5800x3d smoke okt
AMD Ryzen 7 5800X3D 8-Core testing with a ASUS ROG CROSSHAIR VIII HERO (4201 BIOS) and Intel DG2 8GB on Ubuntu 22.04 via the Phoronix Test Suite.
HTML result view exported from: https://openbenchmarking.org/result/2210143-PTS-5800X3DS35.
SMHasher
Hash: wyhash
SMHasher
Hash: wyhash
SMHasher
Hash: SHA3-256
SMHasher
Hash: SHA3-256
SMHasher
Hash: Spooky32
SMHasher
Hash: Spooky32
SMHasher
Hash: fasthash32
SMHasher
Hash: fasthash32
SMHasher
Hash: FarmHash128
SMHasher
Hash: FarmHash128
SMHasher
Hash: t1ha2_atonce
SMHasher
Hash: t1ha2_atonce
SMHasher
Hash: FarmHash32 x86_64 AVX
SMHasher
Hash: FarmHash32 x86_64 AVX
SMHasher
Hash: t1ha0_aes_avx2 x86_64
SMHasher
Hash: t1ha0_aes_avx2 x86_64
SMHasher
Hash: MeowHash x86_64 AES-NI
SMHasher
Hash: MeowHash x86_64 AES-NI
QuadRay
Scene: 1 - Resolution: 4K
QuadRay
Scene: 2 - Resolution: 4K
QuadRay
Scene: 3 - Resolution: 4K
QuadRay
Scene: 5 - Resolution: 4K
QuadRay
Scene: 1 - Resolution: 1080p
QuadRay
Scene: 2 - Resolution: 1080p
QuadRay
Scene: 3 - Resolution: 1080p
QuadRay
Scene: 5 - Resolution: 1080p
AOM AV1
Encoder Mode: Speed 0 Two-Pass - Input: Bosphorus 4K
AOM AV1
Encoder Mode: Speed 4 Two-Pass - Input: Bosphorus 4K
AOM AV1
Encoder Mode: Speed 6 Realtime - Input: Bosphorus 4K
AOM AV1
Encoder Mode: Speed 6 Two-Pass - Input: Bosphorus 4K
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 0 Two-Pass - Input: Bosphorus 1080p
AOM AV1
Encoder Mode: Speed 4 Two-Pass - Input: Bosphorus 1080p
AOM AV1
Encoder Mode: Speed 6 Realtime - Input: Bosphorus 1080p
AOM AV1
Encoder Mode: Speed 6 Two-Pass - Input: Bosphorus 1080p
AOM AV1
Encoder Mode: Speed 8 Realtime - Input: Bosphorus 1080p
AOM AV1
Encoder Mode: Speed 9 Realtime - Input: Bosphorus 1080p
AOM AV1
Encoder Mode: Speed 10 Realtime - Input: Bosphorus 1080p
Y-Cruncher
Pi Digits To Calculate: 1B
Y-Cruncher
Pi Digits To Calculate: 500M
oneDNN
Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU
oneDNN
Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU
oneDNN
Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU
oneDNN
Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU
oneDNN
Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU
oneDNN
Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU
oneDNN
Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU
oneDNN
Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU
oneDNN
Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU
oneDNN
Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU
oneDNN
Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU
oneDNN
Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU
oneDNN
Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU
oneDNN
Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU
oneDNN
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU
oneDNN
Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU
oneDNN
Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU
oneDNN
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU
TensorFlow
Device: CPU - Batch Size: 16 - Model: VGG-16
TensorFlow
Device: CPU - Batch Size: 32 - Model: VGG-16
TensorFlow
Device: CPU - Batch Size: 64 - Model: VGG-16
TensorFlow
Device: CPU - Batch Size: 16 - Model: AlexNet
TensorFlow
Device: CPU - Batch Size: 256 - Model: VGG-16
TensorFlow
Device: CPU - Batch Size: 32 - Model: AlexNet
TensorFlow
Device: CPU - Batch Size: 64 - Model: AlexNet
TensorFlow
Device: CPU - Batch Size: 256 - Model: AlexNet
TensorFlow
Device: CPU - Batch Size: 512 - Model: AlexNet
TensorFlow
Device: CPU - Batch Size: 16 - Model: GoogLeNet
TensorFlow
Device: CPU - Batch Size: 16 - Model: ResNet-50
TensorFlow
Device: CPU - Batch Size: 32 - Model: GoogLeNet
TensorFlow
Device: CPU - Batch Size: 32 - Model: ResNet-50
TensorFlow
Device: CPU - Batch Size: 64 - Model: GoogLeNet
TensorFlow
Device: CPU - Batch Size: 64 - Model: ResNet-50
TensorFlow
Device: CPU - Batch Size: 256 - Model: GoogLeNet
TensorFlow
Device: CPU - Batch Size: 256 - Model: ResNet-50
TensorFlow
Device: CPU - Batch Size: 512 - Model: GoogLeNet
spaCy
Model: en_core_web_lg
spaCy
Model: en_core_web_trf
OpenRadioss
Model: Bumper Beam
OpenRadioss
Model: Cell Phone Drop Test
OpenRadioss
Model: Bird Strike on Windshield
OpenRadioss
Model: Rubber O-Ring Seal Installation
OpenRadioss
Model: INIVOL and Fluid Structure Interaction Drop Container
PostgreSQL
Scaling Factor: 1 - Clients: 1 - Mode: Read Only
PostgreSQL
Scaling Factor: 1 - Clients: 1 - Mode: Read Only - Average Latency
PostgreSQL
Scaling Factor: 1 - Clients: 1 - Mode: Read Write
PostgreSQL
Scaling Factor: 1 - Clients: 1 - Mode: Read Write - Average Latency
PostgreSQL
Scaling Factor: 1 - Clients: 50 - Mode: Read Only
PostgreSQL
Scaling Factor: 1 - Clients: 50 - Mode: Read Only - Average Latency
PostgreSQL
Scaling Factor: 1 - Clients: 100 - Mode: Read Only
PostgreSQL
Scaling Factor: 1 - Clients: 100 - Mode: Read Only - Average Latency
PostgreSQL
Scaling Factor: 1 - Clients: 50 - Mode: Read Write
PostgreSQL
Scaling Factor: 1 - Clients: 50 - Mode: Read Write - Average Latency
PostgreSQL
Scaling Factor: 100 - Clients: 1 - Mode: Read Only
PostgreSQL
Scaling Factor: 100 - Clients: 1 - Mode: Read Only - Average Latency
PostgreSQL
Scaling Factor: 1 - Clients: 100 - Mode: Read Write
PostgreSQL
Scaling Factor: 1 - Clients: 100 - Mode: Read Write - Average Latency
PostgreSQL
Scaling Factor: 100 - Clients: 1 - Mode: Read Write
PostgreSQL
Scaling Factor: 100 - Clients: 1 - Mode: Read Write - Average Latency
PostgreSQL
Scaling Factor: 100 - Clients: 50 - Mode: Read Only
PostgreSQL
Scaling Factor: 100 - Clients: 50 - Mode: Read Only - Average Latency
PostgreSQL
Scaling Factor: 100 - Clients: 100 - Mode: Read Only
PostgreSQL
Scaling Factor: 100 - Clients: 100 - Mode: Read Only - Average Latency
PostgreSQL
Scaling Factor: 100 - Clients: 50 - Mode: Read Write
PostgreSQL
Scaling Factor: 100 - Clients: 50 - Mode: Read Write - Average Latency
PostgreSQL
Scaling Factor: 100 - Clients: 100 - Mode: Read Write
PostgreSQL
Scaling Factor: 100 - Clients: 100 - Mode: Read Write - Average Latency
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 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 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: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-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: CV Detection,YOLOv5s COCO - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: CV Detection,YOLOv5s COCO - 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: 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: 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: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-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 Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-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
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
Phoronix Test Suite v10.8.5