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 adl feb - Phoronix Test Suite 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.
HTML result view exported from: https://openbenchmarking.org/result/2302026-NE-ADLFEB23315&grt&rdt&export=pdf .
adl feb Processor Motherboard Chipset Memory Disk Graphics Audio Network OS Kernel Desktop Display Server OpenGL OpenCL Vulkan Compiler File-System Screen Resolution a n c Intel Core i7-1280P @ 4.80GHz (14 Cores / 20 Threads) MSI MS-14C6 (E14C6IMS.115 BIOS) Intel Alder Lake PCH 16GB 1024GB Micron_3400_MTFDKBA1T0TFH MSI Intel ADL GT2 15GB (1450MHz) Realtek ALC274 Intel Alder Lake-P PCH CNVi WiFi Ubuntu 22.10 5.19.0-29-generic (x86_64) Xfce 4.16 X Server 1.21.1.4 4.6 Mesa 22.2.1 OpenCL 3.0 1.3.224 GCC 12.2.0 ext4 1920x1080 OpenBenchmarking.org Kernel Details - Transparent Huge Pages: madvise Compiler Details - --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-defaulted --enable-offload-targets=nvptx-none=/build/gcc-12-U8K4Qv/gcc-12-12.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-12-U8K4Qv/gcc-12-12.2.0/debian/tmp-gcn/usr --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --program-prefix=x86_64-linux-gnu- --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -v Processor Details - Scaling Governor: intel_pstate powersave (EPP: balance_performance) - CPU Microcode: 0x421 - Thermald 2.5.1 Java Details - OpenJDK Runtime Environment (build 11.0.17+8-post-Ubuntu-1ubuntu2) Python Details - Python 3.10.7 Security Details - itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + retbleed: Not affected + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Enhanced IBRS IBPB: conditional RSB filling PBRSB-eIBRS: SW sequence + srbds: Not affected + tsx_async_abort: Not affected
adl feb spark: 1000000 - 100 - SHA-512 Benchmark Time spark: 1000000 - 100 - Calculate Pi Benchmark spark: 1000000 - 100 - Calculate Pi Benchmark Using Dataframe spark: 1000000 - 100 - Group By Test Time spark: 1000000 - 100 - Repartition Test Time spark: 1000000 - 100 - Inner Join Test Time spark: 1000000 - 100 - Broadcast Inner Join Test Time spark: 1000000 - 500 - SHA-512 Benchmark Time spark: 1000000 - 500 - Calculate Pi Benchmark spark: 1000000 - 500 - Calculate Pi Benchmark Using Dataframe spark: 1000000 - 500 - Group By Test Time spark: 1000000 - 500 - Repartition Test Time spark: 1000000 - 500 - Inner Join Test Time spark: 1000000 - 500 - Broadcast Inner Join Test Time spark: 1000000 - 1000 - SHA-512 Benchmark Time spark: 1000000 - 1000 - Calculate Pi Benchmark spark: 1000000 - 1000 - Calculate Pi Benchmark Using Dataframe spark: 1000000 - 1000 - Group By Test Time spark: 1000000 - 1000 - Repartition Test Time spark: 1000000 - 1000 - Inner Join Test Time spark: 1000000 - 1000 - Broadcast Inner Join Test Time spark: 1000000 - 2000 - SHA-512 Benchmark Time spark: 1000000 - 2000 - Calculate Pi Benchmark spark: 1000000 - 2000 - Calculate Pi Benchmark Using Dataframe spark: 1000000 - 2000 - Group By Test Time spark: 1000000 - 2000 - Repartition Test Time spark: 1000000 - 2000 - Inner Join Test Time spark: 1000000 - 2000 - Broadcast Inner Join Test Time spark: 10000000 - 100 - SHA-512 Benchmark Time spark: 10000000 - 100 - Calculate Pi Benchmark spark: 10000000 - 100 - Calculate Pi Benchmark Using Dataframe spark: 10000000 - 100 - Group By Test Time spark: 10000000 - 100 - Repartition Test Time spark: 10000000 - 100 - Inner Join Test Time spark: 10000000 - 100 - Broadcast Inner Join Test Time spark: 10000000 - 500 - SHA-512 Benchmark Time spark: 10000000 - 500 - Calculate Pi Benchmark spark: 10000000 - 500 - Calculate Pi Benchmark Using Dataframe spark: 10000000 - 500 - Group By Test Time spark: 10000000 - 500 - Repartition Test Time spark: 10000000 - 500 - Inner Join Test Time spark: 10000000 - 500 - Broadcast Inner Join Test Time spark: 10000000 - 1000 - SHA-512 Benchmark Time spark: 10000000 - 1000 - Calculate Pi Benchmark spark: 10000000 - 1000 - Calculate Pi Benchmark Using Dataframe spark: 10000000 - 1000 - Group By Test Time spark: 10000000 - 1000 - Repartition Test Time spark: 10000000 - 1000 - Inner Join Test Time spark: 10000000 - 1000 - Broadcast Inner Join Test Time spark: 10000000 - 2000 - SHA-512 Benchmark Time spark: 10000000 - 2000 - Calculate Pi Benchmark spark: 10000000 - 2000 - Calculate Pi Benchmark Using Dataframe spark: 10000000 - 2000 - Group By Test Time spark: 10000000 - 2000 - Repartition Test Time spark: 10000000 - 2000 - Inner Join Test Time spark: 10000000 - 2000 - Broadcast Inner Join Test Time memcached: 1:1 memcached: 1:5 memcached: 1:10 memcached: 1:100 deepsparse: NLP Document Classification, oBERT base uncased on IMDB - Asynchronous Multi-Stream deepsparse: NLP Document Classification, oBERT base uncased on IMDB - Asynchronous Multi-Stream deepsparse: NLP Document Classification, oBERT base uncased on IMDB - Synchronous Single-Stream deepsparse: NLP Document Classification, oBERT base uncased on IMDB - Synchronous Single-Stream deepsparse: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Asynchronous Multi-Stream deepsparse: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Asynchronous Multi-Stream deepsparse: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Synchronous Single-Stream deepsparse: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Synchronous Single-Stream deepsparse: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Asynchronous Multi-Stream deepsparse: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Asynchronous Multi-Stream deepsparse: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Synchronous Single-Stream deepsparse: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Synchronous Single-Stream deepsparse: CV Detection, YOLOv5s COCO - Asynchronous Multi-Stream deepsparse: CV Detection, YOLOv5s COCO - Asynchronous Multi-Stream deepsparse: CV Detection, YOLOv5s COCO - Synchronous Single-Stream deepsparse: CV Detection, YOLOv5s COCO - Synchronous Single-Stream deepsparse: CV Classification, ResNet-50 ImageNet - Asynchronous Multi-Stream deepsparse: CV Classification, ResNet-50 ImageNet - Asynchronous Multi-Stream deepsparse: CV Classification, ResNet-50 ImageNet - Synchronous Single-Stream deepsparse: CV Classification, ResNet-50 ImageNet - Synchronous Single-Stream deepsparse: NLP Text Classification, DistilBERT mnli - Asynchronous Multi-Stream deepsparse: NLP Text Classification, DistilBERT mnli - Asynchronous Multi-Stream deepsparse: NLP Text Classification, DistilBERT mnli - Synchronous Single-Stream deepsparse: NLP Text Classification, DistilBERT mnli - Synchronous Single-Stream deepsparse: CV Segmentation, 90% Pruned YOLACT Pruned - Asynchronous Multi-Stream deepsparse: CV Segmentation, 90% Pruned YOLACT Pruned - Asynchronous Multi-Stream deepsparse: CV Segmentation, 90% Pruned YOLACT Pruned - Synchronous Single-Stream deepsparse: CV Segmentation, 90% Pruned YOLACT Pruned - Synchronous Single-Stream deepsparse: NLP Text Classification, BERT base uncased SST2 - Asynchronous Multi-Stream deepsparse: NLP Text Classification, BERT base uncased SST2 - Asynchronous Multi-Stream deepsparse: NLP Text Classification, BERT base uncased SST2 - Synchronous Single-Stream deepsparse: NLP Text Classification, BERT base uncased SST2 - Synchronous Single-Stream deepsparse: NLP Token Classification, BERT base uncased conll2003 - Asynchronous Multi-Stream deepsparse: NLP Token Classification, BERT base uncased conll2003 - Asynchronous Multi-Stream deepsparse: NLP Token Classification, BERT base uncased conll2003 - Synchronous Single-Stream deepsparse: NLP Token Classification, BERT base uncased conll2003 - Synchronous Single-Stream a n c 2.93 207.951877263 11.906098387 3.60 2.21 1.583333601 1.35 4.12 209.933188681 11.944524348 3.95 3.03 2.48 1.98 4.56 207.482634438 11.97 4.61 3.32 2.82 2.37 4.79 206.580142731 12.003429445 5.17 3.595425861 3.349109111 2.74 15.513832371 207.559282092 11.840200882 8.01 13.07 14.087262577 14.048404932 16.492305717 207.452984342 11.87 8.93 11.950580371 14.41 12.703110342 15.926342675 207.523566094 11.90 9.05 12.18 13.443032784 13.10 16.768759819 207.554569999 11.85 9.21 12.041006514 14.32 12.84 1767830.19 1869070 1742709.67 1665301.7 4.5104 1534.4547 4.2064 237.725 67.1916 104.1022 44.0878 22.6707 19.7804 351.5017 14.8331 67.4054 28.0309 248.4341 23.4536 42.6237 67.1943 103.9281 50.53 19.7839 43.4532 160.8432 33.3497 29.9799 6.3049 1102.4971 5.7188 174.8444 19.0193 365.1139 16.1559 61.8911 4.5054 1545.7642 4.1644 240.1261 3.03 207.107599919 11.90 3.65 2.25 1.57 1.32 4.155978363 210.212547098 12.10 3.74 3.06 2.32 1.93 4.39 208.49 12.02 4.75 3.39 2.87 2.24 4.99 213.35 12.70 5.29 3.60 3.50 2.81 15.41 208.173581938 12.02 8.39 11.79 13.71 13.18 16.51 207.757873404 12.03 9.02 12.10 14.60 13.55 16.03069788 207.466115076 11.854006987 10.43 11.11 14.46 12.98 16.69 207.762851188 11.74 9.19 12.26 14.48 13.16 1767278.13 1781728.27 1739460.62 1686349.51 4.5519 1508.5481 4.2403 235.821 66.9068 104.5305 44.3119 22.5568 18.6035 374.0639 14.7185 67.9297 28.4189 244.8894 23.1872 43.1131 60.9341 114.7806 43.6605 22.8974 40.9985 170.1949 33.1348 30.1744 6.1199 1137.8994 5.6899 175.7334 19.5227 357.4797 16.235 61.5894 4.4135 1523.2204 4.1721 239.6798 3.17 206.319826158 11.99 3.72 2.26 1.60 1.36 4.24 210.110708861 12.04 3.89 3.28 2.40 1.95 4.36 208.244056069 11.87 4.58 3.36 2.81 2.22 4.96 207.73175806 11.89 5.07 3.65 3.49 2.71 15.160105142 208.61783672 11.80 8.48 11.63 13.41 12.59 16.58 209.010077237 11.88 8.88 12.533537256 15.33 14.39 15.95 207.56 11.79 8.33 12.07 13.77 13.54 16.87 208.248847794 11.84 9.34 12.21 14.15 13.29 1767103.07 1823583.08 1750912.91 1662246.52 4.5475 1520.5369 4.1882 238.7595 67.1439 104.02 44.2419 22.592 18.8282 367.8485 14.8663 67.2541 28.1703 247.3813 23.2034 43.0829 62.1742 112.441 43.4516 23.0076 40.8184 171.2531 33.2175 30.0991 5.7934 1169.8973 5.7275 174.5776 19.1766 361.7257 16.195 61.741 4.4061 1529.3919 4.181 239.168 OpenBenchmarking.org
Apache Spark Row Count: 1000000 - Partitions: 100 - SHA-512 Benchmark Time OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 1000000 - Partitions: 100 - SHA-512 Benchmark Time a n c 0.7133 1.4266 2.1399 2.8532 3.5665 2.93 3.03 3.17
Apache Spark Row Count: 1000000 - Partitions: 100 - Calculate Pi Benchmark OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 1000000 - Partitions: 100 - Calculate Pi Benchmark a n c 50 100 150 200 250 207.95 207.11 206.32
Apache Spark Row Count: 1000000 - Partitions: 100 - Calculate Pi Benchmark Using Dataframe OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 1000000 - Partitions: 100 - Calculate Pi Benchmark Using Dataframe a n c 3 6 9 12 15 11.91 11.90 11.99
Apache Spark Row Count: 1000000 - Partitions: 100 - Group By Test Time OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 1000000 - Partitions: 100 - Group By Test Time a n c 0.837 1.674 2.511 3.348 4.185 3.60 3.65 3.72
Apache Spark Row Count: 1000000 - Partitions: 100 - Repartition Test Time OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 1000000 - Partitions: 100 - Repartition Test Time a n c 0.5085 1.017 1.5255 2.034 2.5425 2.21 2.25 2.26
Apache Spark Row Count: 1000000 - Partitions: 100 - Inner Join Test Time OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 1000000 - Partitions: 100 - Inner Join Test Time a n c 0.36 0.72 1.08 1.44 1.8 1.583333601 1.570000000 1.600000000
Apache Spark Row Count: 1000000 - Partitions: 100 - Broadcast Inner Join Test Time OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 1000000 - Partitions: 100 - Broadcast Inner Join Test Time a n c 0.306 0.612 0.918 1.224 1.53 1.35 1.32 1.36
Apache Spark Row Count: 1000000 - Partitions: 500 - SHA-512 Benchmark Time OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 1000000 - Partitions: 500 - SHA-512 Benchmark Time a n c 0.954 1.908 2.862 3.816 4.77 4.120000000 4.155978363 4.240000000
Apache Spark Row Count: 1000000 - Partitions: 500 - Calculate Pi Benchmark OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 1000000 - Partitions: 500 - Calculate Pi Benchmark a n c 50 100 150 200 250 209.93 210.21 210.11
Apache Spark Row Count: 1000000 - Partitions: 500 - Calculate Pi Benchmark Using Dataframe OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 1000000 - Partitions: 500 - Calculate Pi Benchmark Using Dataframe a n c 3 6 9 12 15 11.94 12.10 12.04
Apache Spark Row Count: 1000000 - Partitions: 500 - Group By Test Time OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 1000000 - Partitions: 500 - Group By Test Time a n c 0.8888 1.7776 2.6664 3.5552 4.444 3.95 3.74 3.89
Apache Spark Row Count: 1000000 - Partitions: 500 - Repartition Test Time OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 1000000 - Partitions: 500 - Repartition Test Time a n c 0.738 1.476 2.214 2.952 3.69 3.03 3.06 3.28
Apache Spark Row Count: 1000000 - Partitions: 500 - Inner Join Test Time OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 1000000 - Partitions: 500 - Inner Join Test Time a n c 0.558 1.116 1.674 2.232 2.79 2.48 2.32 2.40
Apache Spark Row Count: 1000000 - Partitions: 500 - Broadcast Inner Join Test Time OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 1000000 - Partitions: 500 - Broadcast Inner Join Test Time a n c 0.4455 0.891 1.3365 1.782 2.2275 1.98 1.93 1.95
Apache Spark Row Count: 1000000 - Partitions: 1000 - SHA-512 Benchmark Time OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 1000000 - Partitions: 1000 - SHA-512 Benchmark Time a n c 1.026 2.052 3.078 4.104 5.13 4.56 4.39 4.36
Apache Spark Row Count: 1000000 - Partitions: 1000 - Calculate Pi Benchmark OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 1000000 - Partitions: 1000 - Calculate Pi Benchmark a n c 50 100 150 200 250 207.48 208.49 208.24
Apache Spark Row Count: 1000000 - Partitions: 1000 - Calculate Pi Benchmark Using Dataframe OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 1000000 - Partitions: 1000 - Calculate Pi Benchmark Using Dataframe a n c 3 6 9 12 15 11.97 12.02 11.87
Apache Spark Row Count: 1000000 - Partitions: 1000 - Group By Test Time OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 1000000 - Partitions: 1000 - Group By Test Time a n c 1.0688 2.1376 3.2064 4.2752 5.344 4.61 4.75 4.58
Apache Spark Row Count: 1000000 - Partitions: 1000 - Repartition Test Time OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 1000000 - Partitions: 1000 - Repartition Test Time a n c 0.7628 1.5256 2.2884 3.0512 3.814 3.32 3.39 3.36
Apache Spark Row Count: 1000000 - Partitions: 1000 - Inner Join Test Time OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 1000000 - Partitions: 1000 - Inner Join Test Time a n c 0.6458 1.2916 1.9374 2.5832 3.229 2.82 2.87 2.81
Apache Spark Row Count: 1000000 - Partitions: 1000 - Broadcast Inner Join Test Time OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 1000000 - Partitions: 1000 - Broadcast Inner Join Test Time a n c 0.5333 1.0666 1.5999 2.1332 2.6665 2.37 2.24 2.22
Apache Spark Row Count: 1000000 - Partitions: 2000 - SHA-512 Benchmark Time OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 1000000 - Partitions: 2000 - SHA-512 Benchmark Time a n c 1.1228 2.2456 3.3684 4.4912 5.614 4.79 4.99 4.96
Apache Spark Row Count: 1000000 - Partitions: 2000 - Calculate Pi Benchmark OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 1000000 - Partitions: 2000 - Calculate Pi Benchmark a n c 50 100 150 200 250 206.58 213.35 207.73
Apache Spark Row Count: 1000000 - Partitions: 2000 - Calculate Pi Benchmark Using Dataframe OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 1000000 - Partitions: 2000 - Calculate Pi Benchmark Using Dataframe a n c 3 6 9 12 15 12.00 12.70 11.89
Apache Spark Row Count: 1000000 - Partitions: 2000 - Group By Test Time OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 1000000 - Partitions: 2000 - Group By Test Time a n c 1.1903 2.3806 3.5709 4.7612 5.9515 5.17 5.29 5.07
Apache Spark Row Count: 1000000 - Partitions: 2000 - Repartition Test Time OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 1000000 - Partitions: 2000 - Repartition Test Time a n c 0.8213 1.6426 2.4639 3.2852 4.1065 3.595425861 3.600000000 3.650000000
Apache Spark Row Count: 1000000 - Partitions: 2000 - Inner Join Test Time OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 1000000 - Partitions: 2000 - Inner Join Test Time a n c 0.7875 1.575 2.3625 3.15 3.9375 3.349109111 3.500000000 3.490000000
Apache Spark Row Count: 1000000 - Partitions: 2000 - Broadcast Inner Join Test Time OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 1000000 - Partitions: 2000 - Broadcast Inner Join Test Time a n c 0.6323 1.2646 1.8969 2.5292 3.1615 2.74 2.81 2.71
Apache Spark Row Count: 10000000 - Partitions: 100 - SHA-512 Benchmark Time OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 10000000 - Partitions: 100 - SHA-512 Benchmark Time a n c 4 8 12 16 20 15.51 15.41 15.16
Apache Spark Row Count: 10000000 - Partitions: 100 - Calculate Pi Benchmark OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 10000000 - Partitions: 100 - Calculate Pi Benchmark a n c 50 100 150 200 250 207.56 208.17 208.62
Apache Spark Row Count: 10000000 - Partitions: 100 - Calculate Pi Benchmark Using Dataframe OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 10000000 - Partitions: 100 - Calculate Pi Benchmark Using Dataframe a n c 3 6 9 12 15 11.84 12.02 11.80
Apache Spark Row Count: 10000000 - Partitions: 100 - Group By Test Time OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 10000000 - Partitions: 100 - Group By Test Time a n c 2 4 6 8 10 8.01 8.39 8.48
Apache Spark Row Count: 10000000 - Partitions: 100 - Repartition Test Time OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 10000000 - Partitions: 100 - Repartition Test Time a n c 3 6 9 12 15 13.07 11.79 11.63
Apache Spark Row Count: 10000000 - Partitions: 100 - Inner Join Test Time OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 10000000 - Partitions: 100 - Inner Join Test Time a n c 4 8 12 16 20 14.09 13.71 13.41
Apache Spark Row Count: 10000000 - Partitions: 100 - Broadcast Inner Join Test Time OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 10000000 - Partitions: 100 - Broadcast Inner Join Test Time a n c 4 8 12 16 20 14.05 13.18 12.59
Apache Spark Row Count: 10000000 - Partitions: 500 - SHA-512 Benchmark Time OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 10000000 - Partitions: 500 - SHA-512 Benchmark Time a n c 4 8 12 16 20 16.49 16.51 16.58
Apache Spark Row Count: 10000000 - Partitions: 500 - Calculate Pi Benchmark OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 10000000 - Partitions: 500 - Calculate Pi Benchmark a n c 50 100 150 200 250 207.45 207.76 209.01
Apache Spark Row Count: 10000000 - Partitions: 500 - Calculate Pi Benchmark Using Dataframe OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 10000000 - Partitions: 500 - Calculate Pi Benchmark Using Dataframe a n c 3 6 9 12 15 11.87 12.03 11.88
Apache Spark Row Count: 10000000 - Partitions: 500 - Group By Test Time OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 10000000 - Partitions: 500 - Group By Test Time a n c 3 6 9 12 15 8.93 9.02 8.88
Apache Spark Row Count: 10000000 - Partitions: 500 - Repartition Test Time OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 10000000 - Partitions: 500 - Repartition Test Time a n c 3 6 9 12 15 11.95 12.10 12.53
Apache Spark Row Count: 10000000 - Partitions: 500 - Inner Join Test Time OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 10000000 - Partitions: 500 - Inner Join Test Time a n c 4 8 12 16 20 14.41 14.60 15.33
Apache Spark Row Count: 10000000 - Partitions: 500 - Broadcast Inner Join Test Time OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 10000000 - Partitions: 500 - Broadcast Inner Join Test Time a n c 4 8 12 16 20 12.70 13.55 14.39
Apache Spark Row Count: 10000000 - Partitions: 1000 - SHA-512 Benchmark Time OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 10000000 - Partitions: 1000 - SHA-512 Benchmark Time a n c 4 8 12 16 20 15.93 16.03 15.95
Apache Spark Row Count: 10000000 - Partitions: 1000 - Calculate Pi Benchmark OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 10000000 - Partitions: 1000 - Calculate Pi Benchmark a n c 50 100 150 200 250 207.52 207.47 207.56
Apache Spark Row Count: 10000000 - Partitions: 1000 - Calculate Pi Benchmark Using Dataframe OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 10000000 - Partitions: 1000 - Calculate Pi Benchmark Using Dataframe a n c 3 6 9 12 15 11.90 11.85 11.79
Apache Spark Row Count: 10000000 - Partitions: 1000 - Group By Test Time OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 10000000 - Partitions: 1000 - Group By Test Time a n c 3 6 9 12 15 9.05 10.43 8.33
Apache Spark Row Count: 10000000 - Partitions: 1000 - Repartition Test Time OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 10000000 - Partitions: 1000 - Repartition Test Time a n c 3 6 9 12 15 12.18 11.11 12.07
Apache Spark Row Count: 10000000 - Partitions: 1000 - Inner Join Test Time OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 10000000 - Partitions: 1000 - Inner Join Test Time a n c 4 8 12 16 20 13.44 14.46 13.77
Apache Spark Row Count: 10000000 - Partitions: 1000 - Broadcast Inner Join Test Time OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 10000000 - Partitions: 1000 - Broadcast Inner Join Test Time a n c 3 6 9 12 15 13.10 12.98 13.54
Apache Spark Row Count: 10000000 - Partitions: 2000 - SHA-512 Benchmark Time OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 10000000 - Partitions: 2000 - SHA-512 Benchmark Time a n c 4 8 12 16 20 16.77 16.69 16.87
Apache Spark Row Count: 10000000 - Partitions: 2000 - Calculate Pi Benchmark OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 10000000 - Partitions: 2000 - Calculate Pi Benchmark a n c 50 100 150 200 250 207.55 207.76 208.25
Apache Spark Row Count: 10000000 - Partitions: 2000 - Calculate Pi Benchmark Using Dataframe OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 10000000 - Partitions: 2000 - Calculate Pi Benchmark Using Dataframe a n c 3 6 9 12 15 11.85 11.74 11.84
Apache Spark Row Count: 10000000 - Partitions: 2000 - Group By Test Time OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 10000000 - Partitions: 2000 - Group By Test Time a n c 3 6 9 12 15 9.21 9.19 9.34
Apache Spark Row Count: 10000000 - Partitions: 2000 - Repartition Test Time OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 10000000 - Partitions: 2000 - Repartition Test Time a n c 3 6 9 12 15 12.04 12.26 12.21
Apache Spark Row Count: 10000000 - Partitions: 2000 - Inner Join Test Time OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 10000000 - Partitions: 2000 - Inner Join Test Time a n c 4 8 12 16 20 14.32 14.48 14.15
Apache Spark Row Count: 10000000 - Partitions: 2000 - Broadcast Inner Join Test Time OpenBenchmarking.org Seconds, Fewer Is Better Apache Spark 3.3 Row Count: 10000000 - Partitions: 2000 - Broadcast Inner Join Test Time a n c 3 6 9 12 15 12.84 13.16 13.29
Memcached Set To Get Ratio: 1:1 OpenBenchmarking.org Ops/sec, More Is Better Memcached 1.6.18 Set To Get Ratio: 1:1 a n c 400K 800K 1200K 1600K 2000K 1767830.19 1767278.13 1767103.07 1. (CXX) g++ options: -O2 -levent_openssl -levent -lcrypto -lssl -lpthread -lz -lpcre
Memcached Set To Get Ratio: 1:5 OpenBenchmarking.org Ops/sec, More Is Better Memcached 1.6.18 Set To Get Ratio: 1:5 a n c 400K 800K 1200K 1600K 2000K 1869070.00 1781728.27 1823583.08 1. (CXX) g++ options: -O2 -levent_openssl -levent -lcrypto -lssl -lpthread -lz -lpcre
Memcached Set To Get Ratio: 1:10 OpenBenchmarking.org Ops/sec, More Is Better Memcached 1.6.18 Set To Get Ratio: 1:10 a n c 400K 800K 1200K 1600K 2000K 1742709.67 1739460.62 1750912.91 1. (CXX) g++ options: -O2 -levent_openssl -levent -lcrypto -lssl -lpthread -lz -lpcre
Memcached Set To Get Ratio: 1:100 OpenBenchmarking.org Ops/sec, More Is Better Memcached 1.6.18 Set To Get Ratio: 1:100 a n c 400K 800K 1200K 1600K 2000K 1665301.70 1686349.51 1662246.52 1. (CXX) g++ options: -O2 -levent_openssl -levent -lcrypto -lssl -lpthread -lz -lpcre
Neural Magic DeepSparse Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.3.2 Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream a n c 1.0242 2.0484 3.0726 4.0968 5.121 4.5104 4.5519 4.5475
Neural Magic DeepSparse Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.3.2 Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream a n c 300 600 900 1200 1500 1534.45 1508.55 1520.54
Neural Magic DeepSparse Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.3.2 Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream a n c 0.9541 1.9082 2.8623 3.8164 4.7705 4.2064 4.2403 4.1882
Neural Magic DeepSparse Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.3.2 Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream a n c 50 100 150 200 250 237.73 235.82 238.76
Neural Magic DeepSparse Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.3.2 Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream a n c 15 30 45 60 75 67.19 66.91 67.14
Neural Magic DeepSparse Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.3.2 Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream a n c 20 40 60 80 100 104.10 104.53 104.02
Neural Magic DeepSparse Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.3.2 Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-Stream a n c 10 20 30 40 50 44.09 44.31 44.24
Neural Magic DeepSparse Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.3.2 Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-Stream a n c 5 10 15 20 25 22.67 22.56 22.59
Neural Magic DeepSparse Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.3.2 Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream a n c 5 10 15 20 25 19.78 18.60 18.83
Neural Magic DeepSparse Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.3.2 Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream a n c 80 160 240 320 400 351.50 374.06 367.85
Neural Magic DeepSparse Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.3.2 Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream a n c 4 8 12 16 20 14.83 14.72 14.87
Neural Magic DeepSparse Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.3.2 Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream a n c 15 30 45 60 75 67.41 67.93 67.25
Neural Magic DeepSparse Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.3.2 Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream a n c 7 14 21 28 35 28.03 28.42 28.17
Neural Magic DeepSparse Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.3.2 Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream a n c 50 100 150 200 250 248.43 244.89 247.38
Neural Magic DeepSparse Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.3.2 Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream a n c 6 12 18 24 30 23.45 23.19 23.20
Neural Magic DeepSparse Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.3.2 Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream a n c 10 20 30 40 50 42.62 43.11 43.08
Neural Magic DeepSparse Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.3.2 Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream a n c 15 30 45 60 75 67.19 60.93 62.17
Neural Magic DeepSparse Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.3.2 Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream a n c 30 60 90 120 150 103.93 114.78 112.44
Neural Magic DeepSparse Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.3.2 Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream a n c 11 22 33 44 55 50.53 43.66 43.45
Neural Magic DeepSparse Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.3.2 Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream a n c 6 12 18 24 30 19.78 22.90 23.01
Neural Magic DeepSparse Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.3.2 Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream a n c 10 20 30 40 50 43.45 41.00 40.82
Neural Magic DeepSparse Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.3.2 Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream a n c 40 80 120 160 200 160.84 170.19 171.25
Neural Magic DeepSparse Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.3.2 Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream a n c 8 16 24 32 40 33.35 33.13 33.22
Neural Magic DeepSparse Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.3.2 Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream a n c 7 14 21 28 35 29.98 30.17 30.10
Neural Magic DeepSparse Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.3.2 Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream a n c 2 4 6 8 10 6.3049 6.1199 5.7934
Neural Magic DeepSparse Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.3.2 Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream a n c 300 600 900 1200 1500 1102.50 1137.90 1169.90
Neural Magic DeepSparse Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.3.2 Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream a n c 1.2887 2.5774 3.8661 5.1548 6.4435 5.7188 5.6899 5.7275
Neural Magic DeepSparse Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.3.2 Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream a n c 40 80 120 160 200 174.84 175.73 174.58
Neural Magic DeepSparse Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.3.2 Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream a n c 5 10 15 20 25 19.02 19.52 19.18
Neural Magic DeepSparse Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.3.2 Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream a n c 80 160 240 320 400 365.11 357.48 361.73
Neural Magic DeepSparse Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.3.2 Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream a n c 4 8 12 16 20 16.16 16.24 16.20
Neural Magic DeepSparse Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.3.2 Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream a n c 14 28 42 56 70 61.89 61.59 61.74
Neural Magic DeepSparse Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.3.2 Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream a n c 1.0137 2.0274 3.0411 4.0548 5.0685 4.5054 4.4135 4.4061
Neural Magic DeepSparse Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.3.2 Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream a n c 300 600 900 1200 1500 1545.76 1523.22 1529.39
Neural Magic DeepSparse Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.3.2 Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream a n c 0.9407 1.8814 2.8221 3.7628 4.7035 4.1644 4.1721 4.1810
Neural Magic DeepSparse Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.3.2 Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream a n c 50 100 150 200 250 240.13 239.68 239.17
Phoronix Test Suite v10.8.4