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.

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Database Test Suite ^{2 Tests}
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February 01 2023

4 Hours, 44 Minutes

February 02 2023

4 Hours, 42 Minutes

February 02 2023

4 Hours, 41 Minutes

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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. ,,"a","n","c" Processor,,Intel Core i7-1280P @ 4.80GHz (14 Cores / 20 Threads),Intel Core i7-1280P @ 4.80GHz (14 Cores / 20 Threads),Intel Core i7-1280P @ 4.80GHz (14 Cores / 20 Threads) Motherboard,,MSI MS-14C6 (E14C6IMS.115 BIOS),MSI MS-14C6 (E14C6IMS.115 BIOS),MSI MS-14C6 (E14C6IMS.115 BIOS) Chipset,,Intel Alder Lake PCH,Intel Alder Lake PCH,Intel Alder Lake PCH Memory,,16GB,16GB,16GB Disk,,1024GB Micron_3400_MTFDKBA1T0TFH,1024GB Micron_3400_MTFDKBA1T0TFH,1024GB Micron_3400_MTFDKBA1T0TFH Graphics,,MSI Intel ADL GT2 15GB (1450MHz),MSI Intel ADL GT2 15GB (1450MHz),MSI Intel ADL GT2 15GB (1450MHz) Audio,,Realtek ALC274,Realtek ALC274,Realtek ALC274 Network,,Intel Alder Lake-P PCH CNVi WiFi,Intel Alder Lake-P PCH CNVi WiFi,Intel Alder Lake-P PCH CNVi WiFi OS,,Ubuntu 22.10,Ubuntu 22.10,Ubuntu 22.10 Kernel,,5.19.0-29-generic (x86_64),5.19.0-29-generic (x86_64),5.19.0-29-generic (x86_64) Desktop,,Xfce 4.16,Xfce 4.16,Xfce 4.16 Display Server,,X Server 1.21.1.4,X Server 1.21.1.4,X Server 1.21.1.4 OpenGL,,4.6 Mesa 22.2.1,4.6 Mesa 22.2.1,4.6 Mesa 22.2.1 OpenCL,,OpenCL 3.0,OpenCL 3.0,OpenCL 3.0 Vulkan,,1.3.224,1.3.224,1.3.224 Compiler,,GCC 12.2.0,GCC 12.2.0,GCC 12.2.0 File-System,,ext4,ext4,ext4 Screen Resolution,,1920x1080,1920x1080,1920x1080 ,,"a","n","c" "Apache Spark - Row Count: 1000000 - Partitions: 100 - SHA-512 Benchmark Time (sec)",LIB,2.93,3.03,3.17 "Apache Spark - Row Count: 1000000 - Partitions: 100 - Calculate Pi Benchmark (sec)",LIB,207.951877263,207.107599919,206.319826158 "Apache Spark - Row Count: 1000000 - Partitions: 100 - Calculate Pi Benchmark Using Dataframe (sec)",LIB,11.906098387,11.90,11.99 "Apache Spark - Row Count: 1000000 - Partitions: 100 - Group By Test Time (sec)",LIB,3.60,3.65,3.72 "Apache Spark - Row Count: 1000000 - Partitions: 100 - Repartition Test Time (sec)",LIB,2.21,2.25,2.26 "Apache Spark - Row Count: 1000000 - Partitions: 100 - Inner Join Test Time (sec)",LIB,1.583333601,1.57,1.60 "Apache Spark - Row Count: 1000000 - Partitions: 100 - Broadcast Inner Join Test Time (sec)",LIB,1.35,1.32,1.36 "Apache Spark - Row Count: 1000000 - Partitions: 500 - SHA-512 Benchmark Time (sec)",LIB,4.12,4.155978363,4.24 "Apache Spark - Row Count: 1000000 - Partitions: 500 - Calculate Pi Benchmark (sec)",LIB,209.933188681,210.212547098,210.110708861 "Apache Spark - Row Count: 1000000 - Partitions: 500 - Calculate Pi Benchmark Using Dataframe (sec)",LIB,11.944524348,12.10,12.04 "Apache Spark - Row Count: 1000000 - Partitions: 500 - Group By Test Time (sec)",LIB,3.95,3.74,3.89 "Apache Spark - Row Count: 1000000 - Partitions: 500 - Repartition Test Time (sec)",LIB,3.03,3.06,3.28 "Apache Spark - Row Count: 1000000 - Partitions: 500 - Inner Join Test Time (sec)",LIB,2.48,2.32,2.40 "Apache Spark - Row Count: 1000000 - Partitions: 500 - Broadcast Inner Join Test Time (sec)",LIB,1.98,1.93,1.95 "Apache Spark - Row Count: 1000000 - Partitions: 1000 - SHA-512 Benchmark Time (sec)",LIB,4.56,4.39,4.36 "Apache Spark - Row Count: 1000000 - Partitions: 1000 - Calculate Pi Benchmark (sec)",LIB,207.482634438,208.49,208.244056069 "Apache Spark - Row Count: 1000000 - Partitions: 1000 - Calculate Pi Benchmark Using Dataframe (sec)",LIB,11.97,12.02,11.87 "Apache Spark - Row Count: 1000000 - Partitions: 1000 - Group By Test Time (sec)",LIB,4.61,4.75,4.58 "Apache Spark - Row Count: 1000000 - Partitions: 1000 - Repartition Test Time (sec)",LIB,3.32,3.39,3.36 "Apache Spark - Row Count: 1000000 - Partitions: 1000 - Inner Join Test Time (sec)",LIB,2.82,2.87,2.81 "Apache Spark - Row Count: 1000000 - Partitions: 1000 - Broadcast Inner Join Test Time (sec)",LIB,2.37,2.24,2.22 "Apache Spark - Row Count: 1000000 - Partitions: 2000 - SHA-512 Benchmark Time (sec)",LIB,4.79,4.99,4.96 "Apache Spark - Row Count: 1000000 - Partitions: 2000 - Calculate Pi Benchmark (sec)",LIB,206.580142731,213.35,207.73175806 "Apache Spark - Row Count: 1000000 - Partitions: 2000 - Calculate Pi Benchmark Using Dataframe (sec)",LIB,12.003429445,12.70,11.89 "Apache Spark - Row Count: 1000000 - Partitions: 2000 - Group By Test Time (sec)",LIB,5.17,5.29,5.07 "Apache Spark - Row Count: 1000000 - Partitions: 2000 - Repartition Test Time (sec)",LIB,3.595425861,3.60,3.65 "Apache Spark - Row Count: 1000000 - Partitions: 2000 - Inner Join Test Time (sec)",LIB,3.349109111,3.50,3.49 "Apache Spark - Row Count: 1000000 - Partitions: 2000 - Broadcast Inner Join Test Time (sec)",LIB,2.74,2.81,2.71 "Apache Spark - Row Count: 10000000 - Partitions: 100 - SHA-512 Benchmark Time (sec)",LIB,15.513832371,15.41,15.160105142 "Apache Spark - Row Count: 10000000 - Partitions: 100 - Calculate Pi Benchmark (sec)",LIB,207.559282092,208.173581938,208.61783672 "Apache Spark - Row Count: 10000000 - Partitions: 100 - Calculate Pi Benchmark Using Dataframe (sec)",LIB,11.840200882,12.02,11.80 "Apache Spark - Row Count: 10000000 - Partitions: 100 - Group By Test Time (sec)",LIB,8.01,8.39,8.48 "Apache Spark - Row Count: 10000000 - Partitions: 100 - Repartition Test Time (sec)",LIB,13.07,11.79,11.63 "Apache Spark - Row Count: 10000000 - Partitions: 100 - Inner Join Test Time (sec)",LIB,14.087262577,13.71,13.41 "Apache Spark - Row Count: 10000000 - Partitions: 100 - Broadcast Inner Join Test Time (sec)",LIB,14.048404932,13.18,12.59 "Apache Spark - Row Count: 10000000 - Partitions: 500 - SHA-512 Benchmark Time (sec)",LIB,16.492305717,16.51,16.58 "Apache Spark - Row Count: 10000000 - Partitions: 500 - Calculate Pi Benchmark (sec)",LIB,207.452984342,207.757873404,209.010077237 "Apache Spark - Row Count: 10000000 - Partitions: 500 - Calculate Pi Benchmark Using Dataframe (sec)",LIB,11.87,12.03,11.88 "Apache Spark - Row Count: 10000000 - Partitions: 500 - Group By Test Time (sec)",LIB,8.93,9.02,8.88 "Apache Spark - Row Count: 10000000 - Partitions: 500 - Repartition Test Time (sec)",LIB,11.950580371,12.10,12.533537256 "Apache Spark - Row Count: 10000000 - Partitions: 500 - Inner Join Test Time (sec)",LIB,14.41,14.60,15.33 "Apache Spark - Row Count: 10000000 - Partitions: 500 - Broadcast Inner Join Test Time (sec)",LIB,12.703110342,13.55,14.39 "Apache Spark - Row Count: 10000000 - Partitions: 1000 - SHA-512 Benchmark Time (sec)",LIB,15.926342675,16.03069788,15.95 "Apache Spark - Row Count: 10000000 - Partitions: 1000 - Calculate Pi Benchmark (sec)",LIB,207.523566094,207.466115076,207.56 "Apache Spark - Row Count: 10000000 - Partitions: 1000 - Calculate Pi Benchmark Using Dataframe (sec)",LIB,11.90,11.854006987,11.79 "Apache Spark - Row Count: 10000000 - Partitions: 1000 - Group By Test Time (sec)",LIB,9.05,10.43,8.33 "Apache Spark - Row Count: 10000000 - Partitions: 1000 - Repartition Test Time (sec)",LIB,12.18,11.11,12.07 "Apache Spark - Row Count: 10000000 - Partitions: 1000 - Inner Join Test Time (sec)",LIB,13.443032784,14.46,13.77 "Apache Spark - Row Count: 10000000 - Partitions: 1000 - Broadcast Inner Join Test Time (sec)",LIB,13.10,12.98,13.54 "Apache Spark - Row Count: 10000000 - Partitions: 2000 - SHA-512 Benchmark Time (sec)",LIB,16.768759819,16.69,16.87 "Apache Spark - Row Count: 10000000 - Partitions: 2000 - Calculate Pi Benchmark (sec)",LIB,207.554569999,207.762851188,208.248847794 "Apache Spark - Row Count: 10000000 - Partitions: 2000 - Calculate Pi Benchmark Using Dataframe (sec)",LIB,11.85,11.74,11.84 "Apache Spark - Row Count: 10000000 - Partitions: 2000 - Group By Test Time (sec)",LIB,9.21,9.19,9.34 "Apache Spark - Row Count: 10000000 - Partitions: 2000 - Repartition Test Time (sec)",LIB,12.041006514,12.26,12.21 "Apache Spark - Row Count: 10000000 - Partitions: 2000 - Inner Join Test Time (sec)",LIB,14.32,14.48,14.15 "Apache Spark - Row Count: 10000000 - Partitions: 2000 - Broadcast Inner Join Test Time (sec)",LIB,12.84,13.16,13.29 "Memcached - Set To Get Ratio: 1:1 (Ops/sec)",HIB,1767830.19,1767278.13,1767103.07 "Memcached - Set To Get Ratio: 1:5 (Ops/sec)",HIB,1869070,1781728.27,1823583.08 "Memcached - Set To Get Ratio: 1:10 (Ops/sec)",HIB,1742709.67,1739460.62,1750912.91 "Memcached - Set To Get Ratio: 1:100 (Ops/sec)",HIB,1665301.7,1686349.51,1662246.52 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,4.5104,4.5519,4.5475 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,1534.4547,1508.5481,1520.5369 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream (items/sec)",HIB,4.2064,4.2403,4.1882 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream (ms/batch)",LIB,237.725,235.821,238.7595 "Neural Magic DeepSparse - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,67.1916,66.9068,67.1439 "Neural Magic DeepSparse - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,104.1022,104.5305,104.02 "Neural Magic DeepSparse - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-Stream (items/sec)",HIB,44.0878,44.3119,44.2419 "Neural Magic DeepSparse - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-Stream (ms/batch)",LIB,22.6707,22.5568,22.592 "Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,19.7804,18.6035,18.8282 "Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,351.5017,374.0639,367.8485 "Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream (items/sec)",HIB,14.8331,14.7185,14.8663 "Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,67.4054,67.9297,67.2541 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,28.0309,28.4189,28.1703 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,248.4341,244.8894,247.3813 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream (items/sec)",HIB,23.4536,23.1872,23.2034 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream (ms/batch)",LIB,42.6237,43.1131,43.0829 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,67.1943,60.9341,62.1742 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,103.9281,114.7806,112.441 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream (items/sec)",HIB,50.53,43.6605,43.4516 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream (ms/batch)",LIB,19.7839,22.8974,23.0076 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,43.4532,40.9985,40.8184 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,160.8432,170.1949,171.2531 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream (items/sec)",HIB,33.3497,33.1348,33.2175 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream (ms/batch)",LIB,29.9799,30.1744,30.0991 "Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,6.3049,6.1199,5.7934 "Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,1102.4971,1137.8994,1169.8973 "Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream (items/sec)",HIB,5.7188,5.6899,5.7275 "Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream (ms/batch)",LIB,174.8444,175.7334,174.5776 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,19.0193,19.5227,19.1766 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,365.1139,357.4797,361.7257 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream (items/sec)",HIB,16.1559,16.235,16.195 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,61.8911,61.5894,61.741 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,4.5054,4.4135,4.4061 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,1545.7642,1523.2204,1529.3919 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream (items/sec)",HIB,4.1644,4.1721,4.181 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,240.1261,239.6798,239.168