AMD Ryzen 5 4500U testing with a LENOVO LNVNB161216 (EECN20WW BIOS) and AMD Renoir 512MB on Pop 22.04 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 2302063-NE-2023RYZEN21
2023 ryzen 5
AMD Ryzen 5 4500U testing with a LENOVO LNVNB161216 (EECN20WW BIOS) and AMD Renoir 512MB on Pop 22.04 via the Phoronix Test Suite.
,,"a","b","c"
Processor,,AMD Ryzen 5 4500U @ 2.38GHz (6 Cores),AMD Ryzen 5 4500U @ 2.38GHz (6 Cores),AMD Ryzen 5 4500U @ 2.38GHz (6 Cores)
Motherboard,,LENOVO LNVNB161216 (EECN20WW BIOS),LENOVO LNVNB161216 (EECN20WW BIOS),LENOVO LNVNB161216 (EECN20WW BIOS)
Chipset,,AMD Renoir/Cezanne,AMD Renoir/Cezanne,AMD Renoir/Cezanne
Memory,,16GB,16GB,16GB
Disk,,256GB SK hynix HFM256GDHTNI-87A0B,256GB SK hynix HFM256GDHTNI-87A0B,256GB SK hynix HFM256GDHTNI-87A0B
Graphics,,AMD Renoir 512MB (1500/400MHz),AMD Renoir 512MB (1500/400MHz),AMD Renoir 512MB (1500/400MHz)
Audio,,AMD Renoir Radeon HD Audio,AMD Renoir Radeon HD Audio,AMD Renoir Radeon HD Audio
Network,,Realtek RTL8822CE 802.11ac PCIe,Realtek RTL8822CE 802.11ac PCIe,Realtek RTL8822CE 802.11ac PCIe
OS,,Pop 22.04,Pop 22.04,Pop 22.04
Kernel,,5.17.5-76051705-generic (x86_64),5.17.5-76051705-generic (x86_64),5.17.5-76051705-generic (x86_64)
Desktop,,GNOME Shell 42.1,GNOME Shell 42.1,GNOME Shell 42.1
Display Server,,X Server 1.21.1.3,X Server 1.21.1.3,X Server 1.21.1.3
OpenGL,,4.6 Mesa 22.0.1 (LLVM 13.0.1 DRM 3.44),4.6 Mesa 22.0.1 (LLVM 13.0.1 DRM 3.44),4.6 Mesa 22.0.1 (LLVM 13.0.1 DRM 3.44)
Vulkan,,1.2.204,1.2.204,1.2.204
Compiler,,GCC 11.2.0,GCC 11.2.0,GCC 11.2.0
File-System,,ext4,ext4,ext4
Screen Resolution,,1920x1080,1920x1080,1920x1080
,,"a","b","c"
"ET: Legacy - Resolution: 1920 x 1080 (FPS)",HIB,125.9,104,104.2
"Unvanquished - Resolution: 1920 x 1080 - Effects Quality: High (FPS)",HIB,162.2,130.4,123.8
"Unvanquished - Resolution: 1920 x 1080 - Effects Quality: Ultra (FPS)",HIB,126.7,90.7,86.6
"Unvanquished - Resolution: 1920 x 1080 - Effects Quality: Medium (FPS)",HIB,146.3,120.1,124.1
"VVenC - Video Input: Bosphorus 4K - Video Preset: Fast (FPS)",HIB,1.591,1.648,1.621
"VVenC - Video Input: Bosphorus 4K - Video Preset: Faster (FPS)",HIB,3.58,3.644,3.601
"VVenC - Video Input: Bosphorus 1080p - Video Preset: Fast (FPS)",HIB,5.335,5.445,5.4
"VVenC - Video Input: Bosphorus 1080p - Video Preset: Faster (FPS)",HIB,13.07,13.29,13.067
"Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,3.4136,3.3567,3.4003
"Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream (items/sec)",HIB,3.2897,3.295,3.2326
"Neural Magic DeepSparse - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,29.4655,29.6151,28.3787
"Neural Magic DeepSparse - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-Stream (items/sec)",HIB,18.0982,16.9826,16.5889
"Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,10.6658,10.6888,10.5687
"Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream (items/sec)",HIB,6.4244,6.4058,6.4858
"Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,20.1011,20.1628,20.0402
"Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream (items/sec)",HIB,17.7228,17.9517,17.7199
"Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,43.1512,43.0472,42.8792
"Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream (items/sec)",HIB,37.992,38.7942,38.6199
"Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,30.142,30.1457,30.2223
"Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream (items/sec)",HIB,24.698,25.2431,25.2744
"Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,4.0502,4.0529,4.0287
"Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream (items/sec)",HIB,4.2014,4.1893,4.1841
"Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,15.0625,15.0536,14.9442
"Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream (items/sec)",HIB,10.0495,10.5156,11.0567
"Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,3.4341,3.4302,3.4656
"Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream (items/sec)",HIB,3.2499,3.2812,3.3428
"OpenEMS - Test: pyEMS Coupler (MCells/s)",HIB,15.88,16.07,15.43
"OpenEMS - Test: openEMS MSL_NotchFilter (MCells/s)",HIB,60.99,60.86,69.33
"Memcached - Set To Get Ratio: 1:5 (Ops/sec)",HIB,686914.92,680062.15,673400.06
"Memcached - Set To Get Ratio: 1:10 (Ops/sec)",HIB,647418.98,661606.77,652041.2
"Memcached - Set To Get Ratio: 1:100 (Ops/sec)",HIB,646102.05,604441.11,664570.89
"ClickHouse - 100M Rows Hits Dataset, First Run / Cold Cache (Queries/min, Geo Mean)",HIB,62.09,61.80,60.32
"ClickHouse - 100M Rows Hits Dataset, Second Run (Queries/min, Geo Mean)",HIB,66.77,67.00,69.10
"ClickHouse - 100M Rows Hits Dataset, Third Run (Queries/min, Geo Mean)",HIB,68.21,67.68,70.22
"PostgreSQL - Scaling Factor: 1 - Clients: 1 - Mode: Read Only (TPS)",HIB,18849,18350,18694
"PostgreSQL - Scaling Factor: 1 - Clients: 1 - Mode: Read Write (TPS)",HIB,568,574,564
"PostgreSQL - Scaling Factor: 1 - Clients: 50 - Mode: Read Only (TPS)",HIB,189855,190735,187167
"PostgreSQL - Scaling Factor: 1 - Clients: 100 - Mode: Read Only (TPS)",HIB,188986,172730,187969
"PostgreSQL - Scaling Factor: 1 - Clients: 250 - Mode: Read Only (TPS)",HIB,183892,178424,184157
"PostgreSQL - Scaling Factor: 1 - Clients: 50 - Mode: Read Write (TPS)",HIB,557,551,555
"PostgreSQL - Scaling Factor: 1 - Clients: 500 - Mode: Read Only (TPS)",HIB,180688,164072,176550
"PostgreSQL - Scaling Factor: 1 - Clients: 800 - Mode: Read Only (TPS)",HIB,137557,110229,120638
"PostgreSQL - Scaling Factor: 100 - Clients: 1 - Mode: Read Only (TPS)",HIB,19549,16747,20223
"PostgreSQL - Scaling Factor: 1 - Clients: 100 - Mode: Read Write (TPS)",HIB,418,545,474
"PostgreSQL - Scaling Factor: 1 - Clients: 250 - Mode: Read Write (TPS)",HIB,500,497,489
"PostgreSQL - Scaling Factor: 1 - Clients: 500 - Mode: Read Write (TPS)",HIB,388,421,387
"PostgreSQL - Scaling Factor: 1 - Clients: 800 - Mode: Read Write (TPS)",HIB,338,329,306
"PostgreSQL - Scaling Factor: 100 - Clients: 1 - Mode: Read Write (TPS)",HIB,339,350,340
"PostgreSQL - Scaling Factor: 100 - Clients: 50 - Mode: Read Only (TPS)",HIB,185082,175404,174601
"PostgreSQL - Scaling Factor: 100 - Clients: 100 - Mode: Read Only (TPS)",HIB,163009,170386,162692
"PostgreSQL - Scaling Factor: 100 - Clients: 250 - Mode: Read Only (TPS)",HIB,154762,173250,167141
"PostgreSQL - Scaling Factor: 100 - Clients: 50 - Mode: Read Write (TPS)",HIB,4491,4770,3807
"PostgreSQL - Scaling Factor: 100 - Clients: 500 - Mode: Read Only (TPS)",HIB,168618,133815,98790
"PostgreSQL - Scaling Factor: 100 - Clients: 800 - Mode: Read Only (TPS)",HIB,106427,127387,121459
"PostgreSQL - Scaling Factor: 100 - Clients: 100 - Mode: Read Write (TPS)",HIB,5148,5036,4346
"PostgreSQL - Scaling Factor: 100 - Clients: 250 - Mode: Read Write (TPS)",HIB,5258,5060,4505
"PostgreSQL - Scaling Factor: 100 - Clients: 500 - Mode: Read Write (TPS)",HIB,5162,4721,4217
"PostgreSQL - Scaling Factor: 100 - Clients: 800 - Mode: Read Write (TPS)",HIB,4518,4514,4104
"PostgreSQL - Scaling Factor: 1 - Clients: 1 - Mode: Read Only - Average Latency (ms)",LIB,0.053,0.054,0.053
"PostgreSQL - Scaling Factor: 1 - Clients: 1 - Mode: Read Write - Average Latency (ms)",LIB,1.759,1.743,1.773
"PostgreSQL - Scaling Factor: 1 - Clients: 50 - Mode: Read Only - Average Latency (ms)",LIB,0.263,0.262,0.267
"PostgreSQL - Scaling Factor: 1 - Clients: 100 - Mode: Read Only - Average Latency (ms)",LIB,0.529,0.579,0.532
"PostgreSQL - Scaling Factor: 1 - Clients: 250 - Mode: Read Only - Average Latency (ms)",LIB,1.359,1.401,1.358
"PostgreSQL - Scaling Factor: 1 - Clients: 50 - Mode: Read Write - Average Latency (ms)",LIB,89.699,90.761,90.037
"PostgreSQL - Scaling Factor: 1 - Clients: 500 - Mode: Read Only - Average Latency (ms)",LIB,2.767,3.047,2.832
"PostgreSQL - Scaling Factor: 1 - Clients: 800 - Mode: Read Only - Average Latency (ms)",LIB,5.816,7.258,6.631
"PostgreSQL - Scaling Factor: 100 - Clients: 1 - Mode: Read Only - Average Latency (ms)",LIB,0.051,0.06,0.049
"PostgreSQL - Scaling Factor: 1 - Clients: 100 - Mode: Read Write - Average Latency (ms)",LIB,239.139,183.36,210.771
"PostgreSQL - Scaling Factor: 1 - Clients: 250 - Mode: Read Write - Average Latency (ms)",LIB,499.812,503.297,510.899
"PostgreSQL - Scaling Factor: 1 - Clients: 500 - Mode: Read Write - Average Latency (ms)",LIB,1288.174,1188.775,1293.038
"PostgreSQL - Scaling Factor: 1 - Clients: 800 - Mode: Read Write - Average Latency (ms)",LIB,2368.332,2434.467,2613.028
"PostgreSQL - Scaling Factor: 100 - Clients: 1 - Mode: Read Write - Average Latency (ms)",LIB,2.946,2.856,2.939
"PostgreSQL - Scaling Factor: 100 - Clients: 50 - Mode: Read Only - Average Latency (ms)",LIB,0.27,0.285,0.286
"PostgreSQL - Scaling Factor: 100 - Clients: 100 - Mode: Read Only - Average Latency (ms)",LIB,0.613,0.587,0.615
"PostgreSQL - Scaling Factor: 100 - Clients: 250 - Mode: Read Only - Average Latency (ms)",LIB,1.615,1.443,1.496
"PostgreSQL - Scaling Factor: 100 - Clients: 50 - Mode: Read Write - Average Latency (ms)",LIB,11.134,10.481,13.134
"PostgreSQL - Scaling Factor: 100 - Clients: 500 - Mode: Read Only - Average Latency (ms)",LIB,2.965,3.736,5.061
"PostgreSQL - Scaling Factor: 100 - Clients: 800 - Mode: Read Only - Average Latency (ms)",LIB,7.517,6.28,6.587
"PostgreSQL - Scaling Factor: 100 - Clients: 100 - Mode: Read Write - Average Latency (ms)",LIB,19.424,19.855,23.012
"PostgreSQL - Scaling Factor: 100 - Clients: 250 - Mode: Read Write - Average Latency (ms)",LIB,47.548,49.412,55.491
"PostgreSQL - Scaling Factor: 100 - Clients: 500 - Mode: Read Write - Average Latency (ms)",LIB,96.859,105.909,118.556
"PostgreSQL - Scaling Factor: 100 - Clients: 800 - Mode: Read Write - Average Latency (ms)",LIB,177.086,177.23,194.94
"Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,875.4295,887.6234,876.7307
"Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream (ms/batch)",LIB,303.97,303.4783,309.3338
"Neural Magic DeepSparse - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,101.7296,101.2123,105.5944
"Neural Magic DeepSparse - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-Stream (ms/batch)",LIB,55.2365,58.8625,60.2607
"Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,280.4971,280.501,282.9354
"Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,155.6344,156.0881,154.1594
"Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,149.0446,148.4327,149.4841
"Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream (ms/batch)",LIB,56.4089,55.6887,56.4174
"Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,69.4377,69.6415,69.8955
"Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream (ms/batch)",LIB,26.3086,25.7647,25.8809
"Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,99.4922,99.4489,99.219
"Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream (ms/batch)",LIB,40.4788,39.6044,39.556
"Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,734.5892,735.3992,738.2928
"Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream (ms/batch)",LIB,237.9964,238.6848,238.9795
"Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,199.1253,199.0679,200.012
"Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,99.4959,95.0853,90.4316
"Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,866.7573,864.3765,860.1733
"Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,307.695,304.758,299.1392
"Apache Spark - Row Count: 1000000 - Partitions: 100 - SHA-512 Benchmark Time (sec)",LIB,6.20,6.09,6.18
"Apache Spark - Row Count: 1000000 - Partitions: 100 - Calculate Pi Benchmark (sec)",LIB,382.940100565,376.454897054,379.302489887
"Apache Spark - Row Count: 1000000 - Partitions: 100 - Calculate Pi Benchmark Using Dataframe (sec)",LIB,27.820282145,27.29,27.47
"Apache Spark - Row Count: 1000000 - Partitions: 100 - Group By Test Time (sec)",LIB,5.41,5.59,5.38
"Apache Spark - Row Count: 1000000 - Partitions: 100 - Repartition Test Time (sec)",LIB,5.31,5.40,5.38
"Apache Spark - Row Count: 1000000 - Partitions: 100 - Inner Join Test Time (sec)",LIB,3.569670803,3.88,3.77
"Apache Spark - Row Count: 1000000 - Partitions: 100 - Broadcast Inner Join Test Time (sec)",LIB,3.19,3.10,3.15
"Apache Spark - Row Count: 1000000 - Partitions: 500 - SHA-512 Benchmark Time (sec)",LIB,6.60,6.96,6.88
"Apache Spark - Row Count: 1000000 - Partitions: 500 - Calculate Pi Benchmark (sec)",LIB,380.628848195,376.192376586,377.47859139
"Apache Spark - Row Count: 1000000 - Partitions: 500 - Calculate Pi Benchmark Using Dataframe (sec)",LIB,27.703114061,26.911759659,27.50
"Apache Spark - Row Count: 1000000 - Partitions: 500 - Group By Test Time (sec)",LIB,6.30,6.53,6.65
"Apache Spark - Row Count: 1000000 - Partitions: 500 - Repartition Test Time (sec)",LIB,5.41,5.46,5.48
"Apache Spark - Row Count: 1000000 - Partitions: 500 - Inner Join Test Time (sec)",LIB,4.36,4.53,4.47
"Apache Spark - Row Count: 1000000 - Partitions: 500 - Broadcast Inner Join Test Time (sec)",LIB,3.84,3.84,3.80
"Apache Spark - Row Count: 1000000 - Partitions: 1000 - SHA-512 Benchmark Time (sec)",LIB,7.25,7.75,7.27
"Apache Spark - Row Count: 1000000 - Partitions: 1000 - Calculate Pi Benchmark (sec)",LIB,380.687543133,379.89,378.635938282
"Apache Spark - Row Count: 1000000 - Partitions: 1000 - Calculate Pi Benchmark Using Dataframe (sec)",LIB,27.393841671,27.36,27.33
"Apache Spark - Row Count: 1000000 - Partitions: 1000 - Group By Test Time (sec)",LIB,7.04,6.80,6.93
"Apache Spark - Row Count: 1000000 - Partitions: 1000 - Repartition Test Time (sec)",LIB,6.14,5.86,6.31
"Apache Spark - Row Count: 1000000 - Partitions: 1000 - Inner Join Test Time (sec)",LIB,5.41,5.44,5.52
"Apache Spark - Row Count: 1000000 - Partitions: 1000 - Broadcast Inner Join Test Time (sec)",LIB,4.57,4.60,4.60
"Apache Spark - Row Count: 1000000 - Partitions: 2000 - SHA-512 Benchmark Time (sec)",LIB,7.89,7.93,7.88
"Apache Spark - Row Count: 1000000 - Partitions: 2000 - Calculate Pi Benchmark (sec)",LIB,379.064581115,378.345235465,379.193265034
"Apache Spark - Row Count: 1000000 - Partitions: 2000 - Calculate Pi Benchmark Using Dataframe (sec)",LIB,27.350376753,27.62,27.221595518
"Apache Spark - Row Count: 1000000 - Partitions: 2000 - Group By Test Time (sec)",LIB,8.09,8.02,8.05
"Apache Spark - Row Count: 1000000 - Partitions: 2000 - Repartition Test Time (sec)",LIB,7.20,7.03,7.03
"Apache Spark - Row Count: 1000000 - Partitions: 2000 - Inner Join Test Time (sec)",LIB,6.90,7.35,7.36
"Apache Spark - Row Count: 1000000 - Partitions: 2000 - Broadcast Inner Join Test Time (sec)",LIB,5.57,6.12,5.54