Ryzen 9 3900X + TITAN RTX AMD Ryzen 9 3900X 12-Core testing with a ASUS ROG CROSSHAIR VIII HERO (WI-FI) (1001 BIOS) and NVIDIA TITAN RTX 24GB on Ubuntu 19.04 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 1910024-PTS-RYZEN93938 TITAN RTX + 3900X Processor: AMD Ryzen 9 3900X 12-Core @ 3.80GHz (12 Cores / 24 Threads), Motherboard: ASUS ROG CROSSHAIR VIII HERO (WI-FI) (1001 BIOS), Chipset: AMD Device 1480, Memory: 16384MB, Disk: Samsung SSD 970 EVO 250GB, Graphics: NVIDIA TITAN RTX 24GB (1350/7000MHz), Audio: NVIDIA TU102 HD Audio, Monitor: ASUS VP28U, Network: Realtek Device 8125 + Intel I211 + Intel Device 2723
OS: Ubuntu 19.04, Kernel: 5.0.0-29-generic (x86_64), Desktop: GNOME Shell 3.32.2, Display Server: X Server 1.20.4, Display Driver: NVIDIA 435.21, OpenGL: 4.6.0, Compiler: GCC 8.3.0, File-System: ext4, Screen Resolution: 3840x2160
Compiler Notes: --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-bootstrap --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++ --enable-libmpx --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-targets=nvptx-none --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 --with-tune=generic --without-cuda-driver -vProcessor Notes: Scaling Governor: acpi-cpufreq ondemandOpenCL Notes: GPU Compute Cores: 4608Security Notes: 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 STIBP: always-on RSB filling
Ryzen 9 3900X + TITAN RTX OpenBenchmarking.org Phoronix Test Suite AMD Ryzen 9 3900X 12-Core @ 3.80GHz (12 Cores / 24 Threads) ASUS ROG CROSSHAIR VIII HERO (WI-FI) (1001 BIOS) AMD Device 1480 16384MB Samsung SSD 970 EVO 250GB NVIDIA TITAN RTX 24GB (1350/7000MHz) NVIDIA TU102 HD Audio ASUS VP28U Realtek Device 8125 + Intel I211 + Intel Device 2723 Ubuntu 19.04 5.0.0-29-generic (x86_64) GNOME Shell 3.32.2 X Server 1.20.4 NVIDIA 435.21 4.6.0 GCC 8.3.0 ext4 3840x2160 Processor Motherboard Chipset Memory Disk Graphics Audio Monitor Network OS Kernel Desktop Display Server Display Driver OpenGL Compiler File-System Screen Resolution Ryzen 9 3900X + TITAN RTX Benchmarks System Logs - --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-bootstrap --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++ --enable-libmpx --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-targets=nvptx-none --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 --with-tune=generic --without-cuda-driver -v - Scaling Governor: acpi-cpufreq ondemand - GPU Compute Cores: 4608 - 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 STIBP: always-on RSB filling
Ryzen 9 3900X + TITAN RTX luxmark: GPU - Luxball HDR luxmark: GPU - Microphone luxmark: GPU - Hotel blender: Pabellon Barcelona - CPU-Only blender: Pabellon Barcelona - OpenCL blender: Barbershop - CPU-Only blender: Fishy Cat - CPU-Only blender: Classroom - CPU-Only blender: Barbershop - OpenCL blender: Fishy Cat - OpenCL blender: Classroom - OpenCL blender: BMW27 - CPU-Only blender: BMW27 - OpenCL tungsten: Volumetric Caustic tungsten: Water Caustic tungsten: Hair luxcorerender: Rainbow Colors and Prism luxcorerender: DLSC oidn: Memorial embree: Pathtracer ISPC - Asian Dragon Obj embree: Pathtracer ISPC - Asian Dragon embree: Pathtracer - Asian Dragon Obj embree: Pathtracer - Asian Dragon embree: Pathtracer ISPC - Crown embree: Pathtracer - Crown ospray: Magnetic Reconnection - Path Tracer ospray: NASA Streamlines - Path Tracer ospray: Magnetic Reconnection - SciVis ospray: XFrog Forest - Path Tracer ospray: NASA Streamlines - SciVis ospray: San Miguel - Path Tracer ospray: XFrog Forest - SciVis ospray: San Miguel - SciVis mkl-dnn: Convolution Batch conv_googlenet_v3 - u8s8f32s32 mkl-dnn: Convolution Batch conv_googlenet_v3 - u8s8u8s32 mkl-dnn: Convolution Batch conv_alexnet - u8s8f32s32 mkl-dnn: Deconvolution Batch deconv_all - u8s8u8s32 mkl-dnn: Deconvolution Batch deconv_3d - u8s8f32s32 mkl-dnn: Deconvolution Batch deconv_1d - u8s8f32s32 mkl-dnn: Convolution Batch conv_alexnet - u8s8u8s32 mkl-dnn: Deconvolution Batch deconv_3d - u8s8u8s32 mkl-dnn: Deconvolution Batch deconv_1d - u8s8u8s32 mkl-dnn: Convolution Batch conv_googlenet_v3 - f32 mkl-dnn: Convolution Batch conv_all - u8s8f32s32 mkl-dnn: Convolution Batch conv_all - u8s8u8s32 mkl-dnn: Convolution Batch conv_3d - u8s8f32s32 mkl-dnn: Convolution Batch conv_3d - u8s8u8s32 mkl-dnn: Deconvolution Batch deconv_all - f32 mkl-dnn: Convolution Batch conv_alexnet - f32 mkl-dnn: Deconvolution Batch deconv_3d - f32 mkl-dnn: Deconvolution Batch deconv_1d - f32 mkl-dnn: Convolution Batch conv_all - f32 mkl-dnn: Convolution Batch conv_3d - f32 mkl-dnn: IP Batch All - u8s8f32s32 mkl-dnn: IP Batch All - u8s8u8s32 mkl-dnn: IP Batch 1D - u8s8f32s32 mkl-dnn: IP Batch 1D - u8s8u8s32 mkl-dnn: IP Batch All - f32 mkl-dnn: IP Batch 1D - f32 namd-cuda: ATPase Simulation - 327,506 Atoms luxcorerender-cl: Rainbow Colors and Prism luxcorerender-cl: LuxCore Benchmark luxcorerender-cl: Food luxcorerender-cl: DLSC shoc: OpenCL - Texture Read Bandwidth shoc: OpenCL - Bus Speed Readback shoc: OpenCL - Bus Speed Download shoc: OpenCL - Max SP Flops shoc: OpenCL - MD5 Hash shoc: OpenCL - FFT SP shoc: OpenCL - Triad tungsten: Non-Exponential TITAN RTX + 3900X 45932 30616 9878 389.82 864.11 461.91 165.70 316.13 503.59 755.55 407.46 113.13 295.88 7.42 23.77 17.46 2.27 2.28 10.43 14.19 16.48 14.80 16.53 14.71 15.36 200 5.55 12.99 1.88 27.53 1.46 3.58 19.23 1613.96 1619.40 3644.65 31708.60 5522.18 3120.77 3664.46 6065.02 3550.45 111.50 39883.63 39829.47 9881.34 9854.17 6144.28 254.56 5.02 24.46 2088.81 18.78 743.05 741.05 67.47 66.97 209.96 17.36 0.17939 16.12 7.51 2.99 9.64 1165.82 13.54 13.14 17423.93 37.51 1570.13 12.89 6.85 OpenBenchmarking.org
LuxMark LuxMark is a multi-platform OpenGL benchmark using LuxRender. LuxMark supports targeting different OpenCL devices and has multiple scenes available for rendering. LuxMark is a fully open-source OpenCL program with real-world rendering examples. Learn more via the OpenBenchmarking.org test page.
OpenBenchmarking.org Score, More Is Better LuxMark 3.1 OpenCL Device: GPU - Scene: Luxball HDR TITAN RTX + 3900X 10K 20K 30K 40K 50K SE +/- 7.31, N = 3 45932
OpenBenchmarking.org Seconds, Fewer Is Better Blender 2.80 Blend File: Pabellon Barcelona - Compute: OpenCL TITAN RTX + 3900X 200 400 600 800 1000 SE +/- 7.65, N = 3 864.11
Tungsten Renderer Tungsten is a C++ physically based renderer that makes use of Intel's Embree ray tracing library. Learn more via the OpenBenchmarking.org test page.
OpenBenchmarking.org Seconds, Fewer Is Better Tungsten Renderer 0.2.2 Scene: Volumetric Caustic TITAN RTX + 3900X 2 4 6 8 10 SE +/- 0.01, N = 3 7.42 1. (CXX) g++ options: -std=c++0x -march=znver1 -msse2 -msse3 -mssse3 -msse4.1 -msse4.2 -msse4a -mfma -mbmi2 -mno-avx -mno-avx2 -mno-xop -mno-fma4 -mno-avx512f -mno-avx512vl -mno-avx512pf -mno-avx512er -mno-avx512cd -mno-avx512dq -mno-avx512bw -mno-avx512ifma -mno-avx512vbmi -fstrict-aliasing -O3 -rdynamic -ljpeg -lGL -lGLU -lpthread -ldl
OpenBenchmarking.org Seconds, Fewer Is Better Tungsten Renderer 0.2.2 Scene: Water Caustic TITAN RTX + 3900X 6 12 18 24 30 SE +/- 0.05, N = 3 23.77 1. (CXX) g++ options: -std=c++0x -march=znver1 -msse2 -msse3 -mssse3 -msse4.1 -msse4.2 -msse4a -mfma -mbmi2 -mno-avx -mno-avx2 -mno-xop -mno-fma4 -mno-avx512f -mno-avx512vl -mno-avx512pf -mno-avx512er -mno-avx512cd -mno-avx512dq -mno-avx512bw -mno-avx512ifma -mno-avx512vbmi -fstrict-aliasing -O3 -rdynamic -ljpeg -lGL -lGLU -lpthread -ldl
OpenBenchmarking.org Seconds, Fewer Is Better Tungsten Renderer 0.2.2 Scene: Hair TITAN RTX + 3900X 4 8 12 16 20 SE +/- 0.01, N = 3 17.46 1. (CXX) g++ options: -std=c++0x -march=znver1 -msse2 -msse3 -mssse3 -msse4.1 -msse4.2 -msse4a -mfma -mbmi2 -mno-avx -mno-avx2 -mno-xop -mno-fma4 -mno-avx512f -mno-avx512vl -mno-avx512pf -mno-avx512er -mno-avx512cd -mno-avx512dq -mno-avx512bw -mno-avx512ifma -mno-avx512vbmi -fstrict-aliasing -O3 -rdynamic -ljpeg -lGL -lGLU -lpthread -ldl
OpenBenchmarking.org Frames Per Second, More Is Better Embree 3.6.1 Binary: Pathtracer ISPC - Model: Asian Dragon TITAN RTX + 3900X 4 8 12 16 20 SE +/- 0.01, N = 3 16.48 MIN: 16.37 / MAX: 16.72
OpenBenchmarking.org Frames Per Second, More Is Better Embree 3.6.1 Binary: Pathtracer - Model: Asian Dragon Obj TITAN RTX + 3900X 4 8 12 16 20 SE +/- 0.00, N = 3 14.80 MIN: 14.71 / MAX: 15.07
OpenBenchmarking.org Frames Per Second, More Is Better Embree 3.6.1 Binary: Pathtracer - Model: Asian Dragon TITAN RTX + 3900X 4 8 12 16 20 SE +/- 0.01, N = 3 16.53 MIN: 16.41 / MAX: 16.87
OpenBenchmarking.org Frames Per Second, More Is Better Embree 3.6.1 Binary: Pathtracer ISPC - Model: Crown TITAN RTX + 3900X 4 8 12 16 20 SE +/- 0.01, N = 3 14.71 MIN: 14.59 / MAX: 14.95
OpenBenchmarking.org Frames Per Second, More Is Better Embree 3.6.1 Binary: Pathtracer - Model: Crown TITAN RTX + 3900X 4 8 12 16 20 SE +/- 0.01, N = 3 15.36 MIN: 15.24 / MAX: 15.76
OSPray Intel OSPray is a portable ray-tracing engine for high-performance, high-fidenlity scientific visualizations. OSPray builds off Intel's Embree and Intel SPMD Program Compiler (ISPC) components as part of the oneAPI rendering toolkit. Learn more via the OpenBenchmarking.org test page.
OpenBenchmarking.org FPS, More Is Better OSPray 1.8.5 Demo: Magnetic Reconnection - Renderer: Path Tracer TITAN RTX + 3900X 40 80 120 160 200 200 MIN: 166.67 / MAX: 250
OpenBenchmarking.org FPS, More Is Better OSPray 1.8.5 Demo: NASA Streamlines - Renderer: Path Tracer TITAN RTX + 3900X 1.2488 2.4976 3.7464 4.9952 6.244 SE +/- 0.00, N = 12 5.55 MIN: 5.46 / MAX: 5.62
OpenBenchmarking.org FPS, More Is Better OSPray 1.8.5 Demo: Magnetic Reconnection - Renderer: SciVis TITAN RTX + 3900X 3 6 9 12 15 SE +/- 0.00, N = 12 12.99 MIN: 12.35 / MAX: 13.16
OpenBenchmarking.org FPS, More Is Better OSPray 1.8.5 Demo: XFrog Forest - Renderer: Path Tracer TITAN RTX + 3900X 0.423 0.846 1.269 1.692 2.115 SE +/- 0.00, N = 3 1.88 MIN: 1.87 / MAX: 1.9
OpenBenchmarking.org FPS, More Is Better OSPray 1.8.5 Demo: NASA Streamlines - Renderer: SciVis TITAN RTX + 3900X 6 12 18 24 30 SE +/- 0.25, N = 3 27.53 MIN: 26.32 / MAX: 27.78
OpenBenchmarking.org FPS, More Is Better OSPray 1.8.5 Demo: San Miguel - Renderer: Path Tracer TITAN RTX + 3900X 0.3285 0.657 0.9855 1.314 1.6425 SE +/- 0.00, N = 3 1.46 MIN: 1.42 / MAX: 1.47
OpenBenchmarking.org FPS, More Is Better OSPray 1.8.5 Demo: XFrog Forest - Renderer: SciVis TITAN RTX + 3900X 0.8055 1.611 2.4165 3.222 4.0275 SE +/- 0.00, N = 3 3.58 MIN: 3.51 / MAX: 3.62
MKL-DNN This is a test of the Intel MKL-DNN as the Intel Math Kernel Library for Deep Neural Networks and making use of its built-in benchdnn functionality. The result is the total perf time reported. Learn more via the OpenBenchmarking.org test page.
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: Convolution Batch conv_googlenet_v3 - Data Type: u8s8f32s32 TITAN RTX + 3900X 300 600 900 1200 1500 SE +/- 5.05, N = 3 1613.96 MIN: 1510.52 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: Convolution Batch conv_googlenet_v3 - Data Type: u8s8u8s32 TITAN RTX + 3900X 300 600 900 1200 1500 SE +/- 6.80, N = 3 1619.40 MIN: 1517.87 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: Convolution Batch conv_alexnet - Data Type: u8s8f32s32 TITAN RTX + 3900X 800 1600 2400 3200 4000 SE +/- 28.89, N = 3 3644.65 MIN: 3461.36 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: Deconvolution Batch deconv_all - Data Type: u8s8u8s32 TITAN RTX + 3900X 7K 14K 21K 28K 35K SE +/- 33.55, N = 3 31708.60 MIN: 30559.9 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: Deconvolution Batch deconv_3d - Data Type: u8s8f32s32 TITAN RTX + 3900X 1200 2400 3600 4800 6000 SE +/- 9.29, N = 3 5522.18 MIN: 5491.73 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: Deconvolution Batch deconv_1d - Data Type: u8s8f32s32 TITAN RTX + 3900X 700 1400 2100 2800 3500 SE +/- 5.88, N = 3 3120.77 MIN: 3104.26 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: Convolution Batch conv_alexnet - Data Type: u8s8u8s32 TITAN RTX + 3900X 800 1600 2400 3200 4000 SE +/- 18.68, N = 3 3664.46 MIN: 3507.75 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: Deconvolution Batch deconv_3d - Data Type: u8s8u8s32 TITAN RTX + 3900X 1300 2600 3900 5200 6500 SE +/- 10.64, N = 3 6065.02 MIN: 6030.44 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: Deconvolution Batch deconv_1d - Data Type: u8s8u8s32 TITAN RTX + 3900X 800 1600 2400 3200 4000 SE +/- 5.47, N = 3 3550.45 MIN: 3538.57 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: Convolution Batch conv_googlenet_v3 - Data Type: f32 TITAN RTX + 3900X 20 40 60 80 100 SE +/- 0.32, N = 3 111.50 MIN: 109.56 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: Convolution Batch conv_all - Data Type: u8s8f32s32 TITAN RTX + 3900X 9K 18K 27K 36K 45K SE +/- 20.25, N = 3 39883.63 MIN: 38845.9 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: Convolution Batch conv_all - Data Type: u8s8u8s32 TITAN RTX + 3900X 9K 18K 27K 36K 45K SE +/- 76.66, N = 3 39829.47 MIN: 38802.2 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: Convolution Batch conv_3d - Data Type: u8s8f32s32 TITAN RTX + 3900X 2K 4K 6K 8K 10K SE +/- 4.48, N = 3 9881.34 MIN: 9857.42 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: Convolution Batch conv_3d - Data Type: u8s8u8s32 TITAN RTX + 3900X 2K 4K 6K 8K 10K SE +/- 11.69, N = 3 9854.17 MIN: 9819.92 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: Deconvolution Batch deconv_all - Data Type: f32 TITAN RTX + 3900X 1300 2600 3900 5200 6500 SE +/- 15.46, N = 3 6144.28 MIN: 5735.21 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: Convolution Batch conv_alexnet - Data Type: f32 TITAN RTX + 3900X 60 120 180 240 300 SE +/- 2.91, N = 3 254.56 MIN: 249.87 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: Deconvolution Batch deconv_3d - Data Type: f32 TITAN RTX + 3900X 1.1295 2.259 3.3885 4.518 5.6475 SE +/- 0.00, N = 4 5.02 MIN: 4.93 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: Deconvolution Batch deconv_1d - Data Type: f32 TITAN RTX + 3900X 6 12 18 24 30 SE +/- 0.12, N = 3 24.46 MIN: 23.39 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: Convolution Batch conv_all - Data Type: f32 TITAN RTX + 3900X 400 800 1200 1600 2000 SE +/- 4.85, N = 3 2088.81 MIN: 2068.35 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: Convolution Batch conv_3d - Data Type: f32 TITAN RTX + 3900X 5 10 15 20 25 SE +/- 0.11, N = 3 18.78 MIN: 18.22 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: IP Batch All - Data Type: u8s8f32s32 TITAN RTX + 3900X 160 320 480 640 800 SE +/- 1.14, N = 3 743.05 MIN: 660.93 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: IP Batch All - Data Type: u8s8u8s32 TITAN RTX + 3900X 160 320 480 640 800 SE +/- 2.75, N = 3 741.05 MIN: 646.26 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: IP Batch 1D - Data Type: u8s8f32s32 TITAN RTX + 3900X 15 30 45 60 75 SE +/- 0.48, N = 3 67.47 MIN: 50.27 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: IP Batch 1D - Data Type: u8s8u8s32 TITAN RTX + 3900X 15 30 45 60 75 SE +/- 0.54, N = 3 66.97 MIN: 53.15 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: IP Batch All - Data Type: f32 TITAN RTX + 3900X 50 100 150 200 250 SE +/- 0.82, N = 3 209.96 MIN: 167.07 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
OpenBenchmarking.org ms, Fewer Is Better MKL-DNN 2019-04-16 Harness: IP Batch 1D - Data Type: f32 TITAN RTX + 3900X 4 8 12 16 20 SE +/- 0.04, N = 3 17.36 MIN: 11.17 1. (CXX) g++ options: -std=c++11 -march=native -mtune=native -fPIC -fopenmp -O3 -pie -lmklml_intel -ldl
NAMD CUDA NAMD is a parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems. NAMD was developed by the Theoretical and Computational Biophysics Group in the Beckman Institute for Advanced Science and Technology at the University of Illinois at Urbana-Champaign. This version of the NAMD test profile uses CUDA GPU acceleration. Learn more via the OpenBenchmarking.org test page.
OpenBenchmarking.org days/ns, Fewer Is Better NAMD CUDA 2.13 ATPase Simulation - 327,506 Atoms TITAN RTX + 3900X 0.0404 0.0808 0.1212 0.1616 0.202 SE +/- 0.00039, N = 12 0.17939
LuxCoreRender OpenCL LuxCoreRender is an open-source physically based renderer. This test profile is focused on running LuxCoreRender on OpenCL accelerators/GPUs. The alternative luxcorerender test profile is for CPU execution due to a difference in tests, etc. Learn more via the OpenBenchmarking.org test page.
OpenBenchmarking.org M samples/sec, More Is Better LuxCoreRender OpenCL 2.2 Scene: Rainbow Colors and Prism TITAN RTX + 3900X 4 8 12 16 20 SE +/- 0.02, N = 3 16.12 MIN: 14.52 / MAX: 16.69
Tungsten Renderer Tungsten is a C++ physically based renderer that makes use of Intel's Embree ray tracing library. Learn more via the OpenBenchmarking.org test page.
OpenBenchmarking.org Seconds, Fewer Is Better Tungsten Renderer 0.2.2 Scene: Non-Exponential TITAN RTX + 3900X 2 4 6 8 10 SE +/- 0.12, N = 15 6.85 1. (CXX) g++ options: -std=c++0x -march=znver1 -msse2 -msse3 -mssse3 -msse4.1 -msse4.2 -msse4a -mfma -mbmi2 -mno-avx -mno-avx2 -mno-xop -mno-fma4 -mno-avx512f -mno-avx512vl -mno-avx512pf -mno-avx512er -mno-avx512cd -mno-avx512dq -mno-avx512bw -mno-avx512ifma -mno-avx512vbmi -fstrict-aliasing -O3 -rdynamic -ljpeg -lGL -lGLU -lpthread -ldl
TITAN RTX + 3900X Processor: AMD Ryzen 9 3900X 12-Core @ 3.80GHz (12 Cores / 24 Threads), Motherboard: ASUS ROG CROSSHAIR VIII HERO (WI-FI) (1001 BIOS), Chipset: AMD Device 1480, Memory: 16384MB, Disk: Samsung SSD 970 EVO 250GB, Graphics: NVIDIA TITAN RTX 24GB (1350/7000MHz), Audio: NVIDIA TU102 HD Audio, Monitor: ASUS VP28U, Network: Realtek Device 8125 + Intel I211 + Intel Device 2723
OS: Ubuntu 19.04, Kernel: 5.0.0-29-generic (x86_64), Desktop: GNOME Shell 3.32.2, Display Server: X Server 1.20.4, Display Driver: NVIDIA 435.21, OpenGL: 4.6.0, Compiler: GCC 8.3.0, File-System: ext4, Screen Resolution: 3840x2160
Compiler Notes: --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-bootstrap --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++ --enable-libmpx --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-targets=nvptx-none --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 --with-tune=generic --without-cuda-driver -vProcessor Notes: Scaling Governor: acpi-cpufreq ondemandOpenCL Notes: GPU Compute Cores: 4608Security Notes: 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 STIBP: always-on RSB filling
Testing initiated at 1 October 2019 21:12 by user pts.