7763 2204

AMD EPYC 7763 64-Core testing with a AMD DAYTONA_X (RYM1009B BIOS) and ASPEED on Ubuntu 22.04 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 2308059-NE-77632204529
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
Show Result Confidence Charts

Limit displaying results to tests within:

CPU Massive 3 Tests
Creator Workloads 3 Tests
Database Test Suite 3 Tests
HPC - High Performance Computing 2 Tests
Machine Learning 2 Tests
Multi-Core 4 Tests
NVIDIA GPU Compute 2 Tests
Server 3 Tests
Server CPU Tests 2 Tests
Common Workstation Benchmarks 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
August 04 2023
  6 Hours, 9 Minutes
b
August 04 2023
  4 Hours, 37 Minutes
c
August 05 2023
  4 Hours, 37 Minutes
Invert Hiding All Results Option
  5 Hours, 7 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):

a

Kernel Notes: Transparent Huge Pages: madvise
Compiler Notes: --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-bootstrap --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,brig,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-link-serialization=2 --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-targets=nvptx-none=/build/gcc-11-xKiWfi/gcc-11-11.3.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-11-xKiWfi/gcc-11-11.3.0/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-build-config=bootstrap-lto-lean --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 Notes: Scaling Governor: acpi-cpufreq performance (Boost: Enabled) - CPU Microcode: 0xa001173
Java Notes: OpenJDK Runtime Environment (build 11.0.20+8-post-Ubuntu-1ubuntu122.04)
Python Notes: Python 3.10.6
Security Notes: 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 Retpolines IBPB: conditional IBRS_FW STIBP: always-on RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected

b

c

Processor: AMD EPYC 7763 64-Core @ 2.45GHz (64 Cores / 128 Threads), Motherboard: AMD DAYTONA_X (RYM1009B BIOS), Chipset: AMD Starship/Matisse, Memory: 256GB, Disk: 800GB INTEL SSDPF21Q800GB, Graphics: ASPEED, Monitor: VE228, Network: 2 x Mellanox MT27710

OS: Ubuntu 22.04, Kernel: 6.2.0-phx (x86_64), Desktop: GNOME Shell 42.5, Display Server: X Server 1.21.1.3, Vulkan: 1.3.224, Compiler: GCC 11.3.0 + LLVM 14.0.0, File-System: ext4, Screen Resolution: 1920x1080

7763 2204OpenBenchmarking.orgPhoronix Test SuiteAMD EPYC 7763 64-Core @ 2.45GHz (64 Cores / 128 Threads)AMD DAYTONA_X (RYM1009B BIOS)AMD Starship/Matisse256GB800GB INTEL SSDPF21Q800GBASPEEDVE2282 x Mellanox MT27710Ubuntu 22.046.2.0-phx (x86_64)GNOME Shell 42.5X Server 1.21.1.31.3.224GCC 11.3.0 + LLVM 14.0.0ext41920x1080ProcessorMotherboardChipsetMemoryDiskGraphicsMonitorNetworkOSKernelDesktopDisplay ServerVulkanCompilerFile-SystemScreen Resolution7763 2204 BenchmarksSystem Logs- Transparent Huge Pages: madvise- --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-bootstrap --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,brig,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-link-serialization=2 --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-targets=nvptx-none=/build/gcc-11-xKiWfi/gcc-11-11.3.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-11-xKiWfi/gcc-11-11.3.0/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-build-config=bootstrap-lto-lean --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 - Scaling Governor: acpi-cpufreq performance (Boost: Enabled) - CPU Microcode: 0xa001173 - OpenJDK Runtime Environment (build 11.0.20+8-post-Ubuntu-1ubuntu122.04)- Python 3.10.6- 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 Retpolines IBPB: conditional IBRS_FW STIBP: always-on RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected

abcResult OverviewPhoronix Test Suite100%101%103%104%105%NCNNsrsRAN ProjectApache CassandraBRL-CADVVenCApache IoTDBBlenderNeural Magic DeepSparseTimed GCC Compilation

7763 2204couchdb: 500 - 3000 - 30build-gcc: Time To Compilebrl-cad: VGR Performance Metriccouchdb: 300 - 3000 - 30couchdb: 100 - 3000 - 30couchdb: 500 - 1000 - 30blender: Barbershop - CPU-Onlycouchdb: 300 - 1000 - 30deepsparse: BERT-Large, NLP Question Answering - Asynchronous Multi-Streamdeepsparse: BERT-Large, NLP Question Answering - Asynchronous Multi-Streamcassandra: Writesdeepsparse: BERT-Large, NLP Question Answering, Sparse INT8 - Synchronous Single-Streamdeepsparse: BERT-Large, NLP Question Answering, Sparse INT8 - Synchronous Single-Streamdeepsparse: BERT-Large, NLP Question Answering - Synchronous Single-Streamdeepsparse: BERT-Large, NLP Question Answering - Synchronous Single-Streamvvenc: Bosphorus 4K - Fastcouchdb: 100 - 1000 - 30ncnn: CPU - FastestDetncnn: CPU - vision_transformerncnn: CPU - regnety_400mncnn: CPU - squeezenet_ssdncnn: CPU - yolov4-tinyncnn: CPU - resnet50ncnn: CPU - alexnetncnn: CPU - resnet18ncnn: CPU - vgg16ncnn: CPU - googlenetncnn: CPU - blazefacencnn: CPU - efficientnet-b0ncnn: CPU - mnasnetncnn: CPU - shufflenet-v2ncnn: CPU-v3-v3 - mobilenet-v3ncnn: CPU-v2-v2 - mobilenet-v2ncnn: CPU - mobilenetdeepsparse: BERT-Large, NLP Question Answering, Sparse INT8 - Asynchronous Multi-Streamdeepsparse: BERT-Large, NLP Question Answering, Sparse INT8 - Asynchronous Multi-Streamblender: Pabellon Barcelona - CPU-Onlyapache-iotdb: 500 - 100 - 500apache-iotdb: 500 - 100 - 500blender: Classroom - CPU-Onlydeepsparse: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Synchronous Single-Streamdeepsparse: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Synchronous Single-Streamdeepsparse: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Asynchronous Multi-Streamdeepsparse: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Asynchronous Multi-Streamvvenc: Bosphorus 4K - Fasterdeepsparse: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Asynchronous Multi-Streamdeepsparse: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Asynchronous Multi-Streamdeepsparse: NLP Text Classification, BERT base uncased SST2 - Asynchronous Multi-Streamdeepsparse: NLP Text Classification, BERT base uncased SST2 - Asynchronous Multi-Streamdeepsparse: CV Segmentation, 90% Pruned YOLACT Pruned - Asynchronous Multi-Streamdeepsparse: CV Segmentation, 90% Pruned YOLACT Pruned - Asynchronous Multi-Streamdeepsparse: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Asynchronous Multi-Streamdeepsparse: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Asynchronous Multi-Streamdeepsparse: NLP Document Classification, oBERT base uncased on IMDB - Asynchronous Multi-Streamdeepsparse: NLP Document Classification, oBERT base uncased on IMDB - Asynchronous Multi-Streamdeepsparse: NLP Token Classification, BERT base uncased conll2003 - Asynchronous Multi-Streamdeepsparse: NLP Token Classification, BERT base uncased conll2003 - Asynchronous Multi-Streamdeepsparse: NLP Text Classification, BERT base uncased SST2 - Synchronous Single-Streamdeepsparse: NLP Text Classification, BERT base uncased SST2 - Synchronous Single-Streamdeepsparse: NLP Document Classification, oBERT base uncased on IMDB - Synchronous Single-Streamdeepsparse: NLP Document Classification, oBERT base uncased on IMDB - Synchronous Single-Streamdeepsparse: NLP Token Classification, BERT base uncased conll2003 - Synchronous Single-Streamdeepsparse: NLP Token Classification, BERT base uncased conll2003 - Synchronous Single-Streamdeepsparse: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Synchronous Single-Streamdeepsparse: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Synchronous Single-Streamdeepsparse: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Synchronous Single-Streamdeepsparse: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Synchronous Single-Streamapache-iotdb: 200 - 100 - 500apache-iotdb: 200 - 100 - 500deepsparse: CV Segmentation, 90% Pruned YOLACT Pruned - Synchronous Single-Streamdeepsparse: CV Segmentation, 90% Pruned YOLACT Pruned - Synchronous Single-Streamdeepsparse: NLP Text Classification, DistilBERT mnli - Asynchronous Multi-Streamdeepsparse: NLP Text Classification, DistilBERT mnli - Asynchronous Multi-Streamapache-iotdb: 500 - 100 - 200apache-iotdb: 500 - 100 - 200srsran: PUSCH Processor Benchmark, Throughput Totaldeepsparse: NLP Text Classification, DistilBERT mnli - Synchronous Single-Streamdeepsparse: NLP Text Classification, DistilBERT mnli - Synchronous Single-Streamvvenc: Bosphorus 1080p - Fastdeepsparse: CV Detection, YOLOv5s COCO, Sparse INT8 - Asynchronous Multi-Streamdeepsparse: CV Detection, YOLOv5s COCO, Sparse INT8 - Asynchronous Multi-Streamdeepsparse: CV Detection, YOLOv5s COCO - Asynchronous Multi-Streamdeepsparse: CV Detection, YOLOv5s COCO - Asynchronous Multi-Streamdeepsparse: ResNet-50, Sparse INT8 - Synchronous Single-Streamdeepsparse: ResNet-50, Sparse INT8 - Synchronous Single-Streamdeepsparse: CV Detection, YOLOv5s COCO - Synchronous Single-Streamdeepsparse: CV Detection, YOLOv5s COCO - Synchronous Single-Streamdeepsparse: CV Detection, YOLOv5s COCO, Sparse INT8 - Synchronous Single-Streamdeepsparse: CV Detection, YOLOv5s COCO, Sparse INT8 - Synchronous Single-Streamdeepsparse: ResNet-50, Baseline - Asynchronous Multi-Streamdeepsparse: ResNet-50, Baseline - Asynchronous Multi-Streamdeepsparse: CV Classification, ResNet-50 ImageNet - Asynchronous Multi-Streamdeepsparse: CV Classification, ResNet-50 ImageNet - Asynchronous Multi-Streamdeepsparse: ResNet-50, Baseline - Synchronous Single-Streamdeepsparse: ResNet-50, Baseline - Synchronous Single-Streamdeepsparse: CV Classification, ResNet-50 ImageNet - Synchronous Single-Streamdeepsparse: CV Classification, ResNet-50 ImageNet - Synchronous Single-Streamdeepsparse: ResNet-50, Sparse INT8 - Asynchronous Multi-Streamdeepsparse: ResNet-50, Sparse INT8 - Asynchronous Multi-Streamapache-iotdb: 500 - 1 - 500apache-iotdb: 500 - 1 - 500blender: Fishy Cat - CPU-Onlyblender: BMW27 - CPU-Onlyapache-iotdb: 100 - 100 - 500apache-iotdb: 100 - 100 - 500apache-iotdb: 200 - 100 - 200apache-iotdb: 200 - 100 - 200apache-iotdb: 200 - 1 - 500apache-iotdb: 200 - 1 - 500apache-iotdb: 500 - 1 - 200apache-iotdb: 500 - 1 - 200vvenc: Bosphorus 1080p - Fastersrsran: Downlink Processor Benchmarkapache-iotdb: 100 - 100 - 200apache-iotdb: 100 - 100 - 200apache-iotdb: 100 - 1 - 500apache-iotdb: 100 - 1 - 500apache-iotdb: 200 - 1 - 200apache-iotdb: 200 - 1 - 200apache-iotdb: 100 - 1 - 200apache-iotdb: 100 - 1 - 200srsran: PUSCH Processor Benchmark, Throughput Threadabc2390.9331020.133734386572.125346.085339.967253.49169.505681.275646.720423665010.577094.509740.904824.44225.991101.57810.2548.7935.2414.1720.6615.495.238.5023.8414.623.979.986.099.097.006.3514.1155.5817574.955084.5581.8156935634.5568.805.747173.9462223.4715143.072510.64665.2750489.7709192.3546165.9980597.967953.483128.91501105.3791840.541637.6144841.477937.610918.585853.784550.125819.946949.813220.072025.35639.421911.537886.6104109.3842048733.2234.910428.631097.8711326.312335.0551341708.859682.110.264597.356116.083140.3979227.4188141.7036225.31041.3784723.71498.3423119.80418.3056120.364568.2852468.138368.3188467.98486.2525159.84926.2481159.96608.36653814.519427.11636128.7333.7027.2781.251316464.4435.0946437377.6733.51232509.1913.541182440.6229.352657.736.0439287432.9234.361038515.6215.24898967.0817.45644019.72211.11020.846729876253.77679.824046.573923816110.629994.044540.699324.56555.9939.0448.4927.5414.1120.5115.345.228.4223.6414.533.439.785.97.936.556.2613.9755.5601575.285984.3579.8359505306.5568.505.7863172.7505223.3670143.205810.81565.7359486.1728192.1645166.2231596.809153.553128.94511104.0377840.949337.5315840.257337.535218.614453.702349.922620.027949.914420.031525.265939.563111.566686.3905110.8841987111.3934.908228.632997.8434326.543936.150045888.989718.610.238097.612116.090140.2607227.6423141.6185225.45921.3623732.11018.3368119.88188.3182120.176268.2763467.969668.2716468.10876.2566159.74816.2495159.92388.34413824.3045261686943.1633.7627.582.2650507747.1233.8447245476.7833.861226219.8811.631365831.529.39658.136.8538401769.1732.921069145.7913.6978176.7617.28648308.27208.21020.216730434253.43679.811846.902123488710.572094.554840.760624.52845.9768.8848.4327.5914.5920.8015.545.228.5123.9114.473.489.755.867.606.346.1714.0355.5835575.115884.1783.1456463717.5468.705.8092172.0659222.8175143.571410.81866.2837482.1274192.2148166.058596.534353.616628.96441103.3552840.123437.5814840.435037.575518.52853.95249.931220.024549.858020.053625.298939.511711.576486.3219106.7343363203.7634.897828.641697.8698326.410837.1249201448.819727.110.314696.882216.055140.3036227.5740141.4775225.80071.3634731.50998.3300119.97908.2903120.581368.2503468.329368.3388467.63966.2238160.59326.2195160.69858.34683823.083331.41446487.733.7227.2479.1652464142.8334.7946674344.6932.711261385.8911.831367763.4929.469619.335.0139945212.9934.081044153.4416.07870795.9216.35667880.96210.8OpenBenchmarking.org

Apache CouchDB

This is a bulk insertion benchmark of Apache CouchDB. CouchDB is a document-oriented NoSQL database implemented in Erlang. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterApache CouchDB 3.3.2Bulk Size: 500 - Inserts: 3000 - Rounds: 30a50010001500200025002390.931. (CXX) g++ options: -std=c++17 -lmozjs-78 -lm -lei -fPIC -MMD

Timed GCC Compilation

This test times how long it takes to build the GNU Compiler Collection (GCC) open-source compiler. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed GCC Compilation 13.2Time To Compileabc2004006008001000SE +/- 1.81, N = 2SE +/- 0.03, N = 2SE +/- 0.66, N = 21020.131020.851020.22

BRL-CAD

BRL-CAD is a cross-platform, open-source solid modeling system with built-in benchmark mode. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgVGR Performance Metric, More Is BetterBRL-CAD 7.36VGR Performance Metricabc160K320K480K640K800KSE +/- 1805.50, N = 2SE +/- 357.50, N = 2SE +/- 963.50, N = 27343867298767304341. (CXX) g++ options: -std=c++14 -pipe -fvisibility=hidden -fno-strict-aliasing -fno-common -fexceptions -ftemplate-depth-128 -m64 -ggdb3 -O3 -fipa-pta -fstrength-reduce -finline-functions -flto -ltcl8.6 -lregex_brl -lz_brl -lnetpbm -ldl -lm -ltk8.6

Apache CouchDB

This is a bulk insertion benchmark of Apache CouchDB. CouchDB is a document-oriented NoSQL database implemented in Erlang. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterApache CouchDB 3.3.2Bulk Size: 300 - Inserts: 3000 - Rounds: 30a120240360480600SE +/- 0.52, N = 2572.131. (CXX) g++ options: -std=c++17 -lmozjs-78 -lm -lei -fPIC -MMD

OpenBenchmarking.orgSeconds, Fewer Is BetterApache CouchDB 3.3.2Bulk Size: 100 - Inserts: 3000 - Rounds: 30a80160240320400SE +/- 0.25, N = 2346.091. (CXX) g++ options: -std=c++17 -lmozjs-78 -lm -lei -fPIC -MMD

OpenBenchmarking.orgSeconds, Fewer Is BetterApache CouchDB 3.3.2Bulk Size: 500 - Inserts: 1000 - Rounds: 30a70140210280350SE +/- 8.78, N = 2339.971. (CXX) g++ options: -std=c++17 -lmozjs-78 -lm -lei -fPIC -MMD

Blender

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 3.6Blend File: Barbershop - Compute: CPU-Onlyabc60120180240300SE +/- 0.11, N = 2SE +/- 0.23, N = 2SE +/- 0.52, N = 2253.49253.77253.43

Apache CouchDB

This is a bulk insertion benchmark of Apache CouchDB. CouchDB is a document-oriented NoSQL database implemented in Erlang. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterApache CouchDB 3.3.2Bulk Size: 300 - Inserts: 1000 - Rounds: 30a4080120160200SE +/- 0.64, N = 2169.511. (CXX) g++ options: -std=c++17 -lmozjs-78 -lm -lei -fPIC -MMD

Neural Magic DeepSparse

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Streamabc150300450600750SE +/- 0.37, N = 2SE +/- 0.17, N = 2SE +/- 0.34, N = 2681.28679.82679.81

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Streamabc1122334455SE +/- 0.10, N = 2SE +/- 0.03, N = 2SE +/- 0.02, N = 246.7246.5746.90

Apache Cassandra

This is a benchmark of the Apache Cassandra NoSQL database management system making use of cassandra-stress. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgOp/s, More Is BetterApache Cassandra 4.1.3Test: Writesabc50K100K150K200K250KSE +/- 633.50, N = 2SE +/- 817.50, N = 2SE +/- 669.00, N = 2236650238161234887

Neural Magic DeepSparse

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Streamabc3691215SE +/- 0.01, N = 2SE +/- 0.07, N = 2SE +/- 0.02, N = 210.5810.6310.57

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Streamabc20406080100SE +/- 0.05, N = 2SE +/- 0.64, N = 2SE +/- 0.18, N = 294.5194.0494.55

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Streamabc918273645SE +/- 0.01, N = 2SE +/- 0.01, N = 2SE +/- 0.01, N = 240.9040.7040.76

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Streamabc612182430SE +/- 0.01, N = 2SE +/- 0.01, N = 2SE +/- 0.00, N = 224.4424.5724.53

VVenC

VVenC is the Fraunhofer Versatile Video Encoder as a fast/efficient H.266/VVC encoder. The vvenc encoder makes use of SIMD Everywhere (SIMDe). The vvenc software is published under the Clear BSD License. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFrames Per Second, More Is BetterVVenC 1.9Video Input: Bosphorus 4K - Video Preset: Fastabc1.34842.69684.04525.39366.742SE +/- 0.001, N = 2SE +/- 0.006, N = 2SE +/- 0.002, N = 25.9915.9935.9761. (CXX) g++ options: -O3 -flto -fno-fat-lto-objects -flto=auto

Apache CouchDB

This is a bulk insertion benchmark of Apache CouchDB. CouchDB is a document-oriented NoSQL database implemented in Erlang. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterApache CouchDB 3.3.2Bulk Size: 100 - Inserts: 1000 - Rounds: 30a20406080100SE +/- 0.50, N = 2101.581. (CXX) g++ options: -std=c++17 -lmozjs-78 -lm -lei -fPIC -MMD

NCNN

NCNN is a high performance neural network inference framework optimized for mobile and other platforms developed by Tencent. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: FastestDetabc3691215SE +/- 0.94, N = 2SE +/- 0.11, N = 2SE +/- 0.01, N = 210.259.048.88MIN: 8.95 / MAX: 17.14MIN: 8.65 / MAX: 15.19MIN: 8.58 / MAX: 13.341. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: vision_transformerabc1122334455SE +/- 0.07, N = 2SE +/- 0.04, N = 2SE +/- 0.30, N = 248.7948.4948.43MIN: 47.65 / MAX: 78.36MIN: 47.44 / MAX: 58.53MIN: 47.33 / MAX: 85.351. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: regnety_400mabc816243240SE +/- 5.99, N = 2SE +/- 0.15, N = 2SE +/- 0.07, N = 235.2427.5427.59MIN: 27.86 / MAX: 47.9MIN: 26.96 / MAX: 33.56MIN: 26.64 / MAX: 33.411. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: squeezenet_ssdabc48121620SE +/- 0.01, N = 2SE +/- 0.09, N = 2SE +/- 0.62, N = 214.1714.1114.59MIN: 13.53 / MAX: 18.45MIN: 13.32 / MAX: 18.63MIN: 13.37 / MAX: 277.611. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: yolov4-tinyabc510152025SE +/- 0.02, N = 2SE +/- 0.08, N = 2SE +/- 0.13, N = 220.6620.5120.80MIN: 20.04 / MAX: 25.04MIN: 19.87 / MAX: 24.86MIN: 20.01 / MAX: 96.451. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: resnet50abc48121620SE +/- 0.08, N = 2SE +/- 0.10, N = 2SE +/- 0.14, N = 215.4915.3415.54MIN: 15.24 / MAX: 21.82MIN: 15.07 / MAX: 21.69MIN: 15.15 / MAX: 27.21. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: alexnetabc1.17682.35363.53044.70725.884SE +/- 0.01, N = 2SE +/- 0.02, N = 2SE +/- 0.02, N = 25.235.225.22MIN: 5.12 / MAX: 11.62MIN: 5.11 / MAX: 5.77MIN: 5.12 / MAX: 7.841. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: resnet18abc246810SE +/- 0.03, N = 2SE +/- 0.04, N = 2SE +/- 0.03, N = 28.508.428.51MIN: 8.33 / MAX: 14.69MIN: 8.27 / MAX: 14.6MIN: 8.3 / MAX: 13.611. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: vgg16abc612182430SE +/- 0.05, N = 2SE +/- 0.06, N = 2SE +/- 0.08, N = 223.8423.6423.91MIN: 23.45 / MAX: 28.55MIN: 23.33 / MAX: 28.05MIN: 23.48 / MAX: 30.631. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: googlenetabc48121620SE +/- 0.03, N = 2SE +/- 0.02, N = 2SE +/- 0.06, N = 214.6214.5314.47MIN: 14.46 / MAX: 25.51MIN: 14.31 / MAX: 24.11MIN: 14.26 / MAX: 20.571. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: blazefaceabc0.89331.78662.67993.57324.4665SE +/- 0.09, N = 2SE +/- 0.01, N = 2SE +/- 0.05, N = 23.973.433.48MIN: 3.5 / MAX: 7.61MIN: 3.35 / MAX: 3.83MIN: 3.32 / MAX: 8.581. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: efficientnet-b0abc3691215SE +/- 0.03, N = 2SE +/- 0.01, N = 2SE +/- 0.04, N = 29.989.789.75MIN: 9.82 / MAX: 10.96MIN: 9.64 / MAX: 16.01MIN: 9.58 / MAX: 13.381. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: mnasnetabc246810SE +/- 0.11, N = 2SE +/- 0.00, N = 2SE +/- 0.05, N = 26.095.905.86MIN: 5.89 / MAX: 10.35MIN: 5.81 / MAX: 12.33MIN: 5.73 / MAX: 11.671. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: shufflenet-v2abc3691215SE +/- 1.23, N = 2SE +/- 0.31, N = 2SE +/- 0.04, N = 29.097.937.60MIN: 7.74 / MAX: 15.92MIN: 7.51 / MAX: 11.45MIN: 7.44 / MAX: 11.61. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU-v3-v3 - Model: mobilenet-v3abc246810SE +/- 0.55, N = 2SE +/- 0.18, N = 2SE +/- 0.04, N = 27.006.556.34MIN: 6.3 / MAX: 10.2MIN: 6.24 / MAX: 7.61MIN: 6.15 / MAX: 11.571. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU-v2-v2 - Model: mobilenet-v2abc246810SE +/- 0.01, N = 2SE +/- 0.00, N = 2SE +/- 0.07, N = 26.356.266.17MIN: 6.19 / MAX: 12.45MIN: 6.11 / MAX: 12.76MIN: 6.02 / MAX: 6.831. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: mobilenetabc48121620SE +/- 0.04, N = 2SE +/- 0.03, N = 2SE +/- 0.09, N = 214.1113.9714.03MIN: 13.76 / MAX: 19.75MIN: 13.64 / MAX: 19.68MIN: 13.68 / MAX: 18.141. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

Neural Magic DeepSparse

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Streamabc1224364860SE +/- 0.03, N = 2SE +/- 0.06, N = 2SE +/- 0.04, N = 255.5855.5655.58

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Streamabc120240360480600SE +/- 0.03, N = 2SE +/- 0.68, N = 2SE +/- 0.31, N = 2574.96575.29575.12

Blender

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 3.6Blend File: Pabellon Barcelona - Compute: CPU-Onlyabc20406080100SE +/- 0.40, N = 2SE +/- 0.14, N = 2SE +/- 0.04, N = 284.5584.3584.17

Apache IoTDB

OpenBenchmarking.orgAverage Latency, More Is BetterApache IoTDB 1.1.2Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 500abc2040608010081.8179.8383.14MAX: 3018.16MAX: 1607.86MAX: 2932.1

OpenBenchmarking.orgpoint/sec, More Is BetterApache IoTDB 1.1.2Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 500abc13M26M39M52M65M56935634.5559505306.5556463717.54

Blender

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 3.6Blend File: Classroom - Compute: CPU-Onlyabc1530456075SE +/- 0.14, N = 2SE +/- 0.03, N = 2SE +/- 0.13, N = 268.8068.5068.70

Neural Magic DeepSparse

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Streamabc1.30712.61423.92135.22846.5355SE +/- 0.0325, N = 2SE +/- 0.0212, N = 2SE +/- 0.0271, N = 25.74705.78635.8092

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Streamabc4080120160200SE +/- 0.98, N = 2SE +/- 0.63, N = 2SE +/- 0.80, N = 2173.95172.75172.07

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Streamabc50100150200250SE +/- 0.03, N = 2SE +/- 0.26, N = 2SE +/- 0.05, N = 2223.47223.37222.82

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Streamabc306090120150SE +/- 0.01, N = 2SE +/- 0.18, N = 2SE +/- 0.03, N = 2143.07143.21143.57

VVenC

VVenC is the Fraunhofer Versatile Video Encoder as a fast/efficient H.266/VVC encoder. The vvenc encoder makes use of SIMD Everywhere (SIMDe). The vvenc software is published under the Clear BSD License. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFrames Per Second, More Is BetterVVenC 1.9Video Input: Bosphorus 4K - Video Preset: Fasterabc3691215SE +/- 0.18, N = 2SE +/- 0.02, N = 2SE +/- 0.00, N = 210.6510.8210.821. (CXX) g++ options: -O3 -flto -fno-fat-lto-objects -flto=auto

Neural Magic DeepSparse

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Streamabc1530456075SE +/- 0.10, N = 2SE +/- 0.10, N = 2SE +/- 0.99, N = 265.2865.7466.28

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Streamabc110220330440550SE +/- 0.68, N = 2SE +/- 1.03, N = 2SE +/- 7.21, N = 2489.77486.17482.13

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Streamabc4080120160200SE +/- 0.06, N = 2SE +/- 0.00, N = 2SE +/- 0.21, N = 2192.35192.16192.21

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Streamabc4080120160200SE +/- 0.05, N = 2SE +/- 0.06, N = 2SE +/- 0.08, N = 2166.00166.22166.06

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Streamabc130260390520650SE +/- 0.05, N = 2SE +/- 0.12, N = 2SE +/- 0.01, N = 2597.97596.81596.53

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Streamabc1224364860SE +/- 0.00, N = 2SE +/- 0.05, N = 2SE +/- 0.00, N = 253.4853.5553.62

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Streamabc714212835SE +/- 0.03, N = 2SE +/- 0.03, N = 2SE +/- 0.01, N = 228.9228.9528.96

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Streamabc2004006008001000SE +/- 1.07, N = 2SE +/- 1.15, N = 2SE +/- 0.21, N = 21105.381104.041103.36

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Streamabc2004006008001000SE +/- 1.02, N = 2SE +/- 0.16, N = 2SE +/- 0.59, N = 2840.54840.95840.12

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Streamabc918273645SE +/- 0.09, N = 2SE +/- 0.01, N = 2SE +/- 0.02, N = 237.6137.5337.58

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Streamabc2004006008001000SE +/- 0.49, N = 2SE +/- 0.01, N = 2SE +/- 0.44, N = 2841.48840.26840.44

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Streamabc918273645SE +/- 0.04, N = 2SE +/- 0.01, N = 2SE +/- 0.02, N = 237.6137.5437.58

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Streamabc510152025SE +/- 0.03, N = 2SE +/- 0.00, N = 2SE +/- 0.03, N = 218.5918.6118.53

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Streamabc1224364860SE +/- 0.08, N = 2SE +/- 0.01, N = 2SE +/- 0.10, N = 253.7853.7053.95

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Streamabc1122334455SE +/- 0.02, N = 2SE +/- 0.11, N = 2SE +/- 0.05, N = 250.1349.9249.93

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Streamabc510152025SE +/- 0.01, N = 2SE +/- 0.04, N = 2SE +/- 0.02, N = 219.9520.0320.02

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Streamabc1122334455SE +/- 0.05, N = 2SE +/- 0.00, N = 2SE +/- 0.06, N = 249.8149.9149.86

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Streamabc510152025SE +/- 0.02, N = 2SE +/- 0.00, N = 2SE +/- 0.02, N = 220.0720.0320.05

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Streamabc612182430SE +/- 0.04, N = 2SE +/- 0.07, N = 2SE +/- 0.01, N = 225.3625.2725.30

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Streamabc918273645SE +/- 0.06, N = 2SE +/- 0.11, N = 2SE +/- 0.01, N = 239.4239.5639.51

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-Streamabc3691215SE +/- 0.08, N = 2SE +/- 0.01, N = 2SE +/- 0.06, N = 211.5411.5711.58

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-Streamabc20406080100SE +/- 0.59, N = 2SE +/- 0.08, N = 2SE +/- 0.42, N = 286.6186.3986.32

Apache IoTDB

OpenBenchmarking.orgAverage Latency, More Is BetterApache IoTDB 1.1.2Device Count: 200 - Batch Size Per Write: 100 - Sensor Count: 500abc20406080100109.38110.88106.73MAX: 3597.09MAX: 3569.78MAX: 3485.91

OpenBenchmarking.orgpoint/sec, More Is BetterApache IoTDB 1.1.2Device Count: 200 - Batch Size Per Write: 100 - Sensor Count: 500abc9M18M27M36M45M42048733.2241987111.3943363203.76

Neural Magic DeepSparse

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Streamabc816243240SE +/- 0.03, N = 2SE +/- 0.05, N = 2SE +/- 0.03, N = 234.9134.9134.90

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Streamabc714212835SE +/- 0.03, N = 2SE +/- 0.04, N = 2SE +/- 0.02, N = 228.6328.6328.64

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Streamabc20406080100SE +/- 0.00, N = 2SE +/- 0.15, N = 2SE +/- 0.16, N = 297.8797.8497.87

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Streamabc70140210280350SE +/- 0.12, N = 2SE +/- 0.48, N = 2SE +/- 0.49, N = 2326.31326.54326.41

Apache IoTDB

OpenBenchmarking.orgAverage Latency, More Is BetterApache IoTDB 1.1.2Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 200abc91827364535.0536.1037.12MAX: 2157.23MAX: 1990.15MAX: 2182.81

OpenBenchmarking.orgpoint/sec, More Is BetterApache IoTDB 1.1.2Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 200abc11M22M33M44M55M51341708.8550045888.9849201448.81

srsRAN Project

srsRAN Project is a complete ORAN-native 5G RAN solution created by Software Radio Systems (SRS). The srsRAN Project radio suite was formerly known as srsLTE and can be used for building your own software-defined radio (SDR) 4G/5G mobile network. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMbps, More Is BettersrsRAN Project 23.5Test: PUSCH Processor Benchmark, Throughput Totalabc2K4K6K8K10KSE +/- 13.30, N = 2SE +/- 44.35, N = 2SE +/- 56.45, N = 29682.19718.69727.11. (CXX) g++ options: -march=native -mfma -O3 -fno-trapping-math -fno-math-errno -lgtest

Neural Magic DeepSparse

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Streamabc3691215SE +/- 0.00, N = 2SE +/- 0.02, N = 2SE +/- 0.01, N = 210.2610.2410.31

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Streamabc20406080100SE +/- 0.04, N = 2SE +/- 0.17, N = 2SE +/- 0.08, N = 297.3697.6196.88

VVenC

VVenC is the Fraunhofer Versatile Video Encoder as a fast/efficient H.266/VVC encoder. The vvenc encoder makes use of SIMD Everywhere (SIMDe). The vvenc software is published under the Clear BSD License. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFrames Per Second, More Is BetterVVenC 1.9Video Input: Bosphorus 1080p - Video Preset: Fastabc48121620SE +/- 0.02, N = 2SE +/- 0.02, N = 2SE +/- 0.02, N = 216.0816.0916.061. (CXX) g++ options: -O3 -flto -fno-fat-lto-objects -flto=auto

Neural Magic DeepSparse

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Streamabc306090120150SE +/- 0.07, N = 2SE +/- 0.12, N = 2SE +/- 0.23, N = 2140.40140.26140.30

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Streamabc50100150200250SE +/- 0.11, N = 2SE +/- 0.19, N = 2SE +/- 0.22, N = 2227.42227.64227.57

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Streamabc306090120150SE +/- 0.13, N = 2SE +/- 0.09, N = 2SE +/- 0.05, N = 2141.70141.62141.48

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Streamabc50100150200250SE +/- 0.21, N = 2SE +/- 0.10, N = 2SE +/- 0.22, N = 2225.31225.46225.80

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Streamabc0.31010.62020.93031.24041.5505SE +/- 0.0205, N = 2SE +/- 0.0055, N = 2SE +/- 0.0010, N = 21.37841.36231.3634

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Streamabc160320480640800SE +/- 10.71, N = 2SE +/- 2.91, N = 2SE +/- 0.48, N = 2723.71732.11731.51

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Streamabc246810SE +/- 0.0056, N = 2SE +/- 0.0012, N = 2SE +/- 0.0003, N = 28.34238.33688.3300

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Streamabc306090120150SE +/- 0.08, N = 2SE +/- 0.02, N = 2SE +/- 0.01, N = 2119.80119.88119.98

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Streamabc246810SE +/- 0.0127, N = 2SE +/- 0.0014, N = 2SE +/- 0.0053, N = 28.30568.31828.2903

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Streamabc306090120150SE +/- 0.19, N = 2SE +/- 0.02, N = 2SE +/- 0.08, N = 2120.36120.18120.58

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Streamabc1530456075SE +/- 0.04, N = 2SE +/- 0.04, N = 2SE +/- 0.01, N = 268.2968.2868.25

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Streamabc100200300400500SE +/- 0.19, N = 2SE +/- 0.62, N = 2SE +/- 0.09, N = 2468.14467.97468.33

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Streamabc1530456075SE +/- 0.04, N = 2SE +/- 0.13, N = 2SE +/- 0.01, N = 268.3268.2768.34

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Streamabc100200300400500SE +/- 0.36, N = 2SE +/- 1.23, N = 2SE +/- 0.26, N = 2467.98468.11467.64

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: ResNet-50, Baseline - Scenario: Synchronous Single-Streamabc246810SE +/- 0.0166, N = 2SE +/- 0.0087, N = 2SE +/- 0.0308, N = 26.25256.25666.2238

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: ResNet-50, Baseline - Scenario: Synchronous Single-Streamabc4080120160200SE +/- 0.43, N = 2SE +/- 0.22, N = 2SE +/- 0.80, N = 2159.85159.75160.59

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Streamabc246810SE +/- 0.0097, N = 2SE +/- 0.0121, N = 2SE +/- 0.0244, N = 26.24816.24956.2195

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Streamabc4080120160200SE +/- 0.24, N = 2SE +/- 0.32, N = 2SE +/- 0.62, N = 2159.97159.92160.70

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Streamabc246810SE +/- 0.0013, N = 2SE +/- 0.0212, N = 2SE +/- 0.0366, N = 28.36658.34418.3468

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Streamabc8001600240032004000SE +/- 0.54, N = 2SE +/- 10.54, N = 2SE +/- 17.23, N = 23814.523824.303823.08

Apache IoTDB

OpenBenchmarking.orgAverage Latency, More Is BetterApache IoTDB 1.1.2Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 500abc71421283527.126.031.4MAX: 934.45MAX: 873.88MAX: 890.05

OpenBenchmarking.orgpoint/sec, More Is BetterApache IoTDB 1.1.2Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 500abc400K800K1200K1600K2000K1636128.731686943.161446487.70

Blender

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 3.6Blend File: Fishy Cat - Compute: CPU-Onlyabc816243240SE +/- 0.02, N = 2SE +/- 0.25, N = 2SE +/- 0.01, N = 233.7033.7633.72

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 3.6Blend File: BMW27 - Compute: CPU-Onlyabc612182430SE +/- 0.06, N = 2SE +/- 0.15, N = 2SE +/- 0.04, N = 227.2727.5027.24

Apache IoTDB

OpenBenchmarking.orgAverage Latency, More Is BetterApache IoTDB 1.1.2Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 500abc2040608010081.2082.2679.16MAX: 1009.28MAX: 864.29MAX: 1006.03

OpenBenchmarking.orgpoint/sec, More Is BetterApache IoTDB 1.1.2Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 500abc11M22M33M44M55M51316464.4450507747.1252464142.83

OpenBenchmarking.orgAverage Latency, More Is BetterApache IoTDB 1.1.2Device Count: 200 - Batch Size Per Write: 100 - Sensor Count: 200abc81624324035.0933.8434.79MAX: 804.64MAX: 773.52MAX: 780.01

OpenBenchmarking.orgpoint/sec, More Is BetterApache IoTDB 1.1.2Device Count: 200 - Batch Size Per Write: 100 - Sensor Count: 200abc10M20M30M40M50M46437377.6747245476.7846674344.69

OpenBenchmarking.orgAverage Latency, More Is BetterApache IoTDB 1.1.2Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 500abc81624324033.5033.8632.71MAX: 690.29MAX: 659.59MAX: 725.08

OpenBenchmarking.orgpoint/sec, More Is BetterApache IoTDB 1.1.2Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 500abc300K600K900K1200K1500K1232509.191226219.881261385.89

OpenBenchmarking.orgAverage Latency, More Is BetterApache IoTDB 1.1.2Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 200abc369121513.5411.6311.83MAX: 856.65MAX: 860.78MAX: 836.9

OpenBenchmarking.orgpoint/sec, More Is BetterApache IoTDB 1.1.2Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 200abc300K600K900K1200K1500K1182440.621365831.501367763.49

VVenC

VVenC is the Fraunhofer Versatile Video Encoder as a fast/efficient H.266/VVC encoder. The vvenc encoder makes use of SIMD Everywhere (SIMDe). The vvenc software is published under the Clear BSD License. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFrames Per Second, More Is BetterVVenC 1.9Video Input: Bosphorus 1080p - Video Preset: Fasterabc714212835SE +/- 0.07, N = 2SE +/- 0.11, N = 2SE +/- 0.07, N = 229.3529.3929.471. (CXX) g++ options: -O3 -flto -fno-fat-lto-objects -flto=auto

srsRAN Project

srsRAN Project is a complete ORAN-native 5G RAN solution created by Software Radio Systems (SRS). The srsRAN Project radio suite was formerly known as srsLTE and can be used for building your own software-defined radio (SDR) 4G/5G mobile network. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMbps, More Is BettersrsRAN Project 23.5Test: Downlink Processor Benchmarkabc140280420560700SE +/- 17.85, N = 2SE +/- 27.75, N = 2SE +/- 0.35, N = 2657.7658.1619.31. (CXX) g++ options: -march=native -mfma -O3 -fno-trapping-math -fno-math-errno -lgtest

Apache IoTDB

OpenBenchmarking.orgAverage Latency, More Is BetterApache IoTDB 1.1.2Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 200abc81624324036.0436.8535.01MAX: 804.01MAX: 721.27MAX: 746.4

OpenBenchmarking.orgpoint/sec, More Is BetterApache IoTDB 1.1.2Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 200abc9M18M27M36M45M39287432.9238401769.1739945212.99

OpenBenchmarking.orgAverage Latency, More Is BetterApache IoTDB 1.1.2Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 500abc81624324034.3632.9234.08MAX: 704.53MAX: 728.63MAX: 699.28

OpenBenchmarking.orgpoint/sec, More Is BetterApache IoTDB 1.1.2Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 500abc200K400K600K800K1000K1038515.621069145.791044153.44

OpenBenchmarking.orgAverage Latency, More Is BetterApache IoTDB 1.1.2Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 200abc4812162015.2413.6016.07MAX: 583.94MAX: 586.94MAX: 592.48

OpenBenchmarking.orgpoint/sec, More Is BetterApache IoTDB 1.1.2Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 200abc200K400K600K800K1000K898967.08978176.76870795.92

OpenBenchmarking.orgAverage Latency, More Is BetterApache IoTDB 1.1.2Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 200abc4812162017.4517.2816.35MAX: 645.35MAX: 644.33MAX: 668.86

OpenBenchmarking.orgpoint/sec, More Is BetterApache IoTDB 1.1.2Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 200abc140K280K420K560K700K644019.72648308.27667880.96

srsRAN Project

srsRAN Project is a complete ORAN-native 5G RAN solution created by Software Radio Systems (SRS). The srsRAN Project radio suite was formerly known as srsLTE and can be used for building your own software-defined radio (SDR) 4G/5G mobile network. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMbps, More Is BettersrsRAN Project 23.5Test: PUSCH Processor Benchmark, Throughput Threadabc50100150200250SE +/- 0.10, N = 2SE +/- 1.90, N = 2SE +/- 0.20, N = 2211.1208.2210.81. (CXX) g++ options: -march=native -mfma -O3 -fno-trapping-math -fno-math-errno -lgtest

122 Results Shown

Apache CouchDB
Timed GCC Compilation
BRL-CAD
Apache CouchDB:
  300 - 3000 - 30
  100 - 3000 - 30
  500 - 1000 - 30
Blender
Apache CouchDB
Neural Magic DeepSparse:
  BERT-Large, NLP Question Answering - Asynchronous Multi-Stream:
    ms/batch
    items/sec
Apache Cassandra
Neural Magic DeepSparse:
  BERT-Large, NLP Question Answering, Sparse INT8 - Synchronous Single-Stream:
    ms/batch
    items/sec
  BERT-Large, NLP Question Answering - Synchronous Single-Stream:
    ms/batch
    items/sec
VVenC
Apache CouchDB
NCNN:
  CPU - FastestDet
  CPU - vision_transformer
  CPU - regnety_400m
  CPU - squeezenet_ssd
  CPU - yolov4-tiny
  CPU - resnet50
  CPU - alexnet
  CPU - resnet18
  CPU - vgg16
  CPU - googlenet
  CPU - blazeface
  CPU - efficientnet-b0
  CPU - mnasnet
  CPU - shufflenet-v2
  CPU-v3-v3 - mobilenet-v3
  CPU-v2-v2 - mobilenet-v2
  CPU - mobilenet
Neural Magic DeepSparse:
  BERT-Large, NLP Question Answering, Sparse INT8 - Asynchronous Multi-Stream:
    ms/batch
    items/sec
Blender
Apache IoTDB:
  500 - 100 - 500:
    Average Latency
    point/sec
Blender
Neural Magic DeepSparse:
  NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Synchronous Single-Stream:
    ms/batch
    items/sec
  NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Asynchronous Multi-Stream:
    ms/batch
    items/sec
VVenC
Neural Magic DeepSparse:
  NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Asynchronous Multi-Stream:
    ms/batch
    items/sec
  NLP Text Classification, BERT base uncased SST2 - Asynchronous Multi-Stream:
    ms/batch
    items/sec
  CV Segmentation, 90% Pruned YOLACT Pruned - Asynchronous Multi-Stream:
    ms/batch
    items/sec
  NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Asynchronous Multi-Stream:
    ms/batch
    items/sec
  NLP Document Classification, oBERT base uncased on IMDB - Asynchronous Multi-Stream:
    ms/batch
    items/sec
  NLP Token Classification, BERT base uncased conll2003 - Asynchronous Multi-Stream:
    ms/batch
    items/sec
  NLP Text Classification, BERT base uncased SST2 - Synchronous Single-Stream:
    ms/batch
    items/sec
  NLP Document Classification, oBERT base uncased on IMDB - Synchronous Single-Stream:
    ms/batch
    items/sec
  NLP Token Classification, BERT base uncased conll2003 - Synchronous Single-Stream:
    ms/batch
    items/sec
  NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Synchronous Single-Stream:
    ms/batch
    items/sec
  NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Synchronous Single-Stream:
    ms/batch
    items/sec
Apache IoTDB:
  200 - 100 - 500:
    Average Latency
    point/sec
Neural Magic DeepSparse:
  CV Segmentation, 90% Pruned YOLACT Pruned - Synchronous Single-Stream:
    ms/batch
    items/sec
  NLP Text Classification, DistilBERT mnli - Asynchronous Multi-Stream:
    ms/batch
    items/sec
Apache IoTDB:
  500 - 100 - 200:
    Average Latency
    point/sec
srsRAN Project
Neural Magic DeepSparse:
  NLP Text Classification, DistilBERT mnli - Synchronous Single-Stream:
    ms/batch
    items/sec
VVenC
Neural Magic DeepSparse:
  CV Detection, YOLOv5s COCO, Sparse INT8 - Asynchronous Multi-Stream:
    ms/batch
    items/sec
  CV Detection, YOLOv5s COCO - Asynchronous Multi-Stream:
    ms/batch
    items/sec
  ResNet-50, Sparse INT8 - Synchronous Single-Stream:
    ms/batch
    items/sec
  CV Detection, YOLOv5s COCO - Synchronous Single-Stream:
    ms/batch
    items/sec
  CV Detection, YOLOv5s COCO, Sparse INT8 - Synchronous Single-Stream:
    ms/batch
    items/sec
  ResNet-50, Baseline - Asynchronous Multi-Stream:
    ms/batch
    items/sec
  CV Classification, ResNet-50 ImageNet - Asynchronous Multi-Stream:
    ms/batch
    items/sec
  ResNet-50, Baseline - Synchronous Single-Stream:
    ms/batch
    items/sec
  CV Classification, ResNet-50 ImageNet - Synchronous Single-Stream:
    ms/batch
    items/sec
  ResNet-50, Sparse INT8 - Asynchronous Multi-Stream:
    ms/batch
    items/sec
Apache IoTDB:
  500 - 1 - 500:
    Average Latency
    point/sec
Blender:
  Fishy Cat - CPU-Only
  BMW27 - CPU-Only
Apache IoTDB:
  100 - 100 - 500:
    Average Latency
    point/sec
  200 - 100 - 200:
    Average Latency
    point/sec
  200 - 1 - 500:
    Average Latency
    point/sec
  500 - 1 - 200:
    Average Latency
    point/sec
VVenC
srsRAN Project
Apache IoTDB:
  100 - 100 - 200:
    Average Latency
    point/sec
  100 - 1 - 500:
    Average Latency
    point/sec
  200 - 1 - 200:
    Average Latency
    point/sec
  100 - 1 - 200:
    Average Latency
    point/sec
srsRAN Project

a

Kernel Notes: Transparent Huge Pages: madvise
Compiler Notes: --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-bootstrap --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,brig,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-link-serialization=2 --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-targets=nvptx-none=/build/gcc-11-xKiWfi/gcc-11-11.3.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-11-xKiWfi/gcc-11-11.3.0/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-build-config=bootstrap-lto-lean --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 Notes: Scaling Governor: acpi-cpufreq performance (Boost: Enabled) - CPU Microcode: 0xa001173
Java Notes: OpenJDK Runtime Environment (build 11.0.20+8-post-Ubuntu-1ubuntu122.04)
Python Notes: Python 3.10.6
Security Notes: 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 Retpolines IBPB: conditional IBRS_FW STIBP: always-on RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected

Testing initiated at 4 August 2023 14:20 by user phoronix.

b

Kernel Notes: Transparent Huge Pages: madvise
Compiler Notes: --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-bootstrap --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,brig,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-link-serialization=2 --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-targets=nvptx-none=/build/gcc-11-xKiWfi/gcc-11-11.3.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-11-xKiWfi/gcc-11-11.3.0/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-build-config=bootstrap-lto-lean --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 Notes: Scaling Governor: acpi-cpufreq performance (Boost: Enabled) - CPU Microcode: 0xa001173
Java Notes: OpenJDK Runtime Environment (build 11.0.20+8-post-Ubuntu-1ubuntu122.04)
Python Notes: Python 3.10.6
Security Notes: 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 Retpolines IBPB: conditional IBRS_FW STIBP: always-on RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected

Testing initiated at 4 August 2023 19:01 by user phoronix.

c

Processor: AMD EPYC 7763 64-Core @ 2.45GHz (64 Cores / 128 Threads), Motherboard: AMD DAYTONA_X (RYM1009B BIOS), Chipset: AMD Starship/Matisse, Memory: 256GB, Disk: 800GB INTEL SSDPF21Q800GB, Graphics: ASPEED, Monitor: VE228, Network: 2 x Mellanox MT27710

OS: Ubuntu 22.04, Kernel: 6.2.0-phx (x86_64), Desktop: GNOME Shell 42.5, Display Server: X Server 1.21.1.3, Vulkan: 1.3.224, Compiler: GCC 11.3.0 + LLVM 14.0.0, File-System: ext4, Screen Resolution: 1920x1080

Kernel Notes: Transparent Huge Pages: madvise
Compiler Notes: --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-bootstrap --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,brig,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-link-serialization=2 --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-targets=nvptx-none=/build/gcc-11-xKiWfi/gcc-11-11.3.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-11-xKiWfi/gcc-11-11.3.0/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-build-config=bootstrap-lto-lean --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 Notes: Scaling Governor: acpi-cpufreq performance (Boost: Enabled) - CPU Microcode: 0xa001173
Java Notes: OpenJDK Runtime Environment (build 11.0.20+8-post-Ubuntu-1ubuntu122.04)
Python Notes: Python 3.10.6
Security Notes: 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 Retpolines IBPB: conditional IBRS_FW STIBP: always-on RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected

Testing initiated at 5 August 2023 05:10 by user phoronix.