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AMD Ryzen 7 4700U testing with a LENOVO LNVNB161216 (DTCN18WWV1.04 BIOS) and AMD Renoir 512MB on Ubuntu 22.04 via the Phoronix Test Suite.
HTML result view exported from: https://openbenchmarking.org/result/2209013-NE-AA419570266.
Unpacking The Linux Kernel
linux-5.19.tar.xz
C-Blosc
Test: blosclz shuffle
C-Blosc
Test: blosclz bitshuffle
GraphicsMagick
Operation: Swirl
GraphicsMagick
Operation: Rotate
GraphicsMagick
Operation: Sharpen
GraphicsMagick
Operation: Enhanced
GraphicsMagick
Operation: Resizing
GraphicsMagick
Operation: Noise-Gaussian
GraphicsMagick
Operation: HWB Color Space
7-Zip Compression
Test: Compression Rating
7-Zip Compression
Test: Decompression Rating
Apache Spark
Row Count: 1000000 - Partitions: 100 - SHA-512 Benchmark Time
Apache Spark
Row Count: 1000000 - Partitions: 100 - Calculate Pi Benchmark
Apache Spark
Row Count: 1000000 - Partitions: 100 - Calculate Pi Benchmark Using Dataframe
Apache Spark
Row Count: 1000000 - Partitions: 100 - Group By Test Time
Apache Spark
Row Count: 1000000 - Partitions: 100 - Repartition Test Time
Apache Spark
Row Count: 1000000 - Partitions: 100 - Inner Join Test Time
Apache Spark
Row Count: 1000000 - Partitions: 100 - Broadcast Inner Join Test Time
Apache Spark
Row Count: 1000000 - Partitions: 500 - SHA-512 Benchmark Time
Apache Spark
Row Count: 1000000 - Partitions: 500 - Calculate Pi Benchmark
Apache Spark
Row Count: 1000000 - Partitions: 500 - Calculate Pi Benchmark Using Dataframe
Apache Spark
Row Count: 1000000 - Partitions: 500 - Group By Test Time
Apache Spark
Row Count: 1000000 - Partitions: 500 - Repartition Test Time
Apache Spark
Row Count: 1000000 - Partitions: 500 - Inner Join Test Time
Apache Spark
Row Count: 1000000 - Partitions: 500 - Broadcast Inner Join Test Time
Apache Spark
Row Count: 1000000 - Partitions: 1000 - SHA-512 Benchmark Time
Apache Spark
Row Count: 1000000 - Partitions: 1000 - Calculate Pi Benchmark
Apache Spark
Row Count: 1000000 - Partitions: 1000 - Calculate Pi Benchmark Using Dataframe
Apache Spark
Row Count: 1000000 - Partitions: 1000 - Group By Test Time
Apache Spark
Row Count: 1000000 - Partitions: 1000 - Repartition Test Time
Apache Spark
Row Count: 1000000 - Partitions: 1000 - Inner Join Test Time
Apache Spark
Row Count: 1000000 - Partitions: 1000 - Broadcast Inner Join Test Time
Apache Spark
Row Count: 1000000 - Partitions: 2000 - SHA-512 Benchmark Time
Apache Spark
Row Count: 1000000 - Partitions: 2000 - Calculate Pi Benchmark
Apache Spark
Row Count: 1000000 - Partitions: 2000 - Calculate Pi Benchmark Using Dataframe
Apache Spark
Row Count: 1000000 - Partitions: 2000 - Group By Test Time
Apache Spark
Row Count: 1000000 - Partitions: 2000 - Repartition Test Time
Apache Spark
Row Count: 1000000 - Partitions: 2000 - Inner Join Test Time
Apache Spark
Row Count: 1000000 - Partitions: 2000 - Broadcast Inner Join Test Time
Apache Spark
Row Count: 10000000 - Partitions: 100 - SHA-512 Benchmark Time
Apache Spark
Row Count: 10000000 - Partitions: 100 - Calculate Pi Benchmark
Apache Spark
Row Count: 10000000 - Partitions: 100 - Calculate Pi Benchmark Using Dataframe
Apache Spark
Row Count: 10000000 - Partitions: 100 - Group By Test Time
Apache Spark
Row Count: 10000000 - Partitions: 100 - Repartition Test Time
Apache Spark
Row Count: 10000000 - Partitions: 100 - Inner Join Test Time
Apache Spark
Row Count: 10000000 - Partitions: 100 - Broadcast Inner Join Test Time
Apache Spark
Row Count: 10000000 - Partitions: 500 - SHA-512 Benchmark Time
Apache Spark
Row Count: 10000000 - Partitions: 500 - Calculate Pi Benchmark
Apache Spark
Row Count: 10000000 - Partitions: 500 - Calculate Pi Benchmark Using Dataframe
Apache Spark
Row Count: 10000000 - Partitions: 500 - Group By Test Time
Apache Spark
Row Count: 10000000 - Partitions: 500 - Repartition Test Time
Apache Spark
Row Count: 10000000 - Partitions: 500 - Inner Join Test Time
Apache Spark
Row Count: 10000000 - Partitions: 500 - Broadcast Inner Join Test Time
Apache Spark
Row Count: 20000000 - Partitions: 100 - SHA-512 Benchmark Time
Apache Spark
Row Count: 20000000 - Partitions: 100 - Calculate Pi Benchmark
Apache Spark
Row Count: 20000000 - Partitions: 100 - Calculate Pi Benchmark Using Dataframe
Apache Spark
Row Count: 20000000 - Partitions: 100 - Group By Test Time
Apache Spark
Row Count: 20000000 - Partitions: 100 - Repartition Test Time
Apache Spark
Row Count: 20000000 - Partitions: 100 - Inner Join Test Time
Apache Spark
Row Count: 20000000 - Partitions: 100 - Broadcast Inner Join Test Time
Apache Spark
Row Count: 20000000 - Partitions: 500 - SHA-512 Benchmark Time
Apache Spark
Row Count: 20000000 - Partitions: 500 - Calculate Pi Benchmark
Apache Spark
Row Count: 20000000 - Partitions: 500 - Calculate Pi Benchmark Using Dataframe
Apache Spark
Row Count: 20000000 - Partitions: 500 - Group By Test Time
Apache Spark
Row Count: 20000000 - Partitions: 500 - Repartition Test Time
Apache Spark
Row Count: 20000000 - Partitions: 500 - Inner Join Test Time
Apache Spark
Row Count: 20000000 - Partitions: 500 - Broadcast Inner Join Test Time
Apache Spark
Row Count: 40000000 - Partitions: 100 - SHA-512 Benchmark Time
Apache Spark
Row Count: 40000000 - Partitions: 100 - Calculate Pi Benchmark
Apache Spark
Row Count: 40000000 - Partitions: 100 - Calculate Pi Benchmark Using Dataframe
Apache Spark
Row Count: 40000000 - Partitions: 100 - Group By Test Time
Apache Spark
Row Count: 40000000 - Partitions: 100 - Repartition Test Time
Apache Spark
Row Count: 40000000 - Partitions: 100 - Inner Join Test Time
Apache Spark
Row Count: 40000000 - Partitions: 100 - Broadcast Inner Join Test Time
Apache Spark
Row Count: 40000000 - Partitions: 500 - SHA-512 Benchmark Time
Apache Spark
Row Count: 40000000 - Partitions: 500 - Calculate Pi Benchmark
Apache Spark
Row Count: 40000000 - Partitions: 500 - Calculate Pi Benchmark Using Dataframe
Apache Spark
Row Count: 40000000 - Partitions: 500 - Group By Test Time
Apache Spark
Row Count: 40000000 - Partitions: 500 - Repartition Test Time
Apache Spark
Row Count: 40000000 - Partitions: 500 - Inner Join Test Time
Apache Spark
Row Count: 40000000 - Partitions: 500 - Broadcast Inner Join Test Time
Apache Spark
Row Count: 10000000 - Partitions: 1000 - SHA-512 Benchmark Time
Apache Spark
Row Count: 10000000 - Partitions: 1000 - Calculate Pi Benchmark
Apache Spark
Row Count: 10000000 - Partitions: 1000 - Calculate Pi Benchmark Using Dataframe
Apache Spark
Row Count: 10000000 - Partitions: 1000 - Group By Test Time
Apache Spark
Row Count: 10000000 - Partitions: 1000 - Repartition Test Time
Apache Spark
Row Count: 10000000 - Partitions: 1000 - Inner Join Test Time
Apache Spark
Row Count: 10000000 - Partitions: 1000 - Broadcast Inner Join Test Time
Apache Spark
Row Count: 10000000 - Partitions: 2000 - SHA-512 Benchmark Time
Apache Spark
Row Count: 10000000 - Partitions: 2000 - Calculate Pi Benchmark
Apache Spark
Row Count: 10000000 - Partitions: 2000 - Calculate Pi Benchmark Using Dataframe
Apache Spark
Row Count: 10000000 - Partitions: 2000 - Group By Test Time
Apache Spark
Row Count: 10000000 - Partitions: 2000 - Repartition Test Time
Apache Spark
Row Count: 10000000 - Partitions: 2000 - Inner Join Test Time
Apache Spark
Row Count: 10000000 - Partitions: 2000 - Broadcast Inner Join Test Time
Apache Spark
Row Count: 20000000 - Partitions: 1000 - SHA-512 Benchmark Time
Apache Spark
Row Count: 20000000 - Partitions: 1000 - Calculate Pi Benchmark
Apache Spark
Row Count: 20000000 - Partitions: 1000 - Calculate Pi Benchmark Using Dataframe
Apache Spark
Row Count: 20000000 - Partitions: 1000 - Group By Test Time
Apache Spark
Row Count: 20000000 - Partitions: 1000 - Repartition Test Time
Apache Spark
Row Count: 20000000 - Partitions: 1000 - Inner Join Test Time
Apache Spark
Row Count: 20000000 - Partitions: 1000 - Broadcast Inner Join Test Time
Apache Spark
Row Count: 20000000 - Partitions: 2000 - SHA-512 Benchmark Time
Apache Spark
Row Count: 20000000 - Partitions: 2000 - Calculate Pi Benchmark
Apache Spark
Row Count: 20000000 - Partitions: 2000 - Calculate Pi Benchmark Using Dataframe
Apache Spark
Row Count: 20000000 - Partitions: 2000 - Group By Test Time
Apache Spark
Row Count: 20000000 - Partitions: 2000 - Repartition Test Time
Apache Spark
Row Count: 20000000 - Partitions: 2000 - Inner Join Test Time
Apache Spark
Row Count: 20000000 - Partitions: 2000 - Broadcast Inner Join Test Time
Apache Spark
Row Count: 40000000 - Partitions: 1000 - SHA-512 Benchmark Time
Apache Spark
Row Count: 40000000 - Partitions: 1000 - Calculate Pi Benchmark
Apache Spark
Row Count: 40000000 - Partitions: 1000 - Calculate Pi Benchmark Using Dataframe
Apache Spark
Row Count: 40000000 - Partitions: 1000 - Group By Test Time
Apache Spark
Row Count: 40000000 - Partitions: 1000 - Repartition Test Time
Apache Spark
Row Count: 40000000 - Partitions: 1000 - Inner Join Test Time
Apache Spark
Row Count: 40000000 - Partitions: 1000 - Broadcast Inner Join Test Time
Apache Spark
Row Count: 40000000 - Partitions: 2000 - SHA-512 Benchmark Time
Apache Spark
Row Count: 40000000 - Partitions: 2000 - Calculate Pi Benchmark
Apache Spark
Row Count: 40000000 - Partitions: 2000 - Calculate Pi Benchmark Using Dataframe
Apache Spark
Row Count: 40000000 - Partitions: 2000 - Group By Test Time
Apache Spark
Row Count: 40000000 - Partitions: 2000 - Repartition Test Time
Apache Spark
Row Count: 40000000 - Partitions: 2000 - Inner Join Test Time
Apache Spark
Row Count: 40000000 - Partitions: 2000 - Broadcast Inner Join Test Time
Dragonflydb
Clients: 50 - Set To Get Ratio: 1:1
Dragonflydb
Clients: 50 - Set To Get Ratio: 1:5
Dragonflydb
Clients: 50 - Set To Get Ratio: 5:1
Dragonflydb
Clients: 200 - Set To Get Ratio: 1:1
Dragonflydb
Clients: 200 - Set To Get Ratio: 1:5
Dragonflydb
Clients: 200 - Set To Get Ratio: 5:1
Redis
Test: GET - Parallel Connections: 50
Redis
Test: SET - Parallel Connections: 50
Redis
Test: GET - Parallel Connections: 500
Redis
Test: LPOP - Parallel Connections: 50
Redis
Test: SADD - Parallel Connections: 50
Redis
Test: SET - Parallel Connections: 500
Redis
Test: GET - Parallel Connections: 1000
Redis
Test: LPOP - Parallel Connections: 500
Redis
Test: LPUSH - Parallel Connections: 50
Redis
Test: SADD - Parallel Connections: 500
Redis
Test: SET - Parallel Connections: 1000
Redis
Test: LPOP - Parallel Connections: 1000
Redis
Test: LPUSH - Parallel Connections: 500
Redis
Test: SADD - Parallel Connections: 1000
Redis
Test: LPUSH - Parallel Connections: 1000
memtier_benchmark
Protocol: Redis - Clients: 50 - Set To Get Ratio: 1:1
memtier_benchmark
Protocol: Redis - Clients: 50 - Set To Get Ratio: 1:5
memtier_benchmark
Protocol: Redis - Clients: 50 - Set To Get Ratio: 5:1
memtier_benchmark
Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:1
memtier_benchmark
Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:5
memtier_benchmark
Protocol: Redis - Clients: 100 - Set To Get Ratio: 5:1
memtier_benchmark
Protocol: Redis - Clients: 50 - Set To Get Ratio: 1:10
memtier_benchmark
Protocol: Redis - Clients: 500 - Set To Get Ratio: 1:1
memtier_benchmark
Protocol: Redis - Clients: 500 - Set To Get Ratio: 1:5
memtier_benchmark
Protocol: Redis - Clients: 500 - Set To Get Ratio: 5:1
memtier_benchmark
Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:10
memtier_benchmark
Protocol: Redis - Clients: 500 - Set To Get Ratio: 1:10
Mobile Neural Network
Model: nasnet
Mobile Neural Network
Model: mobilenetV3
Mobile Neural Network
Model: squeezenetv1.1
Mobile Neural Network
Model: resnet-v2-50
Mobile Neural Network
Model: SqueezeNetV1.0
Mobile Neural Network
Model: MobileNetV2_224
Mobile Neural Network
Model: mobilenet-v1-1.0
Mobile Neural Network
Model: inception-v3
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
OpenVINO
Model: Face Detection FP16 - Device: CPU
OpenVINO
Model: Face Detection FP16 - Device: CPU
OpenVINO
Model: Person Detection FP16 - Device: CPU
OpenVINO
Model: Person Detection FP16 - Device: CPU
OpenVINO
Model: Person Detection FP32 - Device: CPU
OpenVINO
Model: Person Detection FP32 - Device: CPU
OpenVINO
Model: Vehicle Detection FP16 - Device: CPU
OpenVINO
Model: Vehicle Detection FP16 - Device: CPU
OpenVINO
Model: Face Detection FP16-INT8 - Device: CPU
OpenVINO
Model: Face Detection FP16-INT8 - Device: CPU
OpenVINO
Model: Vehicle Detection FP16-INT8 - Device: CPU
OpenVINO
Model: Vehicle Detection FP16-INT8 - Device: CPU
OpenVINO
Model: Weld Porosity Detection FP16 - Device: CPU
OpenVINO
Model: Weld Porosity Detection FP16 - Device: CPU
OpenVINO
Model: Machine Translation EN To DE FP16 - Device: CPU
OpenVINO
Model: Machine Translation EN To DE FP16 - Device: CPU
OpenVINO
Model: Weld Porosity Detection FP16-INT8 - Device: CPU
OpenVINO
Model: Weld Porosity Detection FP16-INT8 - Device: CPU
OpenVINO
Model: Person Vehicle Bike Detection FP16 - Device: CPU
OpenVINO
Model: Person Vehicle Bike Detection FP16 - Device: CPU
OpenVINO
Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU
OpenVINO
Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU
OpenVINO
Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU
OpenVINO
Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU
Phoronix Test Suite v10.8.4