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.
HTML result view exported from: https://openbenchmarking.org/result/2302021-NE-DWQ81086357&rdt&grt.
Apache Spark
Row Count: 1000000 - Partitions: 100 - SHA-512 Benchmark Time
Apache Spark
Row Count: 1000000 - Partitions: 100 - Calculate Pi Benchmark
Apache Spark
Row Count: 1000000 - Partitions: 100 - Calculate Pi Benchmark Using Dataframe
Apache Spark
Row Count: 1000000 - Partitions: 100 - Group By Test Time
Apache Spark
Row Count: 1000000 - Partitions: 100 - Repartition Test Time
Apache Spark
Row Count: 1000000 - Partitions: 100 - Inner Join Test Time
Apache Spark
Row Count: 1000000 - Partitions: 100 - Broadcast Inner Join Test Time
Apache Spark
Row Count: 1000000 - Partitions: 500 - SHA-512 Benchmark Time
Apache Spark
Row Count: 1000000 - Partitions: 500 - Calculate Pi Benchmark
Apache Spark
Row Count: 1000000 - Partitions: 500 - Calculate Pi Benchmark Using Dataframe
Apache Spark
Row Count: 1000000 - Partitions: 500 - Group By Test Time
Apache Spark
Row Count: 1000000 - Partitions: 500 - Repartition Test Time
Apache Spark
Row Count: 1000000 - Partitions: 500 - Inner Join Test Time
Apache Spark
Row Count: 1000000 - Partitions: 500 - Broadcast Inner Join Test Time
Apache Spark
Row Count: 1000000 - Partitions: 1000 - SHA-512 Benchmark Time
Apache Spark
Row Count: 1000000 - Partitions: 1000 - Calculate Pi Benchmark
Apache Spark
Row Count: 1000000 - Partitions: 1000 - Calculate Pi Benchmark Using Dataframe
Apache Spark
Row Count: 1000000 - Partitions: 1000 - Group By Test Time
Apache Spark
Row Count: 1000000 - Partitions: 1000 - Repartition Test Time
Apache Spark
Row Count: 1000000 - Partitions: 1000 - Inner Join Test Time
Apache Spark
Row Count: 1000000 - Partitions: 1000 - Broadcast Inner Join Test Time
Apache Spark
Row Count: 1000000 - Partitions: 2000 - SHA-512 Benchmark Time
Apache Spark
Row Count: 1000000 - Partitions: 2000 - Calculate Pi Benchmark
Apache Spark
Row Count: 1000000 - Partitions: 2000 - Calculate Pi Benchmark Using Dataframe
Apache Spark
Row Count: 1000000 - Partitions: 2000 - Group By Test Time
Apache Spark
Row Count: 1000000 - Partitions: 2000 - Repartition Test Time
Apache Spark
Row Count: 1000000 - Partitions: 2000 - Inner Join Test Time
Apache Spark
Row Count: 1000000 - Partitions: 2000 - Broadcast Inner Join Test Time
Apache Spark
Row Count: 10000000 - Partitions: 100 - SHA-512 Benchmark Time
Apache Spark
Row Count: 10000000 - Partitions: 100 - Calculate Pi Benchmark
Apache Spark
Row Count: 10000000 - Partitions: 100 - Calculate Pi Benchmark Using Dataframe
Apache Spark
Row Count: 10000000 - Partitions: 100 - Group By Test Time
Apache Spark
Row Count: 10000000 - Partitions: 100 - Repartition Test Time
Apache Spark
Row Count: 10000000 - Partitions: 100 - Inner Join Test Time
Apache Spark
Row Count: 10000000 - Partitions: 100 - Broadcast Inner Join Test Time
Apache Spark
Row Count: 10000000 - Partitions: 500 - SHA-512 Benchmark Time
Apache Spark
Row Count: 10000000 - Partitions: 500 - Calculate Pi Benchmark
Apache Spark
Row Count: 10000000 - Partitions: 500 - Calculate Pi Benchmark Using Dataframe
Apache Spark
Row Count: 10000000 - Partitions: 500 - Group By Test Time
Apache Spark
Row Count: 10000000 - Partitions: 500 - Repartition Test Time
Apache Spark
Row Count: 10000000 - Partitions: 500 - Inner Join Test Time
Apache Spark
Row Count: 10000000 - Partitions: 500 - Broadcast Inner Join Test Time
Apache Spark
Row Count: 10000000 - Partitions: 1000 - SHA-512 Benchmark Time
Apache Spark
Row Count: 10000000 - Partitions: 1000 - Calculate Pi Benchmark
Apache Spark
Row Count: 10000000 - Partitions: 1000 - Calculate Pi Benchmark Using Dataframe
Apache Spark
Row Count: 10000000 - Partitions: 1000 - Group By Test Time
Apache Spark
Row Count: 10000000 - Partitions: 1000 - Repartition Test Time
Apache Spark
Row Count: 10000000 - Partitions: 1000 - Inner Join Test Time
Apache Spark
Row Count: 10000000 - Partitions: 1000 - Broadcast Inner Join Test Time
Apache Spark
Row Count: 10000000 - Partitions: 2000 - SHA-512 Benchmark Time
Apache Spark
Row Count: 10000000 - Partitions: 2000 - Calculate Pi Benchmark
Apache Spark
Row Count: 10000000 - Partitions: 2000 - Calculate Pi Benchmark Using Dataframe
Apache Spark
Row Count: 10000000 - Partitions: 2000 - Group By Test Time
Apache Spark
Row Count: 10000000 - Partitions: 2000 - Repartition Test Time
Apache Spark
Row Count: 10000000 - Partitions: 2000 - Inner Join Test Time
Apache Spark
Row Count: 10000000 - Partitions: 2000 - Broadcast Inner Join Test Time
ClickHouse
100M Rows Hits Dataset, First Run / Cold Cache
ClickHouse
100M Rows Hits Dataset, Second Run
ClickHouse
100M Rows Hits Dataset, Third Run
KeyDB
Test: GET - Parallel Connections: 50
KeyDB
Test: SET - Parallel Connections: 50
KeyDB
Test: GET - Parallel Connections: 100
KeyDB
Test: LPOP - Parallel Connections: 50
KeyDB
Test: SADD - Parallel Connections: 50
KeyDB
Test: SET - Parallel Connections: 100
KeyDB
Test: HMSET - Parallel Connections: 50
KeyDB
Test: LPOP - Parallel Connections: 100
KeyDB
Test: LPUSH - Parallel Connections: 50
KeyDB
Test: SADD - Parallel Connections: 100
KeyDB
Test: HMSET - Parallel Connections: 100
KeyDB
Test: LPUSH - Parallel Connections: 100
Memcached
Set To Get Ratio: 1:5
Memcached
Set To Get Ratio: 1:10
Memcached
Set To Get Ratio: 1:100
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 Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - 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: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream
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: Synchronous Single-Stream
Neural Magic DeepSparse
Model: CV Segmentation, 90% Pruned YOLACT Pruned - 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
OpenEMS
Test: pyEMS Coupler
OpenEMS
Test: openEMS MSL_NotchFilter
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: 250 - Mode: Read Only
PostgreSQL
Scaling Factor: 1 - Clients: 250 - 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: 1 - Clients: 500 - Mode: Read Only
PostgreSQL
Scaling Factor: 1 - Clients: 500 - Mode: Read Only - Average Latency
PostgreSQL
Scaling Factor: 1 - Clients: 800 - Mode: Read Only
PostgreSQL
Scaling Factor: 1 - Clients: 800 - Mode: Read Only - 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: 1 - Clients: 1000 - Mode: Read Only
PostgreSQL
Scaling Factor: 1 - Clients: 1000 - Mode: Read Only - Average Latency
PostgreSQL
Scaling Factor: 1 - Clients: 250 - Mode: Read Write
PostgreSQL
Scaling Factor: 1 - Clients: 250 - Mode: Read Write - Average Latency
PostgreSQL
Scaling Factor: 1 - Clients: 500 - Mode: Read Write
PostgreSQL
Scaling Factor: 1 - Clients: 500 - Mode: Read Write - Average Latency
PostgreSQL
Scaling Factor: 1 - Clients: 800 - Mode: Read Write
PostgreSQL
Scaling Factor: 1 - Clients: 800 - 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: 1 - Clients: 1000 - Mode: Read Write
PostgreSQL
Scaling Factor: 1 - Clients: 1000 - Mode: Read Write - 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: 250 - Mode: Read Only
PostgreSQL
Scaling Factor: 100 - Clients: 250 - 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: 500 - Mode: Read Only
PostgreSQL
Scaling Factor: 100 - Clients: 500 - Mode: Read Only - Average Latency
PostgreSQL
Scaling Factor: 100 - Clients: 800 - Mode: Read Only
PostgreSQL
Scaling Factor: 100 - Clients: 800 - Mode: Read Only - Average Latency
PostgreSQL
Scaling Factor: 100 - Clients: 100 - Mode: Read Write
PostgreSQL
Scaling Factor: 100 - Clients: 100 - Mode: Read Write - Average Latency
PostgreSQL
Scaling Factor: 100 - Clients: 1000 - Mode: Read Only
PostgreSQL
Scaling Factor: 100 - Clients: 1000 - Mode: Read Only - Average Latency
PostgreSQL
Scaling Factor: 100 - Clients: 250 - Mode: Read Write
PostgreSQL
Scaling Factor: 100 - Clients: 250 - Mode: Read Write - Average Latency
PostgreSQL
Scaling Factor: 100 - Clients: 500 - Mode: Read Write
PostgreSQL
Scaling Factor: 100 - Clients: 500 - Mode: Read Write - Average Latency
PostgreSQL
Scaling Factor: 100 - Clients: 800 - Mode: Read Write
PostgreSQL
Scaling Factor: 100 - Clients: 800 - Mode: Read Write - Average Latency
PostgreSQL
Scaling Factor: 100 - Clients: 1000 - Mode: Read Write
PostgreSQL
Scaling Factor: 100 - Clients: 1000 - Mode: Read Write - Average Latency
RocksDB
Test: Random Fill
RocksDB
Test: Random Read
RocksDB
Test: Update Random
RocksDB
Test: Sequential Fill
RocksDB
Test: Random Fill Sync
RocksDB
Test: Read While Writing
RocksDB
Test: Read Random Write Random
Phoronix Test Suite v10.8.5