adl feb

Intel Core i7-1280P testing with a MSI MS-14C6 (E14C6IMS.115 BIOS) and MSI Intel ADL GT2 15GB on Ubuntu 22.10 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 2302026-NE-ADLFEB23315
Jump To Table - Results

View

Do Not Show Noisy Results
Do Not Show Results With Incomplete Data
Do Not Show Results With Little Change/Spread
List Notable Results

Limit displaying results to tests within:

Database Test Suite 2 Tests
Python Tests 2 Tests
Server 2 Tests

Statistics

Show Overall Harmonic Mean(s)
Show Overall Geometric Mean
Show Geometric Means Per-Suite/Category
Show Wins / Losses Counts (Pie Chart)
Normalize Results
Remove Outliers Before Calculating Averages

Graph Settings

Force Line Graphs Where Applicable
Convert To Scalar Where Applicable
Prefer Vertical Bar Graphs

Multi-Way Comparison

Condense Multi-Option Tests Into Single Result Graphs

Table

Show Detailed System Result Table

Run Management

Highlight
Result
Hide
Result
Result
Identifier
View Logs
Performance Per
Dollar
Date
Run
  Test
  Duration
a
February 01 2023
  4 Hours, 44 Minutes
n
February 02 2023
  4 Hours, 42 Minutes
c
February 02 2023
  4 Hours, 41 Minutes
Invert Hiding All Results Option
  4 Hours, 42 Minutes

Only show results where is faster than
Only show results matching title/arguments (delimit multiple options with a comma):
Do not show results matching title/arguments (delimit multiple options with a comma):

adl feb Suite 1.0.0 System Test suite extracted from adl feb. 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/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/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/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/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/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/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:cv/detection/yolov5-s/pytorch/ultralytics/coco/base-none --scenario sync Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream 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/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/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/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/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/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/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/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/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/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/memcached-1.1.0 --ratio=1:1 Set To Get Ratio: 1:1 pts/memcached-1.1.0 --ratio=1:5 Set To Get Ratio: 1:5 pts/memcached-1.1.0 --ratio=1:10 Set To Get Ratio: 1:10 pts/memcached-1.1.0 --ratio=1:100 Set To Get Ratio: 1:100 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 100 Row Count: 1000000 - Partitions: 100 - Calculate Pi Benchmark pts/spark-1.0.1 -r 1000000 -p 100 Row Count: 1000000 - Partitions: 100 - Calculate Pi Benchmark Using Dataframe pts/spark-1.0.1 -r 1000000 -p 100 Row Count: 1000000 - Partitions: 100 - Group By Test Time pts/spark-1.0.1 -r 1000000 -p 100 Row Count: 1000000 - Partitions: 100 - Repartition Test Time 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 - Broadcast 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 1000000 -p 500 Row Count: 1000000 - Partitions: 500 - Calculate Pi Benchmark pts/spark-1.0.1 -r 1000000 -p 500 Row Count: 1000000 - Partitions: 500 - Calculate Pi Benchmark Using Dataframe 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 500 Row Count: 1000000 - Partitions: 500 - Repartition Test Time 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 1000 Row Count: 1000000 - Partitions: 1000 - SHA-512 Benchmark Time 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 - Calculate Pi Benchmark Using Dataframe 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 1000 Row Count: 1000000 - Partitions: 1000 - Repartition Test Time pts/spark-1.0.1 -r 1000000 -p 1000 Row Count: 1000000 - Partitions: 1000 - Inner Join Test Time 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 1000000 -p 2000 Row Count: 1000000 - Partitions: 2000 - SHA-512 Benchmark 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 2000 Row Count: 1000000 - Partitions: 2000 - Calculate Pi Benchmark Using Dataframe pts/spark-1.0.1 -r 1000000 -p 2000 Row Count: 1000000 - Partitions: 2000 - Group By Test Time pts/spark-1.0.1 -r 1000000 -p 2000 Row Count: 1000000 - Partitions: 2000 - Repartition 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 2000 Row Count: 1000000 - Partitions: 2000 - Broadcast Inner Join Test Time 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 100 Row Count: 10000000 - Partitions: 100 - Calculate Pi Benchmark 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 10000000 -p 100 Row Count: 10000000 - Partitions: 100 - Group By Test Time pts/spark-1.0.1 -r 10000000 -p 100 Row Count: 10000000 - Partitions: 100 - Repartition 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 - Broadcast Inner Join 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 500 Row Count: 10000000 - Partitions: 500 - Calculate Pi Benchmark pts/spark-1.0.1 -r 10000000 -p 500 Row Count: 10000000 - Partitions: 500 - Calculate Pi Benchmark Using Dataframe pts/spark-1.0.1 -r 10000000 -p 500 Row Count: 10000000 - Partitions: 500 - Group By Test Time pts/spark-1.0.1 -r 10000000 -p 500 Row Count: 10000000 - Partitions: 500 - Repartition 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 10000000 -p 500 Row Count: 10000000 - Partitions: 500 - Broadcast Inner Join Test Time pts/spark-1.0.1 -r 10000000 -p 1000 Row Count: 10000000 - Partitions: 1000 - SHA-512 Benchmark Time pts/spark-1.0.1 -r 10000000 -p 1000 Row Count: 10000000 - Partitions: 1000 - Calculate Pi Benchmark pts/spark-1.0.1 -r 10000000 -p 1000 Row Count: 10000000 - Partitions: 1000 - Calculate Pi Benchmark Using Dataframe 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 1000 Row Count: 10000000 - Partitions: 1000 - Repartition Test Time pts/spark-1.0.1 -r 10000000 -p 1000 Row Count: 10000000 - Partitions: 1000 - Inner Join Test 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 2000 Row Count: 10000000 - Partitions: 2000 - SHA-512 Benchmark Time pts/spark-1.0.1 -r 10000000 -p 2000 Row Count: 10000000 - Partitions: 2000 - Calculate Pi Benchmark 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 2000 Row Count: 10000000 - Partitions: 2000 - Group By 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 10000000 -p 2000 Row Count: 10000000 - Partitions: 2000 - Inner Join Test Time pts/spark-1.0.1 -r 10000000 -p 2000 Row Count: 10000000 - Partitions: 2000 - Broadcast Inner Join Test Time