5900x xmas

AMD Ryzen 9 5900X 12-Core testing with a ASUS ROG CROSSHAIR VIII HERO (2702 BIOS) and Sapphire AMD Radeon RX 5600 OEM/5600 XT / 5700/5700 6GB on Ubuntu 20.10 via the Phoronix Test Suite.

Compare your own system(s) to this result file with the Phoronix Test Suite by running the command: phoronix-test-suite benchmark 2012257-HA-5900XXMAS22
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December 25 2020
  1 Hour, 20 Minutes
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December 25 2020
  1 Hour, 19 Minutes
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December 25 2020
  1 Hour, 18 Minutes
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5900x xmasProcessorMotherboardChipsetMemoryDiskGraphicsAudioMonitorNetworkOSKernelDesktopDisplay ServerDisplay DriverOpenGLVulkanCompilerFile-SystemScreen Resolution123AMD Ryzen 9 5900X 12-Core @ 3.70GHz (12 Cores / 24 Threads)ASUS ROG CROSSHAIR VIII HERO (2702 BIOS)AMD Starship/Matisse16GB1000GB Sabrent Rocket 4.0 1TBSapphire AMD Radeon RX 5600 OEM/5600 XT / 5700/5700 6GB (1780/875MHz)AMD Navi 10 HDMI AudioASUS VP28URealtek RTL8125 2.5GbE + Intel I211Ubuntu 20.105.10.0-051000daily20201222-generic (x86_64)GNOME Shell 3.38.1X Server 1.20.9modesetting 1.20.94.6 Mesa 20.2.1 (LLVM 11.0.0)1.2.131GCC 10.2.0ext43840x2160OpenBenchmarking.orgCompiler Details- --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --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-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-targets=nvptx-none=/build/gcc-10-JvwpWM/gcc-10-10.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-10-JvwpWM/gcc-10-10.2.0/debian/tmp-gcn/usr,hsa --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --program-prefix=x86_64-linux-gnu- --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -v Processor Details- Scaling Governor: acpi-cpufreq performance (Boost: Enabled) - CPU Microcode: 0xa201009Python Details- Python 3.8.6Security Details- itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + spec_store_bypass: Mitigation of SSB disabled via prctl and seccomp + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Full AMD retpoline IBPB: conditional IBRS_FW STIBP: always-on RSB filling + srbds: Not affected + tsx_async_abort: Not affected

123Result OverviewPhoronix Test Suite 10.6.0100%100%101%101%ECP-CANDLEOpenVKLOpenVKLDarmstadt Automotive Parallel Heterogeneous SuiteDarmstadt Automotive Parallel Heterogeneous SuiteECP-CANDLEDarmstadt Automotive Parallel Heterogeneous SuiteOpenVKLECP-CANDLEOpenVKLP1B2vklBenchmarkVdbVolumevklBenchmarkUnstructuredVolumeOpenMP - Points2ImageOpenMP - Euclidean ClusterP3B2OpenMP - NDT MappingvklBenchmarkP3B1vklBenchmarkStructuredVolume

5900x xmasopenvkl: vklBenchmarkopenvkl: vklBenchmarkVdbVolumeopenvkl: vklBenchmarkStructuredVolumeopenvkl: vklBenchmarkUnstructuredVolumedaphne: OpenMP - NDT Mappingdaphne: OpenMP - Points2Imagedaphne: OpenMP - Euclidean Clusterecp-candle: P1B2ecp-candle: P3B1ecp-candle: P3B2123241.3919570275.37727382969104.8198202366257.3785489934.5931586.9446244981480.0933.5841209.672377.728240.8619340136.40909183109840.0810812396851.1577287932.3131553.4203037271468.7932.9621209.601378.335239.9419682711.95454583065857.2702702393028.8601472928.7231274.4182827511474.5533.2951205.343380.471OpenBenchmarking.org

OpenVKL

OpenVKL is the Intel Open Volume Kernel Library that offers high-performance volume computation kernels and part of the Intel oneAPI rendering toolkit. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgItems / Sec, More Is BetterOpenVKL 0.9Benchmark: vklBenchmark12350100150200250SE +/- 0.57, N = 3SE +/- 0.15, N = 3SE +/- 0.69, N = 3241.39240.86239.94MIN: 1 / MAX: 940MIN: 1 / MAX: 935MIN: 1 / MAX: 934
OpenBenchmarking.orgItems / Sec, More Is BetterOpenVKL 0.9Benchmark: vklBenchmark1234080120160200Min: 240.58 / Avg: 241.39 / Max: 242.5Min: 240.67 / Avg: 240.86 / Max: 241.17Min: 238.58 / Avg: 239.94 / Max: 240.83

OpenBenchmarking.orgItems / Sec, More Is BetterOpenVKL 0.9Benchmark: vklBenchmarkVdbVolume1234M8M12M16M20MSE +/- 199429.73, N = 5SE +/- 232703.70, N = 4SE +/- 94340.36, N = 319570275.3819340136.4119682711.95MIN: 1157756 / MAX: 89758656MIN: 1186688 / MAX: 79048440MIN: 1182407 / MAX: 78800112
OpenBenchmarking.orgItems / Sec, More Is BetterOpenVKL 0.9Benchmark: vklBenchmarkVdbVolume1233M6M9M12M15MMin: 18988244.93 / Avg: 19570275.38 / Max: 20171193.64Min: 18685327.18 / Avg: 19340136.41 / Max: 19694492.36Min: 19551883.95 / Avg: 19682711.95 / Max: 19865868.61

OpenBenchmarking.orgItems / Sec, More Is BetterOpenVKL 0.9Benchmark: vklBenchmarkStructuredVolume12320M40M60M80M100MSE +/- 907349.87, N = 3SE +/- 1005744.83, N = 3SE +/- 302216.96, N = 382969104.8283109840.0883065857.27MIN: 1000000 / MAX: 746519112MIN: 1349601 / MAX: 822902688MIN: 1369302 / MAX: 738011376
OpenBenchmarking.orgItems / Sec, More Is BetterOpenVKL 0.9Benchmark: vklBenchmarkStructuredVolume12314M28M42M56M70MMin: 82016774.22 / Avg: 82969104.82 / Max: 84783048.57Min: 81574868.3 / Avg: 83109840.08 / Max: 85003143.11Min: 82485826.08 / Avg: 83065857.27 / Max: 83503107.86

OpenBenchmarking.orgItems / Sec, More Is BetterOpenVKL 0.9Benchmark: vklBenchmarkUnstructuredVolume123500K1000K1500K2000K2500KSE +/- 3185.21, N = 3SE +/- 16891.07, N = 3SE +/- 21972.58, N = 32366257.382396851.162393028.86MIN: 32264 / MAX: 8362513MIN: 33037 / MAX: 8601704MIN: 32539 / MAX: 8689269
OpenBenchmarking.orgItems / Sec, More Is BetterOpenVKL 0.9Benchmark: vklBenchmarkUnstructuredVolume123400K800K1200K1600K2000KMin: 2360804.79 / Avg: 2366257.38 / Max: 2371836.52Min: 2370094.92 / Avg: 2396851.16 / Max: 2428089.93Min: 2355273.04 / Avg: 2393028.86 / Max: 2431381.29

Darmstadt Automotive Parallel Heterogeneous Suite

DAPHNE is the Darmstadt Automotive Parallel HeterogeNEous Benchmark Suite with OpenCL / CUDA / OpenMP test cases for these automotive benchmarks for evaluating programming models in context to vehicle autonomous driving capabilities. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgTest Cases Per Minute, More Is BetterDarmstadt Automotive Parallel Heterogeneous SuiteBackend: OpenMP - Kernel: NDT Mapping1232004006008001000SE +/- 2.32, N = 3SE +/- 3.44, N = 3SE +/- 0.78, N = 3934.59932.31928.721. (CXX) g++ options: -O3 -std=c++11 -fopenmp
OpenBenchmarking.orgTest Cases Per Minute, More Is BetterDarmstadt Automotive Parallel Heterogeneous SuiteBackend: OpenMP - Kernel: NDT Mapping123160320480640800Min: 930.23 / Avg: 934.59 / Max: 938.14Min: 925.72 / Avg: 932.31 / Max: 937.33Min: 927.17 / Avg: 928.72 / Max: 929.591. (CXX) g++ options: -O3 -std=c++11 -fopenmp

OpenBenchmarking.orgTest Cases Per Minute, More Is BetterDarmstadt Automotive Parallel Heterogeneous SuiteBackend: OpenMP - Kernel: Points2Image1237K14K21K28K35KSE +/- 209.24, N = 3SE +/- 161.66, N = 3SE +/- 220.26, N = 331586.9431553.4231274.421. (CXX) g++ options: -O3 -std=c++11 -fopenmp
OpenBenchmarking.orgTest Cases Per Minute, More Is BetterDarmstadt Automotive Parallel Heterogeneous SuiteBackend: OpenMP - Kernel: Points2Image1235K10K15K20K25KMin: 31173.2 / Avg: 31586.94 / Max: 31848.15Min: 31310.83 / Avg: 31553.42 / Max: 31859.82Min: 31011.44 / Avg: 31274.42 / Max: 31711.971. (CXX) g++ options: -O3 -std=c++11 -fopenmp

OpenBenchmarking.orgTest Cases Per Minute, More Is BetterDarmstadt Automotive Parallel Heterogeneous SuiteBackend: OpenMP - Kernel: Euclidean Cluster12330060090012001500SE +/- 7.28, N = 3SE +/- 0.59, N = 3SE +/- 3.69, N = 31480.091468.791474.551. (CXX) g++ options: -O3 -std=c++11 -fopenmp
OpenBenchmarking.orgTest Cases Per Minute, More Is BetterDarmstadt Automotive Parallel Heterogeneous SuiteBackend: OpenMP - Kernel: Euclidean Cluster12330060090012001500Min: 1467.99 / Avg: 1480.09 / Max: 1493.17Min: 1468.11 / Avg: 1468.79 / Max: 1469.95Min: 1467.19 / Avg: 1474.55 / Max: 1478.631. (CXX) g++ options: -O3 -std=c++11 -fopenmp

ECP-CANDLE

The CANDLE benchmark codes implement deep learning architectures relevant to problems in cancer. These architectures address problems at different biological scales, specifically problems at the molecular, cellular and population scales. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterECP-CANDLE 0.3Benchmark: P1B212381624324033.5832.9633.30

OpenBenchmarking.orgSeconds, Fewer Is BetterECP-CANDLE 0.3Benchmark: P3B1123300600900120015001209.671209.601205.34

OpenBenchmarking.orgSeconds, Fewer Is BetterECP-CANDLE 0.3Benchmark: P3B212380160240320400377.73378.34380.47