dwq

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.

Compare your own system(s) to this result file with the Phoronix Test Suite by running the command: phoronix-test-suite benchmark 2302021-NE-DWQ81086357
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C/C++ Compiler Tests 2 Tests
Database Test Suite 6 Tests
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Python Tests 3 Tests
Server 6 Tests

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February 01 2023
  10 Hours, 57 Minutes
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February 02 2023
  11 Hours, 5 Minutes
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February 02 2023
  11 Hours, 5 Minutes
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dwq Suite 1.0.0 System Test suite extracted from dwq. pts/pgbench-1.13.0 -s 1 -c 250 -S Scaling Factor: 1 - Clients: 250 - Mode: Read Only - Average Latency pts/pgbench-1.13.0 -s 1 -c 250 -S Scaling Factor: 1 - Clients: 250 - Mode: Read Only pts/pgbench-1.13.0 -s 100 -c 100 Scaling Factor: 100 - Clients: 100 - Mode: Read Write pts/pgbench-1.13.0 -s 100 -c 100 Scaling Factor: 100 - Clients: 100 - Mode: Read Write - Average Latency pts/pgbench-1.13.0 -s 100 -c 500 Scaling Factor: 100 - Clients: 500 - Mode: Read Write pts/pgbench-1.13.0 -s 100 -c 500 Scaling Factor: 100 - Clients: 500 - Mode: Read Write - Average Latency pts/pgbench-1.13.0 -s 100 -c 250 Scaling Factor: 100 - Clients: 250 - Mode: Read Write - Average Latency pts/pgbench-1.13.0 -s 100 -c 250 Scaling Factor: 100 - Clients: 250 - Mode: Read Write pts/pgbench-1.13.0 -s 100 -c 1000 Scaling Factor: 100 - Clients: 1000 - Mode: Read Write pts/pgbench-1.13.0 -s 100 -c 1000 Scaling Factor: 100 - Clients: 1000 - Mode: Read Write - Average Latency pts/pgbench-1.13.0 -s 100 -c 1000 -S Scaling Factor: 100 - Clients: 1000 - Mode: Read Only pts/pgbench-1.13.0 -s 100 -c 1000 -S Scaling Factor: 100 - Clients: 1000 - Mode: Read Only - Average Latency pts/pgbench-1.13.0 -s 1 -c 500 -S Scaling Factor: 1 - Clients: 500 - Mode: Read Only - Average Latency pts/pgbench-1.13.0 -s 1 -c 500 -S Scaling Factor: 1 - Clients: 500 - Mode: Read Only pts/pgbench-1.13.0 -s 100 -c 800 -S Scaling Factor: 100 - Clients: 800 - Mode: Read Only - Average Latency pts/pgbench-1.13.0 -s 100 -c 800 -S Scaling Factor: 100 - Clients: 800 - Mode: Read Only pts/deepsparse-1.3.2 zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/pruned90-none --scenario async Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream pts/spark-1.0.1 -r 1000000 -p 100 Row Count: 1000000 - Partitions: 100 - Repartition Test Time pts/pgbench-1.13.0 -s 1 -c 1 -S Scaling Factor: 1 - Clients: 1 - Mode: Read Only pts/pgbench-1.13.0 -s 1 -c 1 -S Scaling Factor: 1 - Clients: 1 - Mode: Read Only - Average Latency pts/keydb-1.4.0 -t lpop -c 50 Test: LPOP - Parallel Connections: 50 pts/pgbench-1.13.0 -s 100 -c 50 Scaling Factor: 100 - Clients: 50 - Mode: Read Write pts/pgbench-1.13.0 -s 100 -c 50 Scaling Factor: 100 - Clients: 50 - Mode: Read Write - Average Latency pts/pgbench-1.13.0 -s 100 -c 500 -S Scaling Factor: 100 - Clients: 500 - Mode: Read Only pts/pgbench-1.13.0 -s 100 -c 500 -S Scaling Factor: 100 - Clients: 500 - Mode: Read Only - Average Latency pts/pgbench-1.13.0 -s 1 -c 250 Scaling Factor: 1 - Clients: 250 - Mode: Read Write - Average Latency pts/pgbench-1.13.0 -s 1 -c 100 -S Scaling Factor: 1 - Clients: 100 - Mode: Read Only pts/pgbench-1.13.0 -s 1 -c 250 Scaling Factor: 1 - Clients: 250 - Mode: Read Write pts/pgbench-1.13.0 -s 1 -c 100 -S Scaling Factor: 1 - Clients: 100 - Mode: Read Only - Average Latency pts/pgbench-1.13.0 -s 1 -c 50 Scaling Factor: 1 - Clients: 50 - Mode: Read Write - Average Latency pts/pgbench-1.13.0 -s 1 -c 50 Scaling Factor: 1 - Clients: 50 - Mode: Read Write pts/keydb-1.4.0 -t lpop -c 100 Test: LPOP - Parallel Connections: 100 pts/spark-1.0.1 -r 1000000 -p 2000 Row Count: 1000000 - Partitions: 2000 - Broadcast Inner Join Test Time pts/spark-1.0.1 -r 10000000 -p 500 Row Count: 10000000 - Partitions: 500 - Inner Join Test Time pts/spark-1.0.1 -r 1000000 -p 100 Row Count: 1000000 - Partitions: 100 - Broadcast Inner Join Test Time pts/pgbench-1.13.0 -s 100 -c 250 -S Scaling Factor: 100 - Clients: 250 - Mode: Read Only - Average Latency pts/pgbench-1.13.0 -s 100 -c 250 -S Scaling Factor: 100 - Clients: 250 - Mode: Read Only pts/pgbench-1.13.0 -s 100 -c 800 Scaling Factor: 100 - Clients: 800 - Mode: Read Write pts/pgbench-1.13.0 -s 100 -c 800 Scaling Factor: 100 - Clients: 800 - Mode: Read Write - Average Latency pts/pgbench-1.13.0 -s 1 -c 1000 Scaling Factor: 1 - Clients: 1000 - Mode: Read Write pts/pgbench-1.13.0 -s 1 -c 1000 Scaling Factor: 1 - Clients: 1000 - Mode: Read Write - Average Latency pts/pgbench-1.13.0 -s 100 -c 1 -S Scaling Factor: 100 - Clients: 1 - Mode: Read Only - Average Latency pts/pgbench-1.13.0 -s 100 -c 1 Scaling Factor: 100 - Clients: 1 - Mode: Read Write - Average Latency pts/pgbench-1.13.0 -s 1 -c 500 Scaling Factor: 1 - Clients: 500 - Mode: Read Write pts/pgbench-1.13.0 -s 100 -c 1 Scaling Factor: 100 - Clients: 1 - Mode: Read Write pts/pgbench-1.13.0 -s 100 -c 1 -S Scaling Factor: 100 - Clients: 1 - Mode: Read Only pts/pgbench-1.13.0 -s 1 -c 1 Scaling Factor: 1 - Clients: 1 - Mode: Read Write - Average Latency pts/pgbench-1.13.0 -s 1 -c 1 Scaling Factor: 1 - Clients: 1 - Mode: Read Write pts/pgbench-1.13.0 -s 1 -c 500 Scaling Factor: 1 - Clients: 500 - Mode: Read Write - Average Latency pts/spark-1.0.1 -r 10000000 -p 500 Row Count: 10000000 - Partitions: 500 - Repartition Test Time pts/spark-1.0.1 -r 1000000 -p 1000 Row Count: 1000000 - Partitions: 1000 - SHA-512 Benchmark Time pts/pgbench-1.13.0 -s 1 -c 800 -S Scaling Factor: 1 - Clients: 800 - Mode: Read Only - Average Latency pts/pgbench-1.13.0 -s 1 -c 800 -S Scaling Factor: 1 - Clients: 800 - Mode: Read Only pts/keydb-1.4.0 -t set -c 100 Test: SET - Parallel Connections: 100 pts/clickhouse-1.2.0 100M Rows Hits Dataset, Second Run pts/clickhouse-1.2.0 100M Rows Hits Dataset, First Run / Cold Cache pts/pgbench-1.13.0 -s 1 -c 800 Scaling Factor: 1 - Clients: 800 - Mode: Read Write pts/pgbench-1.13.0 -s 1 -c 800 Scaling Factor: 1 - Clients: 800 - Mode: Read Write - Average Latency pts/spark-1.0.1 -r 1000000 -p 500 Row Count: 1000000 - Partitions: 500 - Group By Test Time pts/spark-1.0.1 -r 1000000 -p 2000 Row Count: 1000000 - Partitions: 2000 - Group By Test Time pts/keydb-1.4.0 -t lpush -c 100 Test: LPUSH - Parallel Connections: 100 pts/pgbench-1.13.0 -s 100 -c 100 -S Scaling Factor: 100 - Clients: 100 - Mode: Read Only - Average Latency pts/pgbench-1.13.0 -s 100 -c 100 -S Scaling Factor: 100 - Clients: 100 - Mode: Read Only pts/pgbench-1.13.0 -s 1 -c 100 Scaling Factor: 1 - Clients: 100 - Mode: Read Write - Average Latency pts/pgbench-1.13.0 -s 1 -c 100 Scaling Factor: 1 - Clients: 100 - Mode: Read Write pts/spark-1.0.1 -r 1000000 -p 500 Row Count: 1000000 - Partitions: 500 - Inner Join Test Time pts/spark-1.0.1 -r 1000000 -p 500 Row Count: 1000000 - Partitions: 500 - Broadcast Inner Join Test Time pts/spark-1.0.1 -r 1000000 -p 2000 Row Count: 1000000 - Partitions: 2000 - Inner Join Test Time pts/spark-1.0.1 -r 1000000 -p 500 Row Count: 1000000 - Partitions: 500 - SHA-512 Benchmark Time pts/spark-1.0.1 -r 10000000 -p 2000 Row Count: 10000000 - Partitions: 2000 - Group By Test Time pts/keydb-1.4.0 -t get -c 100 Test: GET - Parallel Connections: 100 pts/pgbench-1.13.0 -s 100 -c 50 -S Scaling Factor: 100 - Clients: 50 - Mode: Read Only pts/deepsparse-1.3.2 zoo:nlp/token_classification/bert-base/pytorch/huggingface/conll2003/base-none --scenario sync Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream pts/spark-1.0.1 -r 10000000 -p 1000 Row Count: 10000000 - Partitions: 1000 - Group By Test Time pts/spark-1.0.1 -r 10000000 -p 2000 Row Count: 10000000 - Partitions: 2000 - Inner Join Test Time pts/pgbench-1.13.0 -s 100 -c 50 -S Scaling Factor: 100 - Clients: 50 - Mode: Read Only - Average Latency pts/keydb-1.4.0 -t get -c 50 Test: GET - Parallel Connections: 50 pts/deepsparse-1.3.2 zoo:nlp/text_classification/bert-base/pytorch/huggingface/sst2/base-none --scenario sync Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream pts/spark-1.0.1 -r 10000000 -p 2000 Row Count: 10000000 - Partitions: 2000 - Broadcast Inner Join Test Time pts/spark-1.0.1 -r 10000000 -p 500 Row Count: 10000000 - Partitions: 500 - Group By Test Time pts/spark-1.0.1 -r 1000000 -p 100 Row Count: 1000000 - Partitions: 100 - Group By Test Time pts/spark-1.0.1 -r 10000000 -p 500 Row Count: 10000000 - Partitions: 500 - SHA-512 Benchmark Time pts/spark-1.0.1 -r 10000000 -p 1000 Row Count: 10000000 - Partitions: 1000 - Broadcast Inner Join Test Time pts/spark-1.0.1 -r 10000000 -p 1000 Row Count: 10000000 - Partitions: 1000 - Repartition Test Time pts/spark-1.0.1 -r 10000000 -p 2000 Row Count: 10000000 - Partitions: 2000 - SHA-512 Benchmark Time pts/deepsparse-1.3.2 zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/base-none --scenario async Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.3.2 zoo:nlp/text_classification/distilbert-none/pytorch/huggingface/mnli/base-none --scenario sync Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream pts/rocksdb-1.4.0 --benchmarks="readwhilewriting" Test: Read While Writing pts/deepsparse-1.3.2 zoo:nlp/question_answering/bert-base/pytorch/huggingface/squad/12layer_pruned90-none --scenario sync Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream pts/spark-1.0.1 -r 1000000 -p 1000 Row Count: 1000000 - Partitions: 1000 - Inner Join Test Time pts/spark-1.0.1 -r 10000000 -p 100 Row Count: 10000000 - Partitions: 100 - Inner Join Test Time pts/spark-1.0.1 -r 10000000 -p 100 Row Count: 10000000 - Partitions: 100 - Group By Test Time pts/pgbench-1.13.0 -s 1 -c 50 -S Scaling Factor: 1 - Clients: 50 - Mode: Read Only - Average Latency pts/pgbench-1.13.0 -s 1 -c 1000 -S Scaling Factor: 1 - Clients: 1000 - Mode: Read Only pts/pgbench-1.13.0 -s 1 -c 1000 -S Scaling Factor: 1 - Clients: 1000 - Mode: Read Only - Average Latency pts/spark-1.0.1 -r 1000000 -p 1000 Row Count: 1000000 - Partitions: 1000 - Broadcast Inner Join Test Time pts/spark-1.0.1 -r 10000000 -p 2000 Row Count: 10000000 - Partitions: 2000 - Repartition Test Time pts/spark-1.0.1 -r 1000000 -p 500 Row Count: 1000000 - Partitions: 500 - Repartition Test Time pts/clickhouse-1.2.0 100M Rows Hits Dataset, Third Run pts/pgbench-1.13.0 -s 1 -c 50 -S Scaling Factor: 1 - Clients: 50 - Mode: Read Only pts/spark-1.0.1 -r 10000000 -p 1000 Row Count: 10000000 - Partitions: 1000 - Inner Join Test Time pts/spark-1.0.1 -r 1000000 -p 100 Row Count: 1000000 - Partitions: 100 - SHA-512 Benchmark Time pts/spark-1.0.1 -r 1000000 -p 2000 Row Count: 1000000 - Partitions: 2000 - Repartition Test Time pts/deepsparse-1.3.2 zoo:nlp/text_classification/bert-base/pytorch/huggingface/sst2/base-none --scenario async Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream pts/spark-1.0.1 -r 1000000 -p 2000 Row Count: 1000000 - Partitions: 2000 - SHA-512 Benchmark Time pts/spark-1.0.1 -r 10000000 -p 500 Row Count: 10000000 - Partitions: 500 - Broadcast Inner Join Test Time pts/rocksdb-1.4.0 --benchmarks="fillrandom" Test: Random Fill pts/spark-1.0.1 -r 1000000 -p 1000 Row Count: 1000000 - Partitions: 1000 - Calculate Pi Benchmark pts/spark-1.0.1 -r 1000000 -p 1000 Row Count: 1000000 - Partitions: 1000 - Group By Test Time pts/spark-1.0.1 -r 1000000 -p 2000 Row Count: 1000000 - Partitions: 2000 - Calculate Pi Benchmark pts/spark-1.0.1 -r 1000000 -p 500 Row Count: 1000000 - Partitions: 500 - Calculate Pi Benchmark pts/spark-1.0.1 -r 10000000 -p 100 Row Count: 10000000 - Partitions: 100 - Calculate Pi Benchmark pts/deepsparse-1.3.2 zoo:nlp/sentiment_analysis/bert-base/pytorch/huggingface/sst2/12layer_pruned90-none --scenario sync Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-Stream pts/deepsparse-1.3.2 zoo:nlp/text_classification/distilbert-none/pytorch/huggingface/mnli/base-none --scenario async Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream pts/spark-1.0.1 -r 10000000 -p 100 Row Count: 10000000 - Partitions: 100 - Broadcast Inner Join Test Time pts/deepsparse-1.3.2 zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/base-none --scenario sync Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream pts/spark-1.0.1 -r 10000000 -p 500 Row Count: 10000000 - Partitions: 500 - Calculate Pi Benchmark pts/keydb-1.4.0 -t sadd -c 50 Test: SADD - Parallel Connections: 50 pts/spark-1.0.1 -r 10000000 -p 100 Row Count: 10000000 - Partitions: 100 - Calculate Pi Benchmark Using Dataframe pts/spark-1.0.1 -r 1000000 -p 100 Row Count: 1000000 - Partitions: 100 - Inner Join Test Time pts/spark-1.0.1 -r 1000000 -p 100 Row Count: 1000000 - Partitions: 100 - Calculate Pi Benchmark pts/deepsparse-1.3.2 zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/pruned90-none --scenario sync Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream pts/spark-1.0.1 -r 10000000 -p 1000 Row Count: 10000000 - Partitions: 1000 - Calculate Pi Benchmark Using Dataframe pts/rocksdb-1.4.0 --benchmarks="fillseq" Test: Sequential Fill pts/rocksdb-1.4.0 --benchmarks="fillsync" Test: Random Fill Sync pts/spark-1.0.1 -r 10000000 -p 2000 Row Count: 10000000 - Partitions: 2000 - Calculate Pi Benchmark pts/spark-1.0.1 -r 1000000 -p 1000 Row Count: 1000000 - Partitions: 1000 - Repartition Test Time pts/deepsparse-1.3.2 zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/base-none --scenario async Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream pts/keydb-1.4.0 -t hmset -c 50 Test: HMSET - Parallel Connections: 50 pts/keydb-1.4.0 -t hmset -c 100 Test: HMSET - Parallel Connections: 100 pts/deepsparse-1.3.2 zoo:nlp/document_classification/obert-base/pytorch/huggingface/imdb/base-none --scenario async Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream pts/rocksdb-1.4.0 --benchmarks="readrandom" Test: Random Read pts/spark-1.0.1 -r 1000000 -p 2000 Row Count: 1000000 - Partitions: 2000 - Calculate Pi Benchmark Using Dataframe pts/spark-1.0.1 -r 10000000 -p 100 Row Count: 10000000 - Partitions: 100 - Repartition Test Time pts/deepsparse-1.3.2 zoo:nlp/question_answering/bert-base/pytorch/huggingface/squad/12layer_pruned90-none --scenario async Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream pts/memcached-1.1.0 --ratio=1:100 Set To Get Ratio: 1:100 pts/spark-1.0.1 -r 10000000 -p 1000 Row Count: 10000000 - Partitions: 1000 - SHA-512 Benchmark Time pts/rocksdb-1.4.0 --benchmarks="readrandomwriterandom" Test: Read Random Write Random pts/deepsparse-1.3.2 zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/base-none --scenario sync Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream pts/rocksdb-1.4.0 --benchmarks="updaterandom" Test: Update Random pts/keydb-1.4.0 -t sadd -c 100 Test: SADD - Parallel Connections: 100 pts/deepsparse-1.3.2 zoo:nlp/token_classification/bert-base/pytorch/huggingface/conll2003/base-none --scenario async Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream pts/spark-1.0.1 -r 1000000 -p 100 Row Count: 1000000 - Partitions: 100 - Calculate Pi Benchmark Using Dataframe pts/deepsparse-1.3.2 zoo:nlp/document_classification/obert-base/pytorch/huggingface/imdb/base-none --scenario sync Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream pts/spark-1.0.1 -r 10000000 -p 100 Row Count: 10000000 - Partitions: 100 - SHA-512 Benchmark Time pts/spark-1.0.1 -r 10000000 -p 1000 Row Count: 10000000 - Partitions: 1000 - Calculate Pi Benchmark pts/openems-1.0.0 pyems/examples/coupler.py Test: pyEMS Coupler pts/openems-1.0.0 openEMS-Project/openEMS/python/Tutorials/MSL_NotchFilter.py Test: openEMS MSL_NotchFilter pts/memcached-1.1.0 --ratio=1:10 Set To Get Ratio: 1:10 pts/spark-1.0.1 -r 10000000 -p 2000 Row Count: 10000000 - Partitions: 2000 - Calculate Pi Benchmark Using Dataframe pts/spark-1.0.1 -r 10000000 -p 500 Row Count: 10000000 - Partitions: 500 - Calculate Pi Benchmark Using Dataframe pts/deepsparse-1.3.2 zoo:nlp/sentiment_analysis/bert-base/pytorch/huggingface/sst2/12layer_pruned90-none --scenario async Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream pts/memcached-1.1.0 --ratio=1:5 Set To Get Ratio: 1:5 pts/spark-1.0.1 -r 1000000 -p 500 Row Count: 1000000 - Partitions: 500 - Calculate Pi Benchmark Using Dataframe pts/keydb-1.4.0 -t lpush -c 50 Test: LPUSH - Parallel Connections: 50 pts/keydb-1.4.0 -t set -c 50 Test: SET - Parallel Connections: 50 pts/spark-1.0.1 -r 1000000 -p 1000 Row Count: 1000000 - Partitions: 1000 - Calculate Pi Benchmark Using Dataframe pts/pgbench-1.13.0 -s 100 -c 5000 Scaling Factor: 100 - Clients: 5000 - Mode: Read Write pts/pgbench-1.13.0 -s 100 -c 5000 -S Scaling Factor: 100 - Clients: 5000 - Mode: Read Only pts/pgbench-1.13.0 -s 1 -c 5000 Scaling Factor: 1 - Clients: 5000 - Mode: Read Write pts/pgbench-1.13.0 -s 1 -c 5000 -S Scaling Factor: 1 - Clients: 5000 - Mode: Read Only