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
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Database Test Suite 2 Tests
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a
February 01 2023
  4 Hours, 44 Minutes
n
February 02 2023
  4 Hours, 42 Minutes
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February 02 2023
  4 Hours, 41 Minutes
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  4 Hours, 42 Minutes

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adl feb, "Apache Spark 3.3 - Row Count: 10000000 - Partitions: 500 - Broadcast Inner Join Test Time", Lower Results Are Better "a", "n",13.551010522002 "c",14.385142443003 "Apache Spark 3.3 - Row Count: 10000000 - Partitions: 500 - Inner Join Test Time", Lower Results Are Better "a",14.409949321999 "n",14.604731129002 "c",15.331645033999 "Apache Spark 3.3 - Row Count: 10000000 - Partitions: 500 - Repartition Test Time", Lower Results Are Better "a", "n",12.100752055005 "c", "Apache Spark 3.3 - Row Count: 10000000 - Partitions: 500 - Group By Test Time", Lower Results Are Better "a",8.9252873159994 "n",9.0150375180019 "c",8.8807286699957 "Apache Spark 3.3 - Row Count: 10000000 - Partitions: 500 - Calculate Pi Benchmark Using Dataframe", Lower Results Are Better "a",11.867045601999 "n",12.030399600997 "c",11.883185976003 "Apache Spark 3.3 - Row Count: 10000000 - Partitions: 500 - Calculate Pi Benchmark", Lower Results Are Better "a", "n", "c", "Apache Spark 3.3 - Row Count: 10000000 - Partitions: 500 - SHA-512 Benchmark Time", Lower Results Are Better "a", "n",16.509469525001 "c",16.579500601001 "Apache Spark 3.3 - Row Count: 10000000 - Partitions: 2000 - Broadcast Inner Join Test Time", Lower Results Are Better "a",12.843604368001 "n",13.161357564997 "c",13.291148780998 "Apache Spark 3.3 - Row Count: 10000000 - Partitions: 2000 - Inner Join Test Time", Lower Results Are Better "a",14.322027141001 "n",14.478127132003 "c",14.152904080998 "Apache Spark 3.3 - Row Count: 10000000 - Partitions: 2000 - Repartition Test Time", Lower Results Are Better "a", "n",12.258866650998 "c",12.205215734997 "Apache Spark 3.3 - Row Count: 10000000 - Partitions: 2000 - Group By Test Time", Lower Results Are Better "a",9.2121373800001 "n",9.1894405670027 "c",9.3414837380042 "Apache Spark 3.3 - Row Count: 10000000 - Partitions: 2000 - Calculate Pi Benchmark Using Dataframe", Lower Results Are Better "a",11.847940592001 "n",11.738109399994 "c",11.837314666001 "Apache Spark 3.3 - Row Count: 10000000 - Partitions: 2000 - Calculate Pi Benchmark", Lower Results Are Better "a", "n", "c", "Apache Spark 3.3 - Row Count: 10000000 - Partitions: 2000 - SHA-512 Benchmark Time", Lower Results Are Better "a", "n",16.691064508006 "c",16.866412666001 "Apache Spark 3.3 - Row Count: 10000000 - Partitions: 1000 - Broadcast Inner Join Test Time", Lower Results Are Better "a",13.104396057999 "n",12.984339777002 "c",13.537118790002 "Apache Spark 3.3 - Row Count: 10000000 - Partitions: 1000 - Inner Join Test Time", Lower Results Are Better "a", "n",14.464968423003 "c",13.765479517999 "Apache Spark 3.3 - Row Count: 10000000 - Partitions: 1000 - Repartition Test Time", Lower Results Are Better "a",12.176857185999 "n",11.113394199005 "c",12.071250566994 "Apache Spark 3.3 - Row Count: 10000000 - Partitions: 1000 - Group By Test Time", Lower Results Are Better "a",9.0486194279983 "n",10.429238895995 "c",8.3333057679993 "Apache Spark 3.3 - Row Count: 10000000 - Partitions: 1000 - Calculate Pi Benchmark Using Dataframe", Lower Results Are Better "a",11.899722702999 "n", "c",11.786392870003 "Apache Spark 3.3 - Row Count: 10000000 - Partitions: 1000 - Calculate Pi Benchmark", Lower Results Are Better "a", "n", "c",207.56025432501 "Apache Spark 3.3 - Row Count: 10000000 - Partitions: 1000 - SHA-512 Benchmark Time", Lower Results Are Better "a", "n", "c",15.950117680004 "Apache Spark 3.3 - Row Count: 10000000 - Partitions: 100 - Broadcast Inner Join Test Time", Lower Results Are Better "a", "n",13.184062012995 "c",12.586943483002 "Apache Spark 3.3 - Row Count: 10000000 - Partitions: 100 - Inner Join Test Time", Lower Results Are Better "a", "n",13.714269755001 "c",13.411836669999 "Apache Spark 3.3 - Row Count: 10000000 - Partitions: 100 - Repartition Test Time", Lower Results Are Better "a",13.070503669001 "n",11.794979455997 "c",11.627421371995 "Apache Spark 3.3 - Row Count: 10000000 - Partitions: 100 - Group By Test Time", Lower Results Are Better "a",8.0076489519997 "n",8.3931533959985 "c",8.4812063969948 "Apache Spark 3.3 - Row Count: 10000000 - Partitions: 100 - Calculate Pi Benchmark Using Dataframe", Lower Results Are Better "a", "n",12.021378245998 "c",11.804887015998 "Apache Spark 3.3 - Row Count: 10000000 - Partitions: 100 - Calculate Pi Benchmark", Lower Results Are Better "a", "n", "c", "Apache Spark 3.3 - Row Count: 10000000 - Partitions: 100 - SHA-512 Benchmark Time", Lower Results Are Better "a", "n",15.408624711003 "c", "Apache Spark 3.3 - Row Count: 1000000 - Partitions: 2000 - Broadcast Inner Join Test Time", Lower Results Are Better "a",2.7440083110005 "n",2.8139859530056 "c",2.7092221460043 "Apache Spark 3.3 - Row Count: 1000000 - Partitions: 2000 - Inner Join Test Time", Lower Results Are Better "a", "n",3.5013527780029 "c",3.4879487009966 "Apache Spark 3.3 - Row Count: 1000000 - Partitions: 2000 - Repartition Test Time", Lower Results Are Better "a", "n",3.6014186259999 "c",3.6523158089985 "Apache Spark 3.3 - Row Count: 1000000 - Partitions: 2000 - Group By Test Time", Lower Results Are Better "a",5.1680092719998 "n",5.2924487089986 "c",5.0711030490056 "Apache Spark 3.3 - Row Count: 1000000 - Partitions: 2000 - Calculate Pi Benchmark Using Dataframe", Lower Results Are Better "a", "n",12.695467101999 "c",11.887816671995 "Apache Spark 3.3 - Row Count: 1000000 - Partitions: 2000 - Calculate Pi Benchmark", Lower Results Are Better "a", "n",213.34612123601 "c", "Apache Spark 3.3 - Row Count: 1000000 - Partitions: 2000 - SHA-512 Benchmark Time", Lower Results Are Better "a",4.7867656970002 "n",4.992652076995 "c",4.9551145879959 "Apache Spark 3.3 - Row Count: 1000000 - Partitions: 1000 - Broadcast Inner Join Test Time", Lower Results Are Better "a",2.3724098149996 "n",2.2446360269969 "c",2.2239032229991 "Apache Spark 3.3 - Row Count: 1000000 - Partitions: 1000 - Inner Join Test Time", Lower Results Are Better "a",2.8230571559998 "n",2.8734805319982 "c",2.8075345249963 "Apache Spark 3.3 - Row Count: 1000000 - Partitions: 1000 - Repartition Test Time", Lower Results Are Better "a",3.3213312449998 "n",3.3910418140003 "c",3.3634402020034 "Apache Spark 3.3 - Row Count: 1000000 - Partitions: 1000 - Group By Test Time", Lower Results Are Better "a",4.6077082049997 "n",4.7548452799965 "c",4.5793853489959 "Apache Spark 3.3 - Row Count: 1000000 - Partitions: 1000 - Calculate Pi Benchmark Using Dataframe", Lower Results Are Better "a",11.965187123001 "n",12.018549393004 "c",11.874456791993 "Apache Spark 3.3 - Row Count: 1000000 - Partitions: 1000 - Calculate Pi Benchmark", Lower Results Are Better "a", "n",208.49253782799 "c", "Apache Spark 3.3 - Row Count: 1000000 - Partitions: 1000 - SHA-512 Benchmark Time", Lower Results Are Better "a",4.5555065680001 "n",4.3897262149985 "c",4.3578911110017 "Apache Spark 3.3 - Row Count: 1000000 - Partitions: 500 - Broadcast Inner Join Test Time", Lower Results Are Better "a",1.9772059339994 "n",1.9281307010024 "c",1.9476652130033 "Apache Spark 3.3 - Row Count: 1000000 - Partitions: 500 - Inner Join Test Time", Lower Results Are Better "a",2.4760636850006 "n",2.320358060002 "c",2.4007008369954 "Apache Spark 3.3 - Row Count: 1000000 - Partitions: 500 - Repartition Test Time", Lower Results Are Better "a",3.0256842549998 "n",3.0584527050014 "c",3.2845926000009 "Apache Spark 3.3 - Row Count: 1000000 - Partitions: 500 - Group By Test Time", Lower Results Are Better "a",3.9506342040004 "n",3.7412138020009 "c",3.8904750889997 "Apache Spark 3.3 - Row Count: 1000000 - Partitions: 500 - Calculate Pi Benchmark Using Dataframe", Lower Results Are Better "a", "n",12.097763678998 "c",12.038864547001 "Apache Spark 3.3 - Row Count: 1000000 - Partitions: 500 - Calculate Pi Benchmark", Lower Results Are Better "a", "n", "c", "Apache Spark 3.3 - Row Count: 1000000 - Partitions: 500 - SHA-512 Benchmark Time", Lower Results Are Better "a",4.1248625240005 "n", "c",4.2430642049949 "Apache Spark 3.3 - Row Count: 1000000 - Partitions: 100 - Broadcast Inner Join Test Time", Lower Results Are Better "a",1.3499298239994 "n",1.3231496290027 "c",1.358799366004 "Apache Spark 3.3 - Row Count: 1000000 - Partitions: 100 - Inner Join Test Time", Lower Results Are Better "a", "n",1.5660358809982 "c",1.5989402299965 "Apache Spark 3.3 - Row Count: 1000000 - Partitions: 100 - Repartition Test Time", Lower Results Are Better "a",2.2071628450003 "n",2.2546934150014 "c",2.2625202090057 "Apache Spark 3.3 - Row Count: 1000000 - Partitions: 100 - Group By Test Time", Lower Results Are Better "a",3.5972336300001 "n",3.6501984679999 "c",3.7241963859997 "Apache Spark 3.3 - Row Count: 1000000 - Partitions: 100 - Calculate Pi Benchmark Using Dataframe", Lower Results Are Better "a", "n",11.898674679003 "c",11.986075986999 "Apache Spark 3.3 - Row Count: 1000000 - Partitions: 100 - Calculate Pi Benchmark", Lower Results Are Better "a", "n", "c", "Apache Spark 3.3 - Row Count: 1000000 - Partitions: 100 - SHA-512 Benchmark Time", Lower Results Are Better "a",2.9250109610002 "n",3.0345175200055 "c",3.1731452039967 "Memcached 1.6.18 - Set To Get Ratio: 1:100", Higher Results Are Better "a", "n", "c", "Memcached 1.6.18 - Set To Get Ratio: 1:10", Higher Results Are Better "a", "n", "c", "Memcached 1.6.18 - Set To Get Ratio: 1:1", Higher Results Are Better "a", "n", "c", "Memcached 1.6.18 - Set To Get Ratio: 1:5", Higher Results Are Better "a", "n", "c", "Neural Magic DeepSparse 1.3.2 - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream", Lower Results Are Better "a", "n", "c", "Neural Magic DeepSparse 1.3.2 - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream", Higher Results Are Better "a", "n", "c", "Neural Magic DeepSparse 1.3.2 - Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream", Lower Results Are Better "a", "n", "c", "Neural Magic DeepSparse 1.3.2 - Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream", Higher Results Are Better "a", "n", "c", "Neural Magic DeepSparse 1.3.2 - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream", Lower Results Are Better "a", "n", "c", "Neural Magic DeepSparse 1.3.2 - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream", Higher Results Are Better "a", "n", "c", "Neural Magic DeepSparse 1.3.2 - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream", Lower Results Are Better "a", "n", "c", "Neural Magic DeepSparse 1.3.2 - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream", Higher Results Are Better "a", "n", "c", "Neural Magic DeepSparse 1.3.2 - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream", Lower Results Are Better "a", "n", "c", "Neural Magic DeepSparse 1.3.2 - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream", Higher Results Are Better "a", "n", "c", "Neural Magic DeepSparse 1.3.2 - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream", Lower Results Are Better "a", "n", "c", "Neural Magic DeepSparse 1.3.2 - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream", Higher Results Are Better "a", "n", "c", "Neural Magic DeepSparse 1.3.2 - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream", Lower Results Are Better "a", "n", "c", "Neural Magic DeepSparse 1.3.2 - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream", Higher Results Are Better "a", "n", "c", "Neural Magic DeepSparse 1.3.2 - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream", Lower Results Are Better "a", "n", "c", "Neural Magic DeepSparse 1.3.2 - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream", Higher Results Are Better "a", "n", "c", "Neural Magic DeepSparse 1.3.2 - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream", Lower Results Are Better "a", "n", "c", "Neural Magic DeepSparse 1.3.2 - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream", Higher Results Are Better "a", "n", "c", "Neural Magic DeepSparse 1.3.2 - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream", Lower Results Are Better "a", "n", "c", "Neural Magic DeepSparse 1.3.2 - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream", Higher Results Are Better "a", "n", "c", "Neural Magic DeepSparse 1.3.2 - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream", Lower Results Are Better "a", "n", "c", "Neural Magic DeepSparse 1.3.2 - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream", Higher Results Are Better "a", "n", "c", "Neural Magic DeepSparse 1.3.2 - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream", Lower Results Are Better "a", "n", "c", "Neural Magic DeepSparse 1.3.2 - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream", Higher Results Are Better "a", "n", "c", "Neural Magic DeepSparse 1.3.2 - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream", Lower Results Are Better "a", "n", "c", "Neural Magic DeepSparse 1.3.2 - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream", Higher Results Are Better "a", "n", "c", "Neural Magic DeepSparse 1.3.2 - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-Stream", Lower Results Are Better "a", "n", "c", "Neural Magic DeepSparse 1.3.2 - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-Stream", Higher Results Are Better "a", "n", "c", "Neural Magic DeepSparse 1.3.2 - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream", Lower Results Are Better "a", "n", "c", "Neural Magic DeepSparse 1.3.2 - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream", Higher Results Are Better "a", "n", "c", "Neural Magic DeepSparse 1.3.2 - Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream", Lower Results Are Better "a", "n", "c", "Neural Magic DeepSparse 1.3.2 - Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream", Higher Results Are Better "a", "n", "c", "Neural Magic DeepSparse 1.3.2 - Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream", Lower Results Are Better "a", "n", "c", "Neural Magic DeepSparse 1.3.2 - Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream", Higher Results Are Better "a", "n", "c", "Neural Magic DeepSparse 1.3.2 - Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream", Lower Results Are Better "a", "n", "c", "Neural Magic DeepSparse 1.3.2 - Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream", Higher Results Are Better "a", "n", "c",