xmas

Intel Core i9-10980XE testing with a ASRock X299 Steel Legend (P1.50 BIOS) and llvmpipe 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 2312258-PTS-XMAS815235
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December 24 2023
  3 Hours, 44 Minutes
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December 25 2023
  3 Hours, 40 Minutes
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December 25 2023
  3 Hours, 40 Minutes
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xmasOpenBenchmarking.orgPhoronix Test SuiteIntel Core i9-10980XE @ 4.80GHz (18 Cores / 36 Threads)ASRock X299 Steel Legend (P1.50 BIOS)Intel Sky Lake-E DMI3 Registers32GBSamsung SSD 970 PRO 512GBllvmpipeRealtek ALC1220Intel I219-V + Intel I211Ubuntu 22.046.2.0-36-generic (x86_64)GNOME Shell 42.2X Server 1.21.1.44.5 Mesa 22.0.1 (LLVM 13.0.1 256 bits)1.2.204GCC 11.4.0ext41024x768ProcessorMotherboardChipsetMemoryDiskGraphicsAudioNetworkOSKernelDesktopDisplay ServerOpenGLVulkanCompilerFile-SystemScreen ResolutionXmas PerformanceSystem 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-XeT9lY/gcc-11-11.4.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-11-XeT9lY/gcc-11-11.4.0/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: intel_cpufreq schedutil - CPU Microcode: 0x5003604- a: OpenJDK Runtime Environment (build 11.0.20.1+1-post-Ubuntu-0ubuntu122.04) - b: OpenJDK Runtime Environment (build 11.0.20.1+1-post-Ubuntu-0ubuntu122.04) - c: OpenJDK Runtime Environment (build 11.0.21+9-post-Ubuntu-0ubuntu122.04)- Python 3.10.12- gather_data_sampling: Mitigation of Microcode + itlb_multihit: KVM: Mitigation of VMX disabled + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Mitigation of Clear buffers; SMT vulnerable + retbleed: Mitigation of Enhanced IBRS + spec_rstack_overflow: 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: Mitigation of TSX disabled

abcResult OverviewPhoronix Test Suite100%117%134%150%167%ScyllaDBJava SciMarkApache Spark TPC-HLeelaChessZeroWebP2 Image EncodeXmrigNeural Magic DeepSparseEmbreeSVT-AV1

xmasembree: Pathtracer - Crownembree: Pathtracer ISPC - Crownembree: Pathtracer - Asian Dragonembree: Pathtracer - Asian Dragon Objembree: Pathtracer ISPC - Asian Dragonembree: Pathtracer ISPC - Asian Dragon Objsvt-av1: Preset 4 - Bosphorus 4Ksvt-av1: Preset 8 - Bosphorus 4Ksvt-av1: Preset 12 - Bosphorus 4Ksvt-av1: Preset 13 - Bosphorus 4Ksvt-av1: Preset 4 - Bosphorus 1080psvt-av1: Preset 8 - Bosphorus 1080psvt-av1: Preset 12 - Bosphorus 1080psvt-av1: Preset 13 - Bosphorus 1080pxmrig: KawPow - 1Mxmrig: Monero - 1Mxmrig: Wownero - 1Mxmrig: GhostRider - 1Mxmrig: CryptoNight-Heavy - 1Mxmrig: CryptoNight-Femto UPX2 - 1Mdeepsparse: NLP Document Classification, oBERT base uncased on IMDB - Asynchronous Multi-Streamdeepsparse: NLP Document Classification, oBERT base uncased on IMDB - Synchronous Single-Streamdeepsparse: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Asynchronous Multi-Streamdeepsparse: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Synchronous Single-Streamdeepsparse: ResNet-50, Baseline - Asynchronous Multi-Streamdeepsparse: ResNet-50, Baseline - Synchronous Single-Streamdeepsparse: ResNet-50, Sparse INT8 - Asynchronous Multi-Streamdeepsparse: ResNet-50, Sparse INT8 - Synchronous Single-Streamdeepsparse: CV Detection, YOLOv5s COCO - Asynchronous Multi-Streamdeepsparse: CV Detection, YOLOv5s COCO - Synchronous Single-Streamdeepsparse: BERT-Large, NLP Question Answering - Asynchronous Multi-Streamdeepsparse: BERT-Large, NLP Question Answering - Synchronous Single-Streamdeepsparse: CV Classification, ResNet-50 ImageNet - Asynchronous Multi-Streamdeepsparse: CV Classification, ResNet-50 ImageNet - Synchronous Single-Streamdeepsparse: CV Detection, YOLOv5s COCO, Sparse INT8 - Asynchronous Multi-Streamdeepsparse: CV Detection, YOLOv5s COCO, Sparse INT8 - Synchronous Single-Streamdeepsparse: NLP Text Classification, DistilBERT mnli - Asynchronous Multi-Streamdeepsparse: NLP Text Classification, DistilBERT mnli - Synchronous Single-Streamdeepsparse: CV Segmentation, 90% Pruned YOLACT Pruned - Asynchronous Multi-Streamdeepsparse: CV Segmentation, 90% Pruned YOLACT Pruned - Synchronous Single-Streamdeepsparse: BERT-Large, NLP Question Answering, Sparse INT8 - Asynchronous Multi-Streamdeepsparse: BERT-Large, NLP Question Answering, Sparse INT8 - Synchronous Single-Streamdeepsparse: NLP Token Classification, BERT base uncased conll2003 - Asynchronous Multi-Streamdeepsparse: NLP Token Classification, BERT base uncased conll2003 - Synchronous Single-Streamjava-scimark2: Compositejava-scimark2: Monte Carlojava-scimark2: Fast Fourier Transformjava-scimark2: Sparse Matrix Multiplyjava-scimark2: Dense LU Matrix Factorizationjava-scimark2: Jacobi Successive Over-Relaxationwebp2: Defaultwebp2: Quality 75, Compression Effort 7webp2: Quality 95, Compression Effort 7webp2: Quality 100, Compression Effort 5webp2: Quality 100, Lossless Compressionlczero: BLASlczero: Eigenscylladb: Writesdeepsparse: NLP Document Classification, oBERT base uncased on IMDB - Asynchronous Multi-Streamdeepsparse: NLP Document Classification, oBERT base uncased on IMDB - Synchronous Single-Streamdeepsparse: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Asynchronous Multi-Streamdeepsparse: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Synchronous Single-Streamdeepsparse: ResNet-50, Baseline - Asynchronous Multi-Streamdeepsparse: ResNet-50, Baseline - Synchronous Single-Streamdeepsparse: ResNet-50, Sparse INT8 - Asynchronous Multi-Streamdeepsparse: ResNet-50, Sparse INT8 - Synchronous Single-Streamdeepsparse: CV Detection, YOLOv5s COCO - Asynchronous Multi-Streamdeepsparse: CV Detection, YOLOv5s COCO - Synchronous Single-Streamdeepsparse: BERT-Large, NLP Question Answering - Asynchronous Multi-Streamdeepsparse: BERT-Large, NLP Question Answering - Synchronous Single-Streamdeepsparse: CV Classification, ResNet-50 ImageNet - Asynchronous Multi-Streamdeepsparse: CV Classification, ResNet-50 ImageNet - Synchronous Single-Streamdeepsparse: CV Detection, YOLOv5s COCO, Sparse INT8 - Asynchronous Multi-Streamdeepsparse: CV Detection, YOLOv5s COCO, Sparse INT8 - Synchronous Single-Streamdeepsparse: NLP Text Classification, DistilBERT mnli - Asynchronous Multi-Streamdeepsparse: NLP Text Classification, DistilBERT mnli - Synchronous Single-Streamdeepsparse: CV Segmentation, 90% Pruned YOLACT Pruned - Asynchronous Multi-Streamdeepsparse: CV Segmentation, 90% Pruned YOLACT Pruned - Synchronous Single-Streamdeepsparse: BERT-Large, NLP Question Answering, Sparse INT8 - Asynchronous Multi-Streamdeepsparse: BERT-Large, NLP Question Answering, Sparse INT8 - Synchronous Single-Streamdeepsparse: NLP Token Classification, BERT base uncased conll2003 - Asynchronous Multi-Streamdeepsparse: NLP Token Classification, BERT base uncased conll2003 - Synchronous Single-Streamspark-tpch: 1 - Geometric Mean Of All Queriesspark-tpch: 1 - Q01spark-tpch: 1 - Q02spark-tpch: 1 - Q03spark-tpch: 1 - Q04spark-tpch: 1 - Q05spark-tpch: 1 - Q06spark-tpch: 1 - Q07spark-tpch: 1 - Q08spark-tpch: 1 - Q09spark-tpch: 1 - Q10spark-tpch: 1 - Q11spark-tpch: 1 - Q12spark-tpch: 1 - Q13spark-tpch: 1 - Q14spark-tpch: 1 - Q15spark-tpch: 1 - Q16spark-tpch: 1 - Q17spark-tpch: 1 - Q18spark-tpch: 1 - Q19spark-tpch: 1 - Q20spark-tpch: 1 - Q21spark-tpch: 1 - Q22spark-tpch: 10 - Geometric Mean Of All Queriesspark-tpch: 10 - Q01spark-tpch: 10 - Q02spark-tpch: 10 - Q03spark-tpch: 10 - Q04spark-tpch: 10 - Q05spark-tpch: 10 - Q06spark-tpch: 10 - Q07spark-tpch: 10 - Q08spark-tpch: 10 - Q09spark-tpch: 10 - Q10spark-tpch: 10 - Q11spark-tpch: 10 - Q12spark-tpch: 10 - Q13spark-tpch: 10 - Q14spark-tpch: 10 - Q15spark-tpch: 10 - Q16spark-tpch: 10 - Q17spark-tpch: 10 - Q18spark-tpch: 10 - Q19spark-tpch: 10 - Q20spark-tpch: 10 - Q21spark-tpch: 10 - Q22abc20.205317.562123.028720.715722.861319.57533.12832.264108.781109.9438.48446.14166.804190.6134305.94307.89424.31065.34303.84305.218.037315.9141456.6784146.9619255.5818126.25931200.7226566.7128104.289883.531723.475715.0063260.4289130.1068112.273884.2085151.547388.747422.327421.8915209.783852.665318.609516.40222329.081169.97710.661833.486206.961724.338.080.190.095.350.029982150834497.074762.826619.68456.794535.19037.90787.47631.755386.270411.9564383.344566.627134.52267.67480.102611.864559.365411.2587403.046845.662542.878518.9753481.545760.9571.972006763.992056612.085803032.506989482.418701412.904836420.802325132.537325861.945723183.754305122.398213151.183946491.53979171.636026741.200576311.501062751.648400192.263289453.001428131.154087311.951336987.52318431.484742648.052348499.470716485.9903135311.958260549.4513511712.43594175.942317499.7210130710.6501445813.56535539.686726572.648324977.736131674.266768466.973340516.559264184.0039286616.3847675316.954257976.646073348.6878213931.770061493.3984825620.193917.625623.084320.734222.956719.66263.1132.181109.557108.5778.46145.484168.775193.5264302.84308.79409.81047.94308.74301.218.439216.0113456.1242145.3856257.8639134.83011203.566584.8061105.603884.205423.567915.1019256.1826121.154110.226884.2665148.27787.128921.890921.138206.476957.39618.180816.29572275.421169.02684.241820.445979.071724.338.270.190.095.280.021058690383485.848462.445319.70816.868934.87957.40467.45841.700485.19711.8606381.841966.205635.10868.242381.626811.857260.665611.4689408.050347.292243.562917.4106493.556761.35621.742525363.83936621.885463712.356637721.976770042.342034580.733412272.264334441.872031933.019853351.944525961.13731421.485789661.234474781.091108441.254616861.421266912.114368442.802644251.064415451.704559096.772738931.276329647.7668340410.546422964.931752212.321187979.9794063612.197178845.8046112110.206960689.9221239113.018259059.727175712.598317157.772675514.141022686.811183936.533554553.2482144816.2044448916.556819926.557401668.3949708931.070913313.2390141520.192417.756823.049820.662322.897419.59553.11132.041109.847109.5368.43246.47165.48193.6074307.14302.29344.410814310.84299.718.256116.2535455.2521148.2704258.8256123.72891201.8453569.7959105.424282.782623.482514.9436257.2067129.1964110.708884.4543153.243889.473422.272921.9442210.209356.753818.198116.47632451.211169.33711.852453.436196.151725.37.980.190.095.180.0210780150956492.465461.515319.74586.734934.74888.07077.46891.746485.34212.0644383.232166.907534.96697.728381.271411.830358.707711.1673404.032945.552742.791817.6073493.526660.68331.729948563.734766242.206153152.296066052.026780612.509186030.776137772.296468731.907357453.195964572.050014730.930948621.521212581.212598681.127186661.334101321.492031932.150347952.73754431.100467441.725702646.270213131.102087267.8137748410.409886365.1355609912.297739989.5777244612.861410145.8888950310.3035736110.7421560313.225128179.422932622.484924557.669171814.262536536.641220096.551971443.4871938216.1918907216.658258446.742002498.4676771231.416904453.09575415OpenBenchmarking.org

PyTorch

Device: CPU - Batch Size: 1 - Model: ResNet-50

a: The test quit with a non-zero exit status. E: ValueError: invalid literal for int() with base 10: "NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running."

b: The test quit with a non-zero exit status. E: ValueError: invalid literal for int() with base 10: "NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running."

c: The test quit with a non-zero exit status. E: ValueError: invalid literal for int() with base 10: "NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running."

Device: CPU - Batch Size: 1 - Model: ResNet-152

a: The test quit with a non-zero exit status. E: ValueError: invalid literal for int() with base 10: "NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running."

b: The test quit with a non-zero exit status. E: ValueError: invalid literal for int() with base 10: "NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running."

c: The test quit with a non-zero exit status. E: ValueError: invalid literal for int() with base 10: "NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running."

Device: CPU - Batch Size: 512 - Model: ResNet-50

a: The test quit with a non-zero exit status. E: ValueError: invalid literal for int() with base 10: "NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running."

b: The test quit with a non-zero exit status. E: ValueError: invalid literal for int() with base 10: "NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running."

c: The test quit with a non-zero exit status. E: ValueError: invalid literal for int() with base 10: "NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running."

Device: CPU - Batch Size: 512 - Model: ResNet-152

a: The test quit with a non-zero exit status. E: ValueError: invalid literal for int() with base 10: "NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running."

b: The test quit with a non-zero exit status. E: ValueError: invalid literal for int() with base 10: "NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running."

c: The test quit with a non-zero exit status. E: ValueError: invalid literal for int() with base 10: "NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running."

Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l

a: The test quit with a non-zero exit status. E: ValueError: invalid literal for int() with base 10: "NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running."

b: The test quit with a non-zero exit status. E: ValueError: invalid literal for int() with base 10: "NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running."

c: The test quit with a non-zero exit status. E: ValueError: invalid literal for int() with base 10: "NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running."

Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_l

a: The test quit with a non-zero exit status. E: ValueError: invalid literal for int() with base 10: "NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running."

b: The test quit with a non-zero exit status. E: ValueError: invalid literal for int() with base 10: "NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running."

c: The test quit with a non-zero exit status. E: ValueError: invalid literal for int() with base 10: "NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running."

Embree

Intel Embree is a collection of high-performance ray-tracing kernels for execution on CPUs (and GPUs via SYCL) and supporting instruction sets such as SSE, AVX, AVX2, and AVX-512. Embree also supports making use of the Intel SPMD Program Compiler (ISPC). Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFrames Per Second, More Is BetterEmbree 4.3Binary: Pathtracer - Model: Crownabc51015202520.2120.1920.19MIN: 20 / MAX: 20.52MIN: 19.96 / MAX: 20.52MIN: 19.96 / MAX: 20.55

OpenBenchmarking.orgFrames Per Second, More Is BetterEmbree 4.3Binary: Pathtracer ISPC - Model: Crownabc4812162017.5617.6317.76MIN: 17.33 / MAX: 17.82MIN: 17.37 / MAX: 17.86MIN: 17.5 / MAX: 18.06

OpenBenchmarking.orgFrames Per Second, More Is BetterEmbree 4.3Binary: Pathtracer - Model: Asian Dragonabc61218243023.0323.0823.05MIN: 22.87 / MAX: 23.24MIN: 22.92 / MAX: 23.31MIN: 22.91 / MAX: 23.24

OpenBenchmarking.orgFrames Per Second, More Is BetterEmbree 4.3Binary: Pathtracer - Model: Asian Dragon Objabc51015202520.7220.7320.66MIN: 20.57 / MAX: 20.97MIN: 20.6 / MAX: 20.92MIN: 20.52 / MAX: 20.94

OpenBenchmarking.orgFrames Per Second, More Is BetterEmbree 4.3Binary: Pathtracer ISPC - Model: Asian Dragonabc51015202522.8622.9622.90MIN: 22.69 / MAX: 23.08MIN: 22.78 / MAX: 23.14MIN: 22.72 / MAX: 23.13

OpenBenchmarking.orgFrames Per Second, More Is BetterEmbree 4.3Binary: Pathtracer ISPC - Model: Asian Dragon Objabc51015202519.5819.6619.60MIN: 19.38 / MAX: 19.81MIN: 19.51 / MAX: 19.86MIN: 19.43 / MAX: 19.81

SVT-AV1

This is a benchmark of the SVT-AV1 open-source video encoder/decoder. SVT-AV1 was originally developed by Intel as part of their Open Visual Cloud / Scalable Video Technology (SVT). Development of SVT-AV1 has since moved to the Alliance for Open Media as part of upstream AV1 development. SVT-AV1 is a CPU-based multi-threaded video encoder for the AV1 video format with a sample YUV video file. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFrames Per Second, More Is BetterSVT-AV1 1.8Encoder Mode: Preset 4 - Input: Bosphorus 4Kabc0.70381.40762.11142.81523.5193.1283.1103.1111. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq

OpenBenchmarking.orgFrames Per Second, More Is BetterSVT-AV1 1.8Encoder Mode: Preset 8 - Input: Bosphorus 4Kabc71421283532.2632.1832.041. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq

OpenBenchmarking.orgFrames Per Second, More Is BetterSVT-AV1 1.8Encoder Mode: Preset 12 - Input: Bosphorus 4Kabc20406080100108.78109.56109.851. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq

OpenBenchmarking.orgFrames Per Second, More Is BetterSVT-AV1 1.8Encoder Mode: Preset 13 - Input: Bosphorus 4Kabc20406080100109.94108.58109.541. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq

OpenBenchmarking.orgFrames Per Second, More Is BetterSVT-AV1 1.8Encoder Mode: Preset 4 - Input: Bosphorus 1080pabc2468108.4848.4618.4321. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq

OpenBenchmarking.orgFrames Per Second, More Is BetterSVT-AV1 1.8Encoder Mode: Preset 8 - Input: Bosphorus 1080pabc112233445546.1445.4846.471. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq

OpenBenchmarking.orgFrames Per Second, More Is BetterSVT-AV1 1.8Encoder Mode: Preset 12 - Input: Bosphorus 1080pabc4080120160200166.80168.78165.481. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq

OpenBenchmarking.orgFrames Per Second, More Is BetterSVT-AV1 1.8Encoder Mode: Preset 13 - Input: Bosphorus 1080pabc4080120160200190.61193.53193.611. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq

Xmrig

Xmrig is an open-source cross-platform CPU/GPU miner for RandomX, KawPow, CryptoNight and AstroBWT. This test profile is setup to measure the Xmrig CPU mining performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgH/s, More Is BetterXmrig 6.21Variant: KawPow - Hash Count: 1Mabc90018002700360045004305.94302.84307.11. (CXX) g++ options: -fexceptions -fno-rtti -maes -O3 -Ofast -static-libgcc -static-libstdc++ -rdynamic -lssl -lcrypto -luv -lpthread -lrt -ldl -lhwloc

OpenBenchmarking.orgH/s, More Is BetterXmrig 6.21Variant: Monero - Hash Count: 1Mabc90018002700360045004307.84308.74302.21. (CXX) g++ options: -fexceptions -fno-rtti -maes -O3 -Ofast -static-libgcc -static-libstdc++ -rdynamic -lssl -lcrypto -luv -lpthread -lrt -ldl -lhwloc

OpenBenchmarking.orgH/s, More Is BetterXmrig 6.21Variant: Wownero - Hash Count: 1Mabc2K4K6K8K10K9424.39409.89344.41. (CXX) g++ options: -fexceptions -fno-rtti -maes -O3 -Ofast -static-libgcc -static-libstdc++ -rdynamic -lssl -lcrypto -luv -lpthread -lrt -ldl -lhwloc

OpenBenchmarking.orgH/s, More Is BetterXmrig 6.21Variant: GhostRider - Hash Count: 1Mabc20040060080010001065.31047.91081.01. (CXX) g++ options: -fexceptions -fno-rtti -maes -O3 -Ofast -static-libgcc -static-libstdc++ -rdynamic -lssl -lcrypto -luv -lpthread -lrt -ldl -lhwloc

OpenBenchmarking.orgH/s, More Is BetterXmrig 6.21Variant: CryptoNight-Heavy - Hash Count: 1Mabc90018002700360045004303.84308.74310.81. (CXX) g++ options: -fexceptions -fno-rtti -maes -O3 -Ofast -static-libgcc -static-libstdc++ -rdynamic -lssl -lcrypto -luv -lpthread -lrt -ldl -lhwloc

OpenBenchmarking.orgH/s, More Is BetterXmrig 6.21Variant: CryptoNight-Femto UPX2 - Hash Count: 1Mabc90018002700360045004305.24301.24299.71. (CXX) g++ options: -fexceptions -fno-rtti -maes -O3 -Ofast -static-libgcc -static-libstdc++ -rdynamic -lssl -lcrypto -luv -lpthread -lrt -ldl -lhwloc

Neural Magic DeepSparse

This is a benchmark of Neural Magic's DeepSparse using its built-in deepsparse.benchmark utility and various models from their SparseZoo (https://sparsezoo.neuralmagic.com/). Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.6Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Streamabc51015202518.0418.4418.26

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.6Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Streamabc4812162015.9116.0116.25

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.6Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Streamabc100200300400500456.68456.12455.25

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.6Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Streamabc306090120150146.96145.39148.27

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.6Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Streamabc60120180240300255.58257.86258.83

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.6Model: ResNet-50, Baseline - Scenario: Synchronous Single-Streamabc306090120150126.26134.83123.73

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.6Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Streamabc300600900120015001200.721203.571201.85

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.6Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Streamabc130260390520650566.71584.81569.80

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.6Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Streamabc20406080100104.29105.60105.42

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.6Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Streamabc2040608010083.5384.2182.78

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.6Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Streamabc61218243023.4823.5723.48

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.6Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Streamabc4812162015.0115.1014.94

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.6Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Streamabc60120180240300260.43256.18257.21

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.6Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Streamabc306090120150130.11121.15129.20

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.6Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Streamabc306090120150112.27110.23110.71

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.6Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Streamabc2040608010084.2184.2784.45

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.6Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Streamabc306090120150151.55148.28153.24

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.6Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Streamabc2040608010088.7587.1389.47

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.6Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Streamabc51015202522.3321.8922.27

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.6Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Streamabc51015202521.8921.1421.94

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.6Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Streamabc50100150200250209.78206.48210.21

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.6Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Streamabc132639526552.6757.4056.75

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.6Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Streamabc51015202518.6118.1818.20

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.6Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Streamabc4812162016.4016.3016.48

Java SciMark

This test runs the Java version of SciMark 2, which is a benchmark for scientific and numerical computing developed by programmers at the National Institute of Standards and Technology. This benchmark is made up of Fast Foruier Transform, Jacobi Successive Over-relaxation, Monte Carlo, Sparse Matrix Multiply, and dense LU matrix factorization benchmarks. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMflops, More Is BetterJava SciMark 2.2Computational Test: Compositeabc50010001500200025002329.082275.422451.21

OpenBenchmarking.orgMflops, More Is BetterJava SciMark 2.2Computational Test: Monte Carloabc300600900120015001169.971169.021169.33

OpenBenchmarking.orgMflops, More Is BetterJava SciMark 2.2Computational Test: Fast Fourier Transformabc150300450600750710.66684.24711.85

OpenBenchmarking.orgMflops, More Is BetterJava SciMark 2.2Computational Test: Sparse Matrix Multiplyabc50010001500200025001833.481820.442453.43

OpenBenchmarking.orgMflops, More Is BetterJava SciMark 2.2Computational Test: Dense LU Matrix Factorizationabc130026003900520065006206.965979.076196.15

OpenBenchmarking.orgMflops, More Is BetterJava SciMark 2.2Computational Test: Jacobi Successive Over-Relaxationabc4008001200160020001724.331724.331725.30

WebP2 Image Encode

This is a test of Google's libwebp2 library with the WebP2 image encode utility and using a sample 6000x4000 pixel JPEG image as the input, similar to the WebP/libwebp test profile. WebP2 is currently experimental and under heavy development as ultimately the successor to WebP. WebP2 supports 10-bit HDR, more efficienct lossy compression, improved lossless compression, animation support, and full multi-threading support compared to WebP. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMP/s, More Is BetterWebP2 Image Encode 20220823Encode Settings: Defaultabc2468108.088.277.981. (CXX) g++ options: -msse4.2 -fno-rtti -O3 -ldl

OpenBenchmarking.orgMP/s, More Is BetterWebP2 Image Encode 20220823Encode Settings: Quality 75, Compression Effort 7abc0.04280.08560.12840.17120.2140.190.190.191. (CXX) g++ options: -msse4.2 -fno-rtti -O3 -ldl

OpenBenchmarking.orgMP/s, More Is BetterWebP2 Image Encode 20220823Encode Settings: Quality 95, Compression Effort 7abc0.02030.04060.06090.08120.10150.090.090.091. (CXX) g++ options: -msse4.2 -fno-rtti -O3 -ldl

OpenBenchmarking.orgMP/s, More Is BetterWebP2 Image Encode 20220823Encode Settings: Quality 100, Compression Effort 5abc1.20382.40763.61144.81526.0195.355.285.181. (CXX) g++ options: -msse4.2 -fno-rtti -O3 -ldl

OpenBenchmarking.orgMP/s, More Is BetterWebP2 Image Encode 20220823Encode Settings: Quality 100, Lossless Compressionabc0.00450.0090.01350.0180.02250.020.020.021. (CXX) g++ options: -msse4.2 -fno-rtti -O3 -ldl

LeelaChessZero

LeelaChessZero (lc0 / lczero) is a chess engine automated vian neural networks. This test profile can be used for OpenCL, CUDA + cuDNN, and BLAS (CPU-based) benchmarking. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgNodes Per Second, More Is BetterLeelaChessZero 0.30Backend: BLASabc20406080100991051071. (CXX) g++ options: -flto -pthread

OpenBenchmarking.orgNodes Per Second, More Is BetterLeelaChessZero 0.30Backend: Eigenabc204060801008286801. (CXX) g++ options: -flto -pthread

ScyllaDB

This is a benchmark of ScyllaDB and is making use of Apache Cassandra's cassandra-stress for conducting the benchmark. ScyllaDB is an open-source distributed NoSQL data store that is compatible with Apache Cassandra while focusing on higher throughput and lower latency. ScyllaDB uses a sharded design on each node. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgOp/s, More Is BetterScyllaDB 5.2.9Test: Writesabc30K60K90K120K150K15083490383150956

Neural Magic DeepSparse

This is a benchmark of Neural Magic's DeepSparse using its built-in deepsparse.benchmark utility and various models from their SparseZoo (https://sparsezoo.neuralmagic.com/). Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.6Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Streamabc110220330440550497.07485.85492.47

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.6Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Streamabc142842567062.8362.4561.52

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.6Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Streamabc51015202519.6819.7119.75

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.6Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Streamabc2468106.79456.86896.7349

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.6Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Streamabc81624324035.1934.8834.75

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.6Model: ResNet-50, Baseline - Scenario: Synchronous Single-Streamabc2468107.90787.40468.0707

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.6Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Streamabc2468107.47637.45847.4689

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.6Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Streamabc0.39490.78981.18471.57961.97451.75531.70041.7464

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.6Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Streamabc2040608010086.2785.2085.34

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.6Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Streamabc369121511.9611.8612.06

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.6Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Streamabc80160240320400383.34381.84383.23

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.6Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Streamabc153045607566.6366.2166.91

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.6Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Streamabc81624324034.5235.1134.97

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.6Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Streamabc2468107.67408.24237.7283

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.6Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Streamabc2040608010080.1081.6381.27

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.6Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Streamabc369121511.8611.8611.83

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.6Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Streamabc142842567059.3760.6758.71

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.6Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Streamabc369121511.2611.4711.17

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.6Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Streamabc90180270360450403.05408.05404.03

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.6Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Streamabc112233445545.6647.2945.55

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.6Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Streamabc102030405042.8843.5642.79

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.6Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Streamabc51015202518.9817.4117.61

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.6Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Streamabc110220330440550481.55493.56493.53

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.6Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Streamabc142842567060.9661.3660.68

Apache Spark TPC-H

This is a benchmark of Apache Spark using TPC-H data-set. Apache Spark is an open-source unified analytics engine for large-scale data processing and dealing with big data. This test profile benchmarks the Apache Spark in a single-system configuration using spark-submit. The test makes use of https://github.com/ssavvides/tpch-spark/ for facilitating the TPC-H benchmark. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterApache Spark TPC-H 3.5Scale Factor: 1 - Geometric Mean Of All Queriesabc0.44370.88741.33111.77482.21851.972006761.742525361.72994856MIN: 1.15 / MAX: 7.52MIN: 1.06 / MAX: 6.77MIN: 0.93 / MAX: 6.27

OpenBenchmarking.orgSeconds, Fewer Is BetterApache Spark TPC-H 3.5Scale Factor: 10 - Geometric Mean Of All Queriesabc2468108.052348497.766834047.81377484MIN: 2.65 / MAX: 31.77MIN: 2.6 / MAX: 31.07MIN: 2.48 / MAX: 31.42

84 Results Shown

Embree:
  Pathtracer - Crown
  Pathtracer ISPC - Crown
  Pathtracer - Asian Dragon
  Pathtracer - Asian Dragon Obj
  Pathtracer ISPC - Asian Dragon
  Pathtracer ISPC - Asian Dragon Obj
SVT-AV1:
  Preset 4 - Bosphorus 4K
  Preset 8 - Bosphorus 4K
  Preset 12 - Bosphorus 4K
  Preset 13 - Bosphorus 4K
  Preset 4 - Bosphorus 1080p
  Preset 8 - Bosphorus 1080p
  Preset 12 - Bosphorus 1080p
  Preset 13 - Bosphorus 1080p
Xmrig:
  KawPow - 1M
  Monero - 1M
  Wownero - 1M
  GhostRider - 1M
  CryptoNight-Heavy - 1M
  CryptoNight-Femto UPX2 - 1M
Neural Magic DeepSparse:
  NLP Document Classification, oBERT base uncased on IMDB - Asynchronous Multi-Stream
  NLP Document Classification, oBERT base uncased on IMDB - Synchronous Single-Stream
  NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Asynchronous Multi-Stream
  NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Synchronous Single-Stream
  ResNet-50, Baseline - Asynchronous Multi-Stream
  ResNet-50, Baseline - Synchronous Single-Stream
  ResNet-50, Sparse INT8 - Asynchronous Multi-Stream
  ResNet-50, Sparse INT8 - Synchronous Single-Stream
  CV Detection, YOLOv5s COCO - Asynchronous Multi-Stream
  CV Detection, YOLOv5s COCO - Synchronous Single-Stream
  BERT-Large, NLP Question Answering - Asynchronous Multi-Stream
  BERT-Large, NLP Question Answering - Synchronous Single-Stream
  CV Classification, ResNet-50 ImageNet - Asynchronous Multi-Stream
  CV Classification, ResNet-50 ImageNet - Synchronous Single-Stream
  CV Detection, YOLOv5s COCO, Sparse INT8 - Asynchronous Multi-Stream
  CV Detection, YOLOv5s COCO, Sparse INT8 - Synchronous Single-Stream
  NLP Text Classification, DistilBERT mnli - Asynchronous Multi-Stream
  NLP Text Classification, DistilBERT mnli - Synchronous Single-Stream
  CV Segmentation, 90% Pruned YOLACT Pruned - Asynchronous Multi-Stream
  CV Segmentation, 90% Pruned YOLACT Pruned - Synchronous Single-Stream
  BERT-Large, NLP Question Answering, Sparse INT8 - Asynchronous Multi-Stream
  BERT-Large, NLP Question Answering, Sparse INT8 - Synchronous Single-Stream
  NLP Token Classification, BERT base uncased conll2003 - Asynchronous Multi-Stream
  NLP Token Classification, BERT base uncased conll2003 - Synchronous Single-Stream
Java SciMark:
  Composite
  Monte Carlo
  Fast Fourier Transform
  Sparse Matrix Multiply
  Dense LU Matrix Factorization
  Jacobi Successive Over-Relaxation
WebP2 Image Encode:
  Default
  Quality 75, Compression Effort 7
  Quality 95, Compression Effort 7
  Quality 100, Compression Effort 5
  Quality 100, Lossless Compression
LeelaChessZero:
  BLAS
  Eigen
ScyllaDB
Neural Magic DeepSparse:
  NLP Document Classification, oBERT base uncased on IMDB - Asynchronous Multi-Stream
  NLP Document Classification, oBERT base uncased on IMDB - Synchronous Single-Stream
  NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Asynchronous Multi-Stream
  NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Synchronous Single-Stream
  ResNet-50, Baseline - Asynchronous Multi-Stream
  ResNet-50, Baseline - Synchronous Single-Stream
  ResNet-50, Sparse INT8 - Asynchronous Multi-Stream
  ResNet-50, Sparse INT8 - Synchronous Single-Stream
  CV Detection, YOLOv5s COCO - Asynchronous Multi-Stream
  CV Detection, YOLOv5s COCO - Synchronous Single-Stream
  BERT-Large, NLP Question Answering - Asynchronous Multi-Stream
  BERT-Large, NLP Question Answering - Synchronous Single-Stream
  CV Classification, ResNet-50 ImageNet - Asynchronous Multi-Stream
  CV Classification, ResNet-50 ImageNet - Synchronous Single-Stream
  CV Detection, YOLOv5s COCO, Sparse INT8 - Asynchronous Multi-Stream
  CV Detection, YOLOv5s COCO, Sparse INT8 - Synchronous Single-Stream
  NLP Text Classification, DistilBERT mnli - Asynchronous Multi-Stream
  NLP Text Classification, DistilBERT mnli - Synchronous Single-Stream
  CV Segmentation, 90% Pruned YOLACT Pruned - Asynchronous Multi-Stream
  CV Segmentation, 90% Pruned YOLACT Pruned - Synchronous Single-Stream
  BERT-Large, NLP Question Answering, Sparse INT8 - Asynchronous Multi-Stream
  BERT-Large, NLP Question Answering, Sparse INT8 - Synchronous Single-Stream
  NLP Token Classification, BERT base uncased conll2003 - Asynchronous Multi-Stream
  NLP Token Classification, BERT base uncased conll2003 - Synchronous Single-Stream
Apache Spark TPC-H:
  1 - Geometric Mean Of All Queries
  10 - Geometric Mean Of All Queries