoder Mode: Preset 4 - Input: Bosphorus 4K
SVT-AV1
Encoder Mode: Preset 8 - Input: Bosphorus 4K
SVT-AV1
Encoder Mode: Preset 12 - Input: Bosphorus 4K
SVT-AV1
Encoder Mode: Preset 13 - Input: Bosphorus 4K
SVT-AV1
Encoder Mode: Preset 4 - Input: Bosphorus 1080p
SVT-AV1
Encoder Mode: Preset 8 - Input: Bosphorus 1080p
SVT-AV1
Encoder Mode: Preset 12 - Input: Bosphorus 1080p
SVT-AV1
Encoder Mode: Preset 13 - Input: Bosphorus 1080p
VVenC
Video Input: Bosphorus 4K - Video Preset: Fast
VVenC
Video Input: Bosphorus 4K - Video Preset: Faster
VVenC
Video Input: Bosphorus 1080p - Video Preset: Fast
VVenC
Video Input: Bosphorus 1080p - Video Preset: Faster
Timed Godot Game Engine Compilation
Time To Compile
Embree
Binary: Pathtracer - Model: Crown
Embree
Binary: Pathtracer ISPC - Model: Crown
Embree
Binary: Pathtracer - Model: Asian Dragon
Embree
Binary: Pathtracer - Model: Asian Dragon Obj
Embree
Binary: Pathtracer ISPC - Model: Asian Dragon
Embree
Binary: Pathtracer ISPC - Model: Asian Dragon Obj
Intel Open Image Denoise
Run: RT.hdr_alb_nrm.3840x2160 - Device: CPU-Only
Intel Open Image Denoise
Run: RT.ldr_alb_nrm.3840x2160 - Device: CPU-Only
Intel Open Image Denoise
Run: RTLightmap.hdr.4096x4096 - Device: CPU-Only
OSPRay
Benchmark: particle_volume/ao/real_time
OSPRay
Benchmark: particle_volume/scivis/real_time
OSPRay
Benchmark: particle_volume/pathtracer/real_time
OSPRay
Benchmark: gravity_spheres_volume/dim_512/ao/real_time
OSPRay
Benchmark: gravity_spheres_volume/dim_512/scivis/real_time
OSPRay
Benchmark: gravity_spheres_volume/dim_512/pathtracer/real_time
Build2
Time To Compile
vkpeak
fp32-scalar
Liquid-DSP
Threads: 1 - Buffer Length: 256 - Filter Length: 32
Liquid-DSP
Threads: 1 - Buffer Length: 256 - Filter Length: 57
Liquid-DSP
Threads: 2 - Buffer Length: 256 - Filter Length: 32
Liquid-DSP
Threads: 2 - Buffer Length: 256 - Filter Length: 57
Liquid-DSP
Threads: 4 - Buffer Length: 256 - Filter Length: 32
Liquid-DSP
Threads: 4 - Buffer Length: 256 - Filter Length: 57
Liquid-DSP
Threads: 8 - Buffer Length: 256 - Filter Length: 32
Liquid-DSP
Threads: 8 - Buffer Length: 256 - Filter Length: 57
Liquid-DSP
Threads: 1 - Buffer Length: 256 - Filter Length: 512
Liquid-DSP
Threads: 2 - Buffer Length: 256 - Filter Length: 512
Liquid-DSP
Threads: 4 - Buffer Length: 256 - Filter Length: 512
Liquid-DSP
Threads: 8 - Buffer Length: 256 - Filter Length: 512
Apache IoTDB
Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 200
Apache IoTDB
Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 200
Apache IoTDB
Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 500
Apache IoTDB
Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 500
Apache IoTDB
Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 200
Apache IoTDB
Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 200
Apache IoTDB
Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 500
Apache IoTDB
Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 500
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 200
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 200
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 500
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 500
Apache IoTDB
Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 200
Apache IoTDB
Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 200
Apache IoTDB
Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 500
Apache IoTDB
Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 500
Apache IoTDB
Device Count: 200 - Batch Size Per Write: 100 - Sensor Count: 200
Apache IoTDB
Device Count: 200 - Batch Size Per Write: 100 - Sensor Count: 200
Apache IoTDB
Device Count: 200 - Batch Size Per Write: 100 - Sensor Count: 500
Apache IoTDB
Device Count: 200 - Batch Size Per Write: 100 - Sensor Count: 500
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 200
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 200
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 500
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 500
Memcached
Set To Get Ratio: 1:5
Memcached
Set To Get Ratio: 1:10
Memcached
Set To Get Ratio: 1:100
Redis 7.0.12 + memtier_benchmark
Protocol: Redis - Clients: 50 - Set To Get Ratio: 1:5
Redis 7.0.12 + memtier_benchmark
Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:5
Redis 7.0.12 + memtier_benchmark
Protocol: Redis - Clients: 50 - Set To Get Ratio: 1:10
Redis 7.0.12 + memtier_benchmark
Protocol: Redis - Clients: 500 - Set To Get Ratio: 1:5
Redis 7.0.12 + memtier_benchmark
Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:10
Redis 7.0.12 + memtier_benchmark
Protocol: Redis - Clients: 500 - Set To Get Ratio: 1:10
SQLite
Threads / Copies: 1
SQLite
Threads / Copies: 2
SQLite
Threads / Copies: 4
Apache Cassandra
Test: Writes
Phoronix Test Suite v10.8.5