test
lxc testing on Debian GNU/Linux 12 via the Phoronix Test Suite.
HTML result view exported from: https://openbenchmarking.org/result/2411144-NE-TEST4170061&grr.
TensorFlow
Device: CPU - Batch Size: 512 - Model: VGG-16
TensorFlow
Device: CPU - Batch Size: 256 - Model: VGG-16
TensorFlow
Device: GPU - Batch Size: 32 - Model: VGG-16
Xcompact3d Incompact3d
Input: X3D-benchmarking input.i3d
TensorFlow
Device: GPU - Batch Size: 64 - Model: VGG-16
Timed GCC Compilation
Time To Compile
TensorFlow
Device: GPU - Batch Size: 16 - Model: VGG-16
Timed Node.js Compilation
Time To Compile
QuantLib
Size: S
Llama.cpp
Model: llama-2-70b-chat.Q5_0.gguf
Whisper.cpp
Model: ggml-medium.en - Input: 2016 State of the Union
PyTorch
Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_l
PyTorch
Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_l
PyTorch
Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l
LeelaChessZero
Backend: Eigen
LeelaChessZero
Backend: BLAS
TensorFlow
Device: CPU - Batch Size: 64 - Model: VGG-16
Stockfish
Total Time
Blender
Blend File: Barbershop - Compute: CPU-Only
PyTorch
Device: CPU - Batch Size: 64 - Model: ResNet-152
PyTorch
Device: CPU - Batch Size: 512 - Model: ResNet-152
Mobile Neural Network
Model: inception-v3
Mobile Neural Network
Model: mobilenet-v1-1.0
Mobile Neural Network
Model: MobileNetV2_224
Mobile Neural Network
Model: SqueezeNetV1.0
Mobile Neural Network
Model: resnet-v2-50
Mobile Neural Network
Model: squeezenetv1.1
Mobile Neural Network
Model: mobilenetV3
Mobile Neural Network
Model: nasnet
Xmrig
Variant: GhostRider - Hash Count: 1M
Llamafile
Test: mistral-7b-instruct-v0.2.Q5_K_M - Acceleration: CPU
Renaissance
Test: Savina Reactors.IO
Timed LLVM Compilation
Build System: Unix Makefiles
TensorFlow
Device: CPU - Batch Size: 32 - Model: VGG-16
Whisper.cpp
Model: ggml-small.en - Input: 2016 State of the Union
NCNN
Target: CPU - Model: FastestDet
NCNN
Target: CPU - Model: vision_transformer
NCNN
Target: CPU - Model: regnety_400m
NCNN
Target: CPU - Model: squeezenet_ssd
NCNN
Target: CPU - Model: yolov4-tiny
NCNN
Target: CPUv2-yolov3v2-yolov3 - Model: mobilenetv2-yolov3
NCNN
Target: CPU - Model: resnet50
NCNN
Target: CPU - Model: alexnet
NCNN
Target: CPU - Model: resnet18
NCNN
Target: CPU - Model: vgg16
NCNN
Target: CPU - Model: googlenet
NCNN
Target: CPU - Model: blazeface
NCNN
Target: CPU - Model: efficientnet-b0
NCNN
Target: CPU - Model: mnasnet
NCNN
Target: CPU - Model: shufflenet-v2
NCNN
Target: CPU-v3-v3 - Model: mobilenet-v3
NCNN
Target: CPU-v2-v2 - Model: mobilenet-v2
NCNN
Target: CPU - Model: mobilenet
Timed LLVM Compilation
Build System: Ninja
QuantLib
Size: XXS
PyTorch
Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_l
PyTorch
Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_l
PyTorch
Device: CPU - Batch Size: 1 - Model: ResNet-152
InfluxDB
Concurrent Streams: 64 - Batch Size: 10000 - Tags: 2,5000,1 - Points Per Series: 10000
Llama.cpp
Model: Meta-Llama-3-8B-Instruct-Q8_0.gguf
PyTorch
Device: CPU - Batch Size: 256 - Model: ResNet-50
InfluxDB
Concurrent Streams: 4 - Batch Size: 10000 - Tags: 2,5000,1 - Points Per Series: 10000
Llama.cpp
Model: llama-2-13b.Q4_0.gguf
TensorFlow Lite
Model: Inception V4
TensorFlow Lite
Model: Inception ResNet V2
TensorFlow Lite
Model: NASNet Mobile
GraphicsMagick
Operation: Resizing
TensorFlow
Device: CPU - Batch Size: 16 - Model: VGG-16
Blender
Blend File: Classroom - Compute: CPU-Only
TNN
Target: CPU - Model: DenseNet
Apache Siege
Concurrent Users: 1000
Apache Siege
Concurrent Users: 500
Numpy Benchmark
OpenCV
Test: DNN - Deep Neural Network
miniBUDE
Implementation: OpenMP - Input Deck: BM2
miniBUDE
Implementation: OpenMP - Input Deck: BM2
PyTorch
Device: CPU - Batch Size: 16 - Model: ResNet-152
PyTorch
Device: CPU - Batch Size: 32 - Model: ResNet-152
TensorFlow Lite
Model: Mobilenet Float
TensorFlow Lite
Model: SqueezeNet
PyTorch
Device: CPU - Batch Size: 256 - Model: ResNet-152
GraphicsMagick
Operation: Swirl
OpenRadioss
Model: Bird Strike on Windshield
Blender
Blend File: Pabellon Barcelona - Compute: CPU-Only
CacheBench
Test: Write
PyTorch
Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l
Whisper.cpp
Model: ggml-base.en - Input: 2016 State of the Union
XNNPACK
Model: QS8MobileNetV2
XNNPACK
Model: FP16MobileNetV3Small
XNNPACK
Model: FP16MobileNetV3Large
XNNPACK
Model: FP16MobileNetV2
XNNPACK
Model: FP16MobileNetV1
XNNPACK
Model: FP32MobileNetV3Small
XNNPACK
Model: FP32MobileNetV3Large
XNNPACK
Model: FP32MobileNetV2
XNNPACK
Model: FP32MobileNetV1
Apache Siege
Concurrent Users: 200
DeepSpeech
Acceleration: CPU
CLOMP
Static OMP Speedup
Redis
Test: SET - Parallel Connections: 500
Numenta Anomaly Benchmark
Detector: KNN CAD
TensorFlow
Device: GPU - Batch Size: 16 - Model: AlexNet
asmFish
1024 Hash Memory, 26 Depth
Redis
Test: LPOP - Parallel Connections: 500
PyTorch
Device: CPU - Batch Size: 64 - Model: ResNet-50
NAMD
Input: STMV with 1,066,628 Atoms
libavif avifenc
Encoder Speed: 0
R Benchmark
OpenRadioss
Model: Bumper Beam
Redis
Test: LPOP - Parallel Connections: 1000
TensorFlow
Device: GPU - Batch Size: 1 - Model: VGG-16
Redis
Test: LPOP - Parallel Connections: 50
Rodinia
Test: OpenMP HotSpot3D
AOM AV1
Encoder Mode: Speed 4 Two-Pass - Input: Bosphorus 4K
Rodinia
Test: OpenMP LavaMD
NCNN
Target: Vulkan GPU - Model: FastestDet
NCNN
Target: Vulkan GPU - Model: vision_transformer
NCNN
Target: Vulkan GPU - Model: regnety_400m
NCNN
Target: Vulkan GPU - Model: squeezenet_ssd
NCNN
Target: Vulkan GPU - Model: yolov4-tiny
NCNN
Target: Vulkan GPUv2-yolov3v2-yolov3 - Model: mobilenetv2-yolov3
NCNN
Target: Vulkan GPU - Model: resnet50
NCNN
Target: Vulkan GPU - Model: alexnet
NCNN
Target: Vulkan GPU - Model: resnet18
NCNN
Target: Vulkan GPU - Model: vgg16
NCNN
Target: Vulkan GPU - Model: googlenet
NCNN
Target: Vulkan GPU - Model: blazeface
NCNN
Target: Vulkan GPU - Model: efficientnet-b0
NCNN
Target: Vulkan GPU - Model: mnasnet
NCNN
Target: Vulkan GPU - Model: shufflenet-v2
NCNN
Target: Vulkan GPU-v3-v3 - Model: mobilenet-v3
NCNN
Target: Vulkan GPU-v2-v2 - Model: mobilenet-v2
NCNN
Target: Vulkan GPU - Model: mobilenet
PyTorch
Device: CPU - Batch Size: 32 - Model: ResNet-50
Xmrig
Variant: CryptoNight-Heavy - Hash Count: 1M
Xmrig
Variant: Monero - Hash Count: 1M
Xmrig
Variant: KawPow - Hash Count: 1M
CacheBench
Test: Read / Modify / Write
CacheBench
Test: Read
Xmrig
Variant: CryptoNight-Femto UPX2 - Hash Count: 1M
AOM AV1
Encoder Mode: Speed 6 Two-Pass - Input: Bosphorus 1080p
Redis
Test: LPUSH - Parallel Connections: 500
Numenta Anomaly Benchmark
Detector: Earthgecko Skyline
InfluxDB
Concurrent Streams: 1024 - Batch Size: 10000 - Tags: 2,5000,1 - Points Per Series: 10000
AOM AV1
Encoder Mode: Speed 6 Realtime - Input: Bosphorus 4K
Blender
Blend File: Fishy Cat - Compute: CPU-Only
Redis
Test: SADD - Parallel Connections: 50
Blender
Blend File: Junkshop - Compute: CPU-Only
AOM AV1
Encoder Mode: Speed 0 Two-Pass - Input: Bosphorus 4K
PyTorch
Device: CPU - Batch Size: 512 - Model: ResNet-50
PyTorch
Device: CPU - Batch Size: 16 - Model: ResNet-50
Neural Magic DeepSparse
Model: Llama2 Chat 7b Quantized - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: Llama2 Chat 7b Quantized - Scenario: Asynchronous Multi-Stream
Apache Siege
Concurrent Users: 100
Redis
Test: SET - Parallel Connections: 1000
OpenRadioss
Model: Cell Phone Drop Test
Xmrig
Variant: Wownero - Hash Count: 1M
libavif avifenc
Encoder Speed: 2
Blender
Blend File: BMW27 - Compute: CPU-Only
oneDNN
Harness: Recurrent Neural Network Training - Engine: CPU
Timed PHP Compilation
Time To Compile
PyTorch
Device: CPU - Batch Size: 1 - Model: ResNet-50
Timed Wasmer Compilation
Time To Compile
oneDNN
Harness: Recurrent Neural Network Inference - Engine: CPU
DaCapo Benchmark
Java Test: Tradebeans
TensorFlow
Device: CPU - Batch Size: 64 - Model: AlexNet
Zstd Compression
Compression Level: 19 - Decompression Speed
Zstd Compression
Compression Level: 19 - Compression Speed
OpenVINO
Model: Face Detection FP16 - Device: CPU
OpenVINO
Model: Face Detection FP16 - Device: CPU
Llama.cpp
Model: llama-2-7b.Q4_0.gguf
Rodinia
Test: OpenMP Streamcluster
TensorFlow
Device: CPU - Batch Size: 1 - Model: VGG-16
AOM AV1
Encoder Mode: Speed 6 Two-Pass - Input: Bosphorus 4K
Zstd Compression
Compression Level: 19, Long Mode - Decompression Speed
Zstd Compression
Compression Level: 19, Long Mode - Compression Speed
Redis 7.0.12 + memtier_benchmark
Protocol: Redis - Clients: 100 - Set To Get Ratio: 10:1
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: 100 - Set To Get Ratio: 1:10
Redis 7.0.12 + memtier_benchmark
Protocol: Redis - Clients: 100 - Set To Get Ratio: 5:1
Redis 7.0.12 + memtier_benchmark
Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:1
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: 50 - Set To Get Ratio: 10:1
Redis 7.0.12 + memtier_benchmark
Protocol: Redis - Clients: 50 - Set To Get Ratio: 1:1
Redis 7.0.12 + memtier_benchmark
Protocol: Redis - Clients: 50 - Set To Get Ratio: 5:1
OpenVINO
Model: Face Detection FP16-INT8 - Device: CPU
OpenVINO
Model: Face Detection FP16-INT8 - Device: CPU
Redis 7.0.12 + memtier_benchmark
Protocol: Redis - Clients: 50 - Set To Get Ratio: 1:5
Memcached
Set To Get Ratio: 1:1
Rodinia
Test: OpenMP Leukocyte
Memcached
Set To Get Ratio: 1:10
Memcached
Set To Get Ratio: 1:5
Memcached
Set To Get Ratio: 1:100
Memcached
Set To Get Ratio: 5:1
Zstd Compression
Compression Level: 3 - Decompression Speed
Zstd Compression
Compression Level: 3 - Compression Speed
Zstd Compression
Compression Level: 8, Long Mode - Decompression Speed
Zstd Compression
Compression Level: 8, Long Mode - Compression Speed
Zstd Compression
Compression Level: 8 - Decompression Speed
Zstd Compression
Compression Level: 8 - Compression Speed
Zstd Compression
Compression Level: 3, Long Mode - Decompression Speed
Zstd Compression
Compression Level: 3, Long Mode - Compression Speed
Zstd Compression
Compression Level: 12 - Decompression Speed
Zstd Compression
Compression Level: 12 - Compression Speed
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 Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream
Redis
Test: SET - Parallel Connections: 50
OpenVINO
Model: Machine Translation EN To DE FP16 - Device: CPU
OpenVINO
Model: Machine Translation EN To DE FP16 - Device: CPU
OpenVINO
Model: Person Detection FP32 - Device: CPU
OpenVINO
Model: Person Detection FP32 - Device: CPU
OpenVINO
Model: Person Detection FP16 - Device: CPU
OpenVINO
Model: Person Detection FP16 - Device: CPU
OpenVINO
Model: Road Segmentation ADAS FP16-INT8 - Device: CPU
OpenVINO
Model: Road Segmentation ADAS FP16-INT8 - Device: CPU
OpenVINO
Model: Noise Suppression Poconet-Like FP16 - Device: CPU
OpenVINO
Model: Noise Suppression Poconet-Like FP16 - Device: CPU
OpenVINO
Model: Person Vehicle Bike Detection FP16 - Device: CPU
OpenVINO
Model: Person Vehicle Bike Detection FP16 - Device: CPU
OpenVINO
Model: Handwritten English Recognition FP16-INT8 - Device: CPU
OpenVINO
Model: Handwritten English Recognition FP16-INT8 - Device: CPU
Neural Magic DeepSparse
Model: Llama2 Chat 7b Quantized - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: Llama2 Chat 7b Quantized - Scenario: Synchronous Single-Stream
OpenVINO
Model: Person Re-Identification Retail FP16 - Device: CPU
OpenVINO
Model: Person Re-Identification Retail FP16 - Device: CPU
OpenVINO
Model: Road Segmentation ADAS FP16 - Device: CPU
OpenVINO
Model: Road Segmentation ADAS FP16 - Device: CPU
OpenVINO
Model: Vehicle Detection FP16-INT8 - Device: CPU
OpenVINO
Model: Vehicle Detection FP16-INT8 - Device: CPU
OpenVINO
Model: Handwritten English Recognition FP16 - Device: CPU
OpenVINO
Model: Handwritten English Recognition FP16 - Device: CPU
OpenVINO
Model: Face Detection Retail FP16-INT8 - Device: CPU
OpenVINO
Model: Face Detection Retail FP16-INT8 - Device: CPU
OpenVINO
Model: Weld Porosity Detection FP16-INT8 - Device: CPU
OpenVINO
Model: Weld Porosity Detection FP16-INT8 - Device: CPU
OpenVINO
Model: Vehicle Detection FP16 - Device: CPU
OpenVINO
Model: Vehicle Detection FP16 - Device: CPU
OpenVINO
Model: Weld Porosity Detection FP16 - Device: CPU
OpenVINO
Model: Weld Porosity Detection 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
OpenVINO
Model: Face Detection Retail FP16 - Device: CPU
OpenVINO
Model: Face Detection Retail FP16 - Device: CPU
OpenVINO
Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU
OpenVINO
Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU
GraphicsMagick
Operation: Noise-Gaussian
TensorFlow Lite
Model: Mobilenet Quant
GraphicsMagick
Operation: Sharpen
GraphicsMagick
Operation: Enhanced
GraphicsMagick
Operation: Rotate
GraphicsMagick
Operation: HWB Color Space
NAMD
Input: ATPase with 327,506 Atoms
AOM AV1
Encoder Mode: Speed 4 Two-Pass - Input: Bosphorus 1080p
Hackbench
Count: 32 - Type: Process
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
Himeno Benchmark
Poisson Pressure Solver
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 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
Redis
Test: LPUSH - Parallel Connections: 1000
Neural Magic DeepSparse
Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream
AOM AV1
Encoder Mode: Speed 6 Realtime - Input: Bosphorus 1080p
AOM AV1
Encoder Mode: Speed 8 Realtime - Input: Bosphorus 1080p
Neural Magic DeepSparse
Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream
Xcompact3d Incompact3d
Input: input.i3d 193 Cells Per Direction
7-Zip Compression
Test: Decompression Rating
7-Zip Compression
Test: Compression Rating
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
AOM AV1
Encoder Mode: Speed 9 Realtime - Input: Bosphorus 1080p
Numenta Anomaly Benchmark
Detector: Bayesian Changepoint
Rust Mandelbrot
Time To Complete Serial/Parallel Mandelbrot
NAS Parallel Benchmarks
Test / Class: LU.C
AOM AV1
Encoder Mode: Speed 10 Realtime - Input: Bosphorus 1080p
AOM AV1
Encoder Mode: Speed 11 Realtime - Input: Bosphorus 1080p
Neural Magic DeepSparse
Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream
Apache Siege
Concurrent Users: 50
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
Numenta Anomaly Benchmark
Detector: Contextual Anomaly Detector OSE
TensorFlow
Device: CPU - Batch Size: 32 - Model: AlexNet
Redis
Test: LPUSH - Parallel Connections: 50
Numenta Anomaly Benchmark
Detector: Windowed Gaussian
Redis
Test: SADD - Parallel Connections: 1000
Neural Magic DeepSparse
Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream
miniBUDE
Implementation: OpenMP - Input Deck: BM1
miniBUDE
Implementation: OpenMP - Input Deck: BM1
Redis
Test: SADD - Parallel Connections: 500
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, Sparse INT8 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream
Pennant
Test: sedovbig
Neural Magic DeepSparse
Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-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: ResNet-50, Baseline - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream
m-queens
Time To Solve
Redis
Test: GET - Parallel Connections: 1000
Redis
Test: GET - Parallel Connections: 500
TensorFlow
Device: GPU - Batch Size: 1 - Model: AlexNet
Cython Benchmark
Test: N-Queens
DaCapo Benchmark
Java Test: Jython
AOM AV1
Encoder Mode: Speed 0 Two-Pass - Input: Bosphorus 1080p
Aircrack-ng
John The Ripper
Test: Blowfish
Cpuminer-Opt
Algorithm: Myriad-Groestl
Cpuminer-Opt
Algorithm: x20r
Cpuminer-Opt
Algorithm: Skeincoin
Cpuminer-Opt
Algorithm: Magi
Cpuminer-Opt
Algorithm: Blake-2 S
Cpuminer-Opt
Algorithm: Deepcoin
Cpuminer-Opt
Algorithm: scrypt
Cpuminer-Opt
Algorithm: LBC, LBRY Credits
Cpuminer-Opt
Algorithm: Garlicoin
Cpuminer-Opt
Algorithm: Triple SHA-256, Onecoin
Cpuminer-Opt
Algorithm: Ringcoin
Cpuminer-Opt
Algorithm: Quad SHA-256, Pyrite
Numenta Anomaly Benchmark
Detector: Relative Entropy
Redis
Test: GET - Parallel Connections: 50
TensorFlow
Device: CPU - Batch Size: 16 - Model: AlexNet
TNN
Target: CPU - Model: MobileNet v2
PyBench
Total For Average Test Times
POV-Ray
Trace Time
TNN
Target: CPU - Model: SqueezeNet v1.1
Algebraic Multi-Grid Benchmark
oneDNN
Harness: Deconvolution Batch shapes_1d - Engine: CPU
Xcompact3d Incompact3d
Input: input.i3d 129 Cells Per Direction
Pennant
Test: leblancbig
AOM AV1
Encoder Mode: Speed 8 Realtime - Input: Bosphorus 4K
AOM AV1
Encoder Mode: Speed 9 Realtime - Input: Bosphorus 4K
AOM AV1
Encoder Mode: Speed 10 Realtime - Input: Bosphorus 4K
AOM AV1
Encoder Mode: Speed 11 Realtime - Input: Bosphorus 4K
RNNoise
Input: 26 Minute Long Talking Sample
TensorFlow
Device: CPU - Batch Size: 1 - Model: AlexNet
oneDNN
Harness: IP Shapes 1D - Engine: CPU
oneDNN
Harness: Deconvolution Batch shapes_3d - Engine: CPU
libavif avifenc
Encoder Speed: 6, Lossless
Rodinia
Test: OpenMP CFD Solver
oneDNN
Harness: IP Shapes 3D - Engine: CPU
Apache Siege
Concurrent Users: 10
libavif avifenc
Encoder Speed: 10, Lossless
Glibc Benchmarks
Benchmark: sincos
libavif avifenc
Encoder Speed: 6
TNN
Target: CPU - Model: SqueezeNet v2
oneDNN
Harness: Convolution Batch Shapes Auto - Engine: CPU
NAS Parallel Benchmarks
Test / Class: EP.C
ctx_clock
Context Switch Time
Glibc Benchmarks
Benchmark: exp
Glibc Benchmarks
Benchmark: sin
Glibc Benchmarks
Benchmark: pow
Glibc Benchmarks
Benchmark: cos
Glibc Benchmarks
Benchmark: tanh
Glibc Benchmarks
Benchmark: sinh
Glibc Benchmarks
Benchmark: asinh
Glibc Benchmarks
Benchmark: pthread_once
Glibc Benchmarks
Benchmark: ffs
Glibc Benchmarks
Benchmark: atanh
Glibc Benchmarks
Benchmark: log2
Glibc Benchmarks
Benchmark: ffsll
Glibc Benchmarks
Benchmark: sqrt
Glibc Benchmarks
Benchmark: modf
C-Ray
Total Time - 4K, 16 Rays Per Pixel
Phoronix Test Suite v10.8.5