AMD Custom APU 0405 testing with a Valve Jupiter v1 (F7A0110 BIOS) and AMD Custom GPU 0405 1GB on SteamOS rolling 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 2309052-NE-DECKING9724
decking
AMD Custom APU 0405 testing with a Valve Jupiter v1 (F7A0110 BIOS) and AMD Custom GPU 0405 1GB on SteamOS rolling via the Phoronix Test Suite.
,,"a","b","c"
Processor,,AMD Custom APU 0405 @ 2.80GHz (4 Cores / 8 Threads),AMD Custom APU 0405 @ 2.80GHz (4 Cores / 8 Threads),AMD Custom APU 0405 @ 2.80GHz (4 Cores / 8 Threads)
Motherboard,,Valve Jupiter v1 (F7A0110 BIOS),Valve Jupiter v1 (F7A0110 BIOS),Valve Jupiter v1 (F7A0110 BIOS)
Chipset,,AMD VanGogh Root Complex,AMD VanGogh Root Complex,AMD VanGogh Root Complex
Memory,,16GB,16GB,16GB
Disk,,512GB Phison ESMP512GKB4C3-E13TS + 1000GB RTL9210B-CG,512GB Phison ESMP512GKB4C3-E13TS + 1000GB RTL9210B-CG,512GB Phison ESMP512GKB4C3-E13TS + 1000GB RTL9210B-CG
Graphics,,AMD Custom GPU 0405 1GB (1600/400MHz),AMD Custom GPU 0405 1GB (1600/400MHz),AMD Custom GPU 0405 1GB (1600/400MHz)
Audio,,AMD Rembrandt Radeon HD Audio,AMD Rembrandt Radeon HD Audio,AMD Rembrandt Radeon HD Audio
Monitor,,ANX7530 U,ANX7530 U,ANX7530 U
Network,,Realtek RTL8822CE 802.11ac PCIe,Realtek RTL8822CE 802.11ac PCIe,Realtek RTL8822CE 802.11ac PCIe
OS,,SteamOS rolling,SteamOS rolling,SteamOS rolling
Kernel,,5.13.0-valve36-1-neptune (x86_64),5.13.0-valve36-1-neptune (x86_64),5.13.0-valve36-1-neptune (x86_64)
Desktop,,KDE Plasma 5.26.1,KDE Plasma 5.26.1,KDE Plasma 5.26.1
Display Server,,X Server 1.21.1.3,X Server 1.21.1.3,X Server 1.21.1.3
OpenGL,,4.6 Mesa 22.2.0 (git-17e5312102) (LLVM 14.0.6 DRM 3.45),4.6 Mesa 22.2.0 (git-17e5312102) (LLVM 14.0.6 DRM 3.45),4.6 Mesa 22.2.0 (git-17e5312102) (LLVM 14.0.6 DRM 3.45)
Vulkan,,1.3.238,1.3.238,1.3.238
Compiler,,GCC 12.2.0,GCC 12.2.0,GCC 12.2.0
File-System,,ext4,ext4,ext4
Screen Resolution,,1280x800,1280x800,1280x800
,,"a","b","c"
"Apache IoTDB - Device Count: 100 - 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: 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: 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: 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: 500 - Batch Size Per Write: 100 - Sensor Count: 200 ()",,,,
"Apache IoTDB - Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 500 ()",,,,
"vkpeak - fp32-scalar (GFLOPS)",HIB,1638.24,1638.72,1639.27
"vkpeak - fp32-vec4 (GFLOPS)",HIB,1626.08,1626.6,1628.05
"vkpeak - fp16-scalar (GFLOPS)",HIB,1638.6,1639.31,1639.19
"vkpeak - fp16-vec4 (GFLOPS)",HIB,2618.33,2618.49,2619.64
"vkpeak - fp64-scalar (GFLOPS)",HIB,102.02,102.01,102.03
"vkpeak - fp64-vec4 (GFLOPS)",HIB,,102.03,102.06
"vkpeak - int32-scalar (GIOPS)",HIB,279.05,279.13,279.16
"vkpeak - int32-vec4 (GIOPS)",HIB,326.93,326.95,327.27
"vkpeak - int16-scalar (GIOPS)",HIB,1638.31,1638.91,1639.04
"vkpeak - int16-vec4 (GIOPS)",HIB,2620,2619.45,2621.89
"Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,2.1122,2.1213,2.3264
"Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream (items/sec)",HIB,2.1732,2.1968,2.3847
"Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,53.703,53.3204,58.1446
"Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream (items/sec)",HIB,50.1845,50.2327,55.6174
"Neural Magic DeepSparse - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,21.3085,21.0947,22.179
"Neural Magic DeepSparse - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-Stream (items/sec)",HIB,18.1783,18.1777,19.4976
"Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,6.423,6.4754,7.3794
"Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream (items/sec)",HIB,6.0942,6.1143,6.6652
"Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,26.9902,26.6504,29.4727
"Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream (items/sec)",HIB,25.1393,24.9051,27.0662
"Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,188.9603,188.3105,203.5527
"Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream (items/sec)",HIB,170.8681,170.4127,184.0044
"Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,12.9044,12.961,13.9757
"Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream (items/sec)",HIB,12.3337,12.3783,13.1751
"Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,2.7457,,
"Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream (items/sec)",HIB,2.6313,,
"Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,26.5799,28.872,29.5078
"Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream (items/sec)",HIB,24.5736,26.9723,26.9725
"Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,13.1293,14.2177,14.2805
"Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream (items/sec)",HIB,12.5215,13.509,13.5048
"Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,18.1143,19.2761,19.6773
"Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream (items/sec)",HIB,15.6395,16.377,16.4839
"Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,2.2637,2.6289,2.5672
"Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream (items/sec)",HIB,2.3648,2.556,2.5558
"Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,27.7184,29.8442,29.8109
"Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream (items/sec)",HIB,25.7729,28.554,28.7074
"Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,9.9683,11.0097,10.9375
"Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream (items/sec)",HIB,8.7687,9.4427,9.4377
"Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,2.1364,2.2798,2.342
"Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream (items/sec)",HIB,2.2291,2.3896,2.3825
"Apache Cassandra - Test: Writes (Op/s)",HIB,31081,29616,
"Apache IoTDB - Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 500 (point/sec)",HIB,711045.2,,
"Apache IoTDB - Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 500 (Latency)",LIB,56.1,,
"Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,940.7032,940.4732,853.076
"Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream (ms/batch)",LIB,460.1402,455.1871,419.3245
"Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,37.1925,37.4598,34.3521
"Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,19.9099,19.8905,17.9622
"Neural Magic DeepSparse - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,93.8248,94.7494,90.1375
"Neural Magic DeepSparse - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-Stream (ms/batch)",LIB,54.9957,54.9971,51.2707
"Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,311.1324,308.5592,270.324
"Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,164.0752,163.5343,150.0141
"Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,74.0619,75.0032,67.8272
"Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream (ms/batch)",LIB,39.7569,40.1323,36.9295
"Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,10.5418,10.5794,9.7887
"Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,5.8323,5.8481,5.4147
"Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,154.7848,154.1224,143.0633
"Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream (ms/batch)",LIB,81.0533,80.7619,75.8765
"Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,724.8871,,
"Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream (ms/batch)",LIB,380.0204,,
"Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,75.1762,69.2135,67.7439
"Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream (ms/batch)",LIB,40.6737,37.0593,37.0592
"Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,152.0727,140.6311,140.0128
"Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,79.8452,74.0106,74.033
"Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,110.3133,103.5922,101.6061
"Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream (ms/batch)",LIB,63.922,61.0449,60.6503
"Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,878.3129,760.724,778.4706
"Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream (ms/batch)",LIB,422.8352,391.2056,391.2325
"Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,72.108,66.9637,67.0376
"Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,38.7851,35.0067,34.8195
"Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,200.4555,181.6241,182.6961
"Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,114.0271,105.8874,105.9419
"Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,932.3056,877.2339,850.6684
"Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,448.6054,418.4656,419.7098