ssss Intel Core i9-14900K testing with a ASUS PRIME Z790-P WIFI (1662 BIOS) and XFX AMD Radeon RX 7900 XTX 24GB on Ubuntu 24.04 via the Phoronix Test Suite.
HTML result view exported from: https://openbenchmarking.org/result/2408222-PTS-SSSS158023&sor&grw .
ssss Processor Motherboard Chipset Memory Disk Graphics Audio Monitor OS Kernel Desktop Display Server OpenGL Compiler File-System Screen Resolution a b c d e Intel Core i9-14900K @ 5.70GHz (24 Cores / 32 Threads) ASUS PRIME Z790-P WIFI (1662 BIOS) Intel Raptor Lake-S PCH 2 x 16GB DDR5-6000MT/s Corsair CMK32GX5M2B6000C36 Western Digital WD_BLACK SN850X 2000GB XFX AMD Radeon RX 7900 XTX 24GB Realtek ALC897 ASUS VP28U Ubuntu 24.04 6.10.0-061000rc6daily20240706-generic (x86_64) GNOME Shell 46.0 X Server 1.21.1.11 + Wayland 4.6 Mesa 24.2~git2407080600.801ed4~oibaf~n (git-801ed4d 2024-07-08 noble-oibaf-ppa) (LLVM 17.0.6 DRM 3.57) GCC 13.2.0 ext4 3840x2160 OpenBenchmarking.org Kernel Details - Transparent Huge Pages: madvise Compiler Details - --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-backtrace --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-defaulted --enable-offload-targets=nvptx-none=/build/gcc-13-uJ7kn6/gcc-13-13.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-13-uJ7kn6/gcc-13-13.2.0/debian/tmp-gcn/usr --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --program-prefix=x86_64-linux-gnu- --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -v Processor Details - Scaling Governor: intel_pstate powersave (EPP: balance_performance) - CPU Microcode: 0x129 - Thermald 2.5.6 Python Details - Python 3.12.3 Security Details - gather_data_sampling: Not affected + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + reg_file_data_sampling: Mitigation of Clear Register File + retbleed: Not affected + 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 / Automatic IBRS; IBPB: conditional; RSB filling; PBRSB-eIBRS: SW sequence; BHI: BHI_DIS_S + srbds: Not affected + tsx_async_abort: Not affected
ssss onnx: GPT-2 - CPU - Parallel onnx: GPT-2 - CPU - Standard onnx: yolov4 - CPU - Parallel onnx: yolov4 - CPU - Standard onnx: T5 Encoder - CPU - Parallel onnx: T5 Encoder - CPU - Standard onnx: bertsquad-12 - CPU - Parallel onnx: bertsquad-12 - CPU - Standard onnx: CaffeNet 12-int8 - CPU - Parallel onnx: CaffeNet 12-int8 - CPU - Standard onnx: fcn-resnet101-11 - CPU - Parallel onnx: fcn-resnet101-11 - CPU - Standard onnx: ArcFace ResNet-100 - CPU - Parallel onnx: ArcFace ResNet-100 - CPU - Standard onnx: ResNet50 v1-12-int8 - CPU - Parallel onnx: ResNet50 v1-12-int8 - CPU - Standard onnx: super-resolution-10 - CPU - Parallel onnx: super-resolution-10 - CPU - Standard onnx: ResNet101_DUC_HDC-12 - CPU - Parallel onnx: ResNet101_DUC_HDC-12 - CPU - Standard onnx: Faster R-CNN R-50-FPN-int8 - CPU - Parallel onnx: Faster R-CNN R-50-FPN-int8 - CPU - Standard svt-av1: Preset 3 - Bosphorus 4K svt-av1: Preset 5 - Bosphorus 4K svt-av1: Preset 8 - Bosphorus 4K svt-av1: Preset 13 - Bosphorus 4K svt-av1: Preset 3 - Bosphorus 1080p svt-av1: Preset 5 - Bosphorus 1080p svt-av1: Preset 8 - Bosphorus 1080p svt-av1: Preset 13 - Bosphorus 1080p svt-av1: Preset 3 - Beauty 4K 10-bit svt-av1: Preset 5 - Beauty 4K 10-bit svt-av1: Preset 8 - Beauty 4K 10-bit svt-av1: Preset 13 - Beauty 4K 10-bit onnx: GPT-2 - CPU - Parallel onnx: GPT-2 - CPU - Standard onnx: yolov4 - CPU - Parallel onnx: yolov4 - CPU - Standard onnx: T5 Encoder - CPU - Parallel onnx: T5 Encoder - CPU - Standard onnx: bertsquad-12 - CPU - Parallel onnx: bertsquad-12 - CPU - Standard onnx: CaffeNet 12-int8 - CPU - Parallel onnx: CaffeNet 12-int8 - CPU - Standard onnx: fcn-resnet101-11 - CPU - Parallel onnx: fcn-resnet101-11 - CPU - Standard onnx: ArcFace ResNet-100 - CPU - Parallel onnx: ArcFace ResNet-100 - CPU - Standard onnx: ResNet50 v1-12-int8 - CPU - Parallel onnx: ResNet50 v1-12-int8 - CPU - Standard onnx: super-resolution-10 - CPU - Parallel onnx: super-resolution-10 - CPU - Standard onnx: ResNet101_DUC_HDC-12 - CPU - Parallel onnx: ResNet101_DUC_HDC-12 - CPU - Standard onnx: Faster R-CNN R-50-FPN-int8 - CPU - Parallel onnx: Faster R-CNN R-50-FPN-int8 - CPU - Standard a b c d e 118.382 176.398 14.0174 17.2377 144.924 209.093 17.3545 21.7574 313.191 972.397 2.03157 2.80524 9.63175 9.49206 194.152 315.352 96.1880 86.9976 1.007155 1.11819 46.6357 59.5411 7.451 27.269 58.880 227.364 23.690 82.460 187.970 688.064 1.330 5.937 8.359 17.995 8.44437 5.66499 71.3665 58.0153 6.90233 4.78134 57.6288 45.9658 3.19209 1.02774 492.383 356.477 104.5014 105.350 5.15165 3.17050 10.3968 11.4946 993.327 894.328 21.4419 16.7942 125.514 176.609 14.9301 17.6406 141.471 209.048 17.7094 21.893 310.172 965.837 1.98904 2.81097 9.05554 9.47765 200.329 313.466 95.6044 86.6082 0.978795 1.12367 45.2622 60.0199 7.478 27.592 59.44 228.324 23.741 83.754 188.994 693.231 1.324 5.88 8.501 18.022 7.96304 5.65885 66.9766 56.6858 7.06699 4.78224 56.4661 45.675 3.22287 1.03469 502.753 355.747 110.428 105.51 4.9909 3.18971 10.4591 11.5458 1021.66 889.875 22.0917 16.6593 114.763 176.609 15.7981 17.1352 136.25 209.112 16.361 22.0142 312.619 972.911 2.0198 2.79435 9.19721 9.4962 200.771 315.739 94.8399 87.0258 1.04145 1.12825 46.7177 58.6053 7.442 27.388 59.241 227.363 23.809 82.454 190.825 696.738 1.335 5.919 8.485 18.015 8.70901 5.65828 63.296 58.3579 7.33799 4.78108 61.1198 45.4239 3.19755 1.02726 495.095 357.864 108.727 105.304 4.97989 3.16663 10.5436 11.4904 960.202 886.325 21.4036 17.0618 120.672 176.041 15.08 17.4064 146.627 209.89 17.458 22.0779 313.696 965.116 2.02126 2.80968 8.87924 9.49065 196.612 316.774 94.4282 87.2427 0.999466 1.12563 45.5834 59.1651 7.474 27.584 60.083 226.982 23.702 83.754 187.625 697.158 1.33 5.991 8.514 17.982 8.28234 5.67701 66.3103 57.4486 6.8187 4.76332 57.2785 45.2925 3.18653 1.0355 494.738 355.91 112.621 105.366 5.08514 3.15625 10.5896 11.4619 1000.53 888.389 21.9361 16.9005 121.398 176.424 13.4955 17.3838 138.84 209.306 18.156 21.6207 307.619 960.341 2.07055 2.79826 9.64652 9.49647 192.686 312.486 93.5546 87.4288 0.96783 1.12443 46.1047 60.2025 7.446 27.307 58.834 227.699 23.639 82.218 190.467 700.094 1.32 5.906 8.568 18.03 8.23308 5.66489 74.0973 57.5235 7.20087 4.7765 55.0761 46.25 3.24962 1.04055 482.961 357.363 103.663 105.301 5.18875 3.19967 10.6885 11.4375 1033.24 889.34 21.6882 16.609 OpenBenchmarking.org
ONNX Runtime Model: GPT-2 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: GPT-2 - Device: CPU - Executor: Parallel b e d a c 30 60 90 120 150 SE +/- 1.21, N = 3 125.51 121.40 120.67 118.38 114.76 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: GPT-2 - Device: CPU - Executor: Standard OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: GPT-2 - Device: CPU - Executor: Standard c b e a d 40 80 120 160 200 SE +/- 0.34, N = 3 176.61 176.61 176.42 176.40 176.04 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: yolov4 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: yolov4 - Device: CPU - Executor: Parallel c d b a e 4 8 12 16 20 SE +/- 0.14, N = 5 15.80 15.08 14.93 14.02 13.50 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: yolov4 - Device: CPU - Executor: Standard OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: yolov4 - Device: CPU - Executor: Standard b d e a c 4 8 12 16 20 SE +/- 0.11, N = 3 17.64 17.41 17.38 17.24 17.14 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: T5 Encoder - Device: CPU - Executor: Parallel OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: T5 Encoder - Device: CPU - Executor: Parallel d a b e c 30 60 90 120 150 SE +/- 1.25, N = 8 146.63 144.92 141.47 138.84 136.25 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: T5 Encoder - Device: CPU - Executor: Standard OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: T5 Encoder - Device: CPU - Executor: Standard d e c a b 50 100 150 200 250 SE +/- 0.23, N = 3 209.89 209.31 209.11 209.09 209.05 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: bertsquad-12 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: bertsquad-12 - Device: CPU - Executor: Parallel e b d a c 4 8 12 16 20 SE +/- 0.15, N = 3 18.16 17.71 17.46 17.35 16.36 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: bertsquad-12 - Device: CPU - Executor: Standard OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: bertsquad-12 - Device: CPU - Executor: Standard d c b a e 5 10 15 20 25 SE +/- 0.18, N = 3 22.08 22.01 21.89 21.76 21.62 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: CaffeNet 12-int8 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: CaffeNet 12-int8 - Device: CPU - Executor: Parallel d a c b e 70 140 210 280 350 SE +/- 2.57, N = 3 313.70 313.19 312.62 310.17 307.62 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: CaffeNet 12-int8 - Device: CPU - Executor: Standard OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: CaffeNet 12-int8 - Device: CPU - Executor: Standard c a b d e 200 400 600 800 1000 SE +/- 5.46, N = 3 972.91 972.40 965.84 965.12 960.34 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: fcn-resnet101-11 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: fcn-resnet101-11 - Device: CPU - Executor: Parallel e a d c b 0.4659 0.9318 1.3977 1.8636 2.3295 SE +/- 0.02543, N = 3 2.07055 2.03157 2.02126 2.01980 1.98904 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: fcn-resnet101-11 - Device: CPU - Executor: Standard OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: fcn-resnet101-11 - Device: CPU - Executor: Standard b d a e c 0.6325 1.265 1.8975 2.53 3.1625 SE +/- 0.00600, N = 3 2.81097 2.80968 2.80524 2.79826 2.79435 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: ArcFace ResNet-100 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: ArcFace ResNet-100 - Device: CPU - Executor: Parallel e a c b d 3 6 9 12 15 SE +/- 0.21792, N = 15 9.64652 9.63175 9.19721 9.05554 8.87924 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: ArcFace ResNet-100 - Device: CPU - Executor: Standard OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: ArcFace ResNet-100 - Device: CPU - Executor: Standard e c a d b 3 6 9 12 15 SE +/- 0.00338, N = 3 9.49647 9.49620 9.49206 9.49065 9.47765 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Parallel c b d a e 40 80 120 160 200 SE +/- 1.93, N = 5 200.77 200.33 196.61 194.15 192.69 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Standard OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Standard d c a b e 70 140 210 280 350 SE +/- 0.27, N = 3 316.77 315.74 315.35 313.47 312.49 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: super-resolution-10 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: super-resolution-10 - Device: CPU - Executor: Parallel a b c d e 20 40 60 80 100 SE +/- 0.71, N = 3 96.19 95.60 94.84 94.43 93.55 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: super-resolution-10 - Device: CPU - Executor: Standard OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: super-resolution-10 - Device: CPU - Executor: Standard e d c a b 20 40 60 80 100 SE +/- 0.39, N = 3 87.43 87.24 87.03 87.00 86.61 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: ResNet101_DUC_HDC-12 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: ResNet101_DUC_HDC-12 - Device: CPU - Executor: Parallel c a d b e 0.2343 0.4686 0.7029 0.9372 1.1715 SE +/- 0.010507, N = 5 1.041450 1.007155 0.999466 0.978795 0.967830 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: ResNet101_DUC_HDC-12 - Device: CPU - Executor: Standard OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: ResNet101_DUC_HDC-12 - Device: CPU - Executor: Standard c d e b a 0.2539 0.5078 0.7617 1.0156 1.2695 SE +/- 0.00471, N = 3 1.12825 1.12563 1.12443 1.12367 1.11819 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: Faster R-CNN R-50-FPN-int8 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: Faster R-CNN R-50-FPN-int8 - Device: CPU - Executor: Parallel c a e d b 11 22 33 44 55 SE +/- 0.18, N = 3 46.72 46.64 46.10 45.58 45.26 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: Faster R-CNN R-50-FPN-int8 - Device: CPU - Executor: Standard OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: Faster R-CNN R-50-FPN-int8 - Device: CPU - Executor: Standard e b a d c 13 26 39 52 65 SE +/- 0.28, N = 3 60.20 60.02 59.54 59.17 58.61 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
SVT-AV1 Encoder Mode: Preset 3 - Input: Bosphorus 4K OpenBenchmarking.org Frames Per Second, More Is Better SVT-AV1 2.2 Encoder Mode: Preset 3 - Input: Bosphorus 4K b d a e c 2 4 6 8 10 SE +/- 0.014, N = 3 7.478 7.474 7.451 7.446 7.442 1. (CXX) g++ options: -march=native -mno-avx
SVT-AV1 Encoder Mode: Preset 5 - Input: Bosphorus 4K OpenBenchmarking.org Frames Per Second, More Is Better SVT-AV1 2.2 Encoder Mode: Preset 5 - Input: Bosphorus 4K b d c e a 6 12 18 24 30 SE +/- 0.03, N = 3 27.59 27.58 27.39 27.31 27.27 1. (CXX) g++ options: -march=native -mno-avx
SVT-AV1 Encoder Mode: Preset 8 - Input: Bosphorus 4K OpenBenchmarking.org Frames Per Second, More Is Better SVT-AV1 2.2 Encoder Mode: Preset 8 - Input: Bosphorus 4K d b c a e 13 26 39 52 65 SE +/- 0.23, N = 3 60.08 59.44 59.24 58.88 58.83 1. (CXX) g++ options: -march=native -mno-avx
SVT-AV1 Encoder Mode: Preset 13 - Input: Bosphorus 4K OpenBenchmarking.org Frames Per Second, More Is Better SVT-AV1 2.2 Encoder Mode: Preset 13 - Input: Bosphorus 4K b e a c d 50 100 150 200 250 SE +/- 0.72, N = 3 228.32 227.70 227.36 227.36 226.98 1. (CXX) g++ options: -march=native -mno-avx
SVT-AV1 Encoder Mode: Preset 3 - Input: Bosphorus 1080p OpenBenchmarking.org Frames Per Second, More Is Better SVT-AV1 2.2 Encoder Mode: Preset 3 - Input: Bosphorus 1080p c b d a e 6 12 18 24 30 SE +/- 0.04, N = 3 23.81 23.74 23.70 23.69 23.64 1. (CXX) g++ options: -march=native -mno-avx
SVT-AV1 Encoder Mode: Preset 5 - Input: Bosphorus 1080p OpenBenchmarking.org Frames Per Second, More Is Better SVT-AV1 2.2 Encoder Mode: Preset 5 - Input: Bosphorus 1080p d b a c e 20 40 60 80 100 SE +/- 0.23, N = 3 83.75 83.75 82.46 82.45 82.22 1. (CXX) g++ options: -march=native -mno-avx
SVT-AV1 Encoder Mode: Preset 8 - Input: Bosphorus 1080p OpenBenchmarking.org Frames Per Second, More Is Better SVT-AV1 2.2 Encoder Mode: Preset 8 - Input: Bosphorus 1080p c e b a d 40 80 120 160 200 SE +/- 1.66, N = 3 190.83 190.47 188.99 187.97 187.63 1. (CXX) g++ options: -march=native -mno-avx
SVT-AV1 Encoder Mode: Preset 13 - Input: Bosphorus 1080p OpenBenchmarking.org Frames Per Second, More Is Better SVT-AV1 2.2 Encoder Mode: Preset 13 - Input: Bosphorus 1080p e d c b a 150 300 450 600 750 SE +/- 5.44, N = 3 700.09 697.16 696.74 693.23 688.06 1. (CXX) g++ options: -march=native -mno-avx
SVT-AV1 Encoder Mode: Preset 3 - Input: Beauty 4K 10-bit OpenBenchmarking.org Frames Per Second, More Is Better SVT-AV1 2.2 Encoder Mode: Preset 3 - Input: Beauty 4K 10-bit c d a b e 0.3004 0.6008 0.9012 1.2016 1.502 SE +/- 0.004, N = 3 1.335 1.330 1.330 1.324 1.320 1. (CXX) g++ options: -march=native -mno-avx
SVT-AV1 Encoder Mode: Preset 5 - Input: Beauty 4K 10-bit OpenBenchmarking.org Frames Per Second, More Is Better SVT-AV1 2.2 Encoder Mode: Preset 5 - Input: Beauty 4K 10-bit d a c e b 1.348 2.696 4.044 5.392 6.74 SE +/- 0.023, N = 3 5.991 5.937 5.919 5.906 5.880 1. (CXX) g++ options: -march=native -mno-avx
SVT-AV1 Encoder Mode: Preset 8 - Input: Beauty 4K 10-bit OpenBenchmarking.org Frames Per Second, More Is Better SVT-AV1 2.2 Encoder Mode: Preset 8 - Input: Beauty 4K 10-bit e d b c a 2 4 6 8 10 SE +/- 0.048, N = 3 8.568 8.514 8.501 8.485 8.359 1. (CXX) g++ options: -march=native -mno-avx
SVT-AV1 Encoder Mode: Preset 13 - Input: Beauty 4K 10-bit OpenBenchmarking.org Frames Per Second, More Is Better SVT-AV1 2.2 Encoder Mode: Preset 13 - Input: Beauty 4K 10-bit e b c a d 4 8 12 16 20 SE +/- 0.03, N = 3 18.03 18.02 18.02 18.00 17.98 1. (CXX) g++ options: -march=native -mno-avx
ONNX Runtime Model: GPT-2 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: GPT-2 - Device: CPU - Executor: Parallel b e d a c 2 4 6 8 10 SE +/- 0.08654, N = 3 7.96304 8.23308 8.28234 8.44437 8.70901 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: GPT-2 - Device: CPU - Executor: Standard OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: GPT-2 - Device: CPU - Executor: Standard c b e a d 1.2773 2.5546 3.8319 5.1092 6.3865 SE +/- 0.01074, N = 3 5.65828 5.65885 5.66489 5.66499 5.67701 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: yolov4 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: yolov4 - Device: CPU - Executor: Parallel c d b a e 16 32 48 64 80 SE +/- 0.73, N = 5 63.30 66.31 66.98 71.37 74.10 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: yolov4 - Device: CPU - Executor: Standard OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: yolov4 - Device: CPU - Executor: Standard b d e a c 13 26 39 52 65 SE +/- 0.36, N = 3 56.69 57.45 57.52 58.02 58.36 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: T5 Encoder - Device: CPU - Executor: Parallel OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: T5 Encoder - Device: CPU - Executor: Parallel d a b e c 2 4 6 8 10 SE +/- 0.05998, N = 8 6.81870 6.90233 7.06699 7.20087 7.33799 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: T5 Encoder - Device: CPU - Executor: Standard OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: T5 Encoder - Device: CPU - Executor: Standard d e c a b 1.076 2.152 3.228 4.304 5.38 SE +/- 0.00534, N = 3 4.76332 4.77650 4.78108 4.78134 4.78224 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: bertsquad-12 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: bertsquad-12 - Device: CPU - Executor: Parallel e b d a c 14 28 42 56 70 SE +/- 0.49, N = 3 55.08 56.47 57.28 57.63 61.12 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: bertsquad-12 - Device: CPU - Executor: Standard OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: bertsquad-12 - Device: CPU - Executor: Standard d c b a e 10 20 30 40 50 SE +/- 0.37, N = 3 45.29 45.42 45.68 45.97 46.25 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: CaffeNet 12-int8 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: CaffeNet 12-int8 - Device: CPU - Executor: Parallel d a c b e 0.7312 1.4624 2.1936 2.9248 3.656 SE +/- 0.02636, N = 3 3.18653 3.19209 3.19755 3.22287 3.24962 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: CaffeNet 12-int8 - Device: CPU - Executor: Standard OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: CaffeNet 12-int8 - Device: CPU - Executor: Standard c a b d e 0.2341 0.4682 0.7023 0.9364 1.1705 SE +/- 0.00581, N = 3 1.02726 1.02774 1.03469 1.03550 1.04055 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: fcn-resnet101-11 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: fcn-resnet101-11 - Device: CPU - Executor: Parallel e a d c b 110 220 330 440 550 SE +/- 6.17, N = 3 482.96 492.38 494.74 495.10 502.75 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: fcn-resnet101-11 - Device: CPU - Executor: Standard OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: fcn-resnet101-11 - Device: CPU - Executor: Standard b d a e c 80 160 240 320 400 SE +/- 0.76, N = 3 355.75 355.91 356.48 357.36 357.86 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: ArcFace ResNet-100 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: ArcFace ResNet-100 - Device: CPU - Executor: Parallel e a c b d 30 60 90 120 150 SE +/- 2.15, N = 15 103.66 104.50 108.73 110.43 112.62 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: ArcFace ResNet-100 - Device: CPU - Executor: Standard OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: ArcFace ResNet-100 - Device: CPU - Executor: Standard e c a d b 20 40 60 80 100 SE +/- 0.04, N = 3 105.30 105.30 105.35 105.37 105.51 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Parallel c b d a e 1.1675 2.335 3.5025 4.67 5.8375 SE +/- 0.05176, N = 5 4.97989 4.99090 5.08514 5.15165 5.18875 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Standard OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Standard d c a b e 0.7199 1.4398 2.1597 2.8796 3.5995 SE +/- 0.00266, N = 3 3.15625 3.16663 3.17050 3.18971 3.19967 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: super-resolution-10 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: super-resolution-10 - Device: CPU - Executor: Parallel a b c d e 3 6 9 12 15 SE +/- 0.08, N = 3 10.40 10.46 10.54 10.59 10.69 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: super-resolution-10 - Device: CPU - Executor: Standard OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: super-resolution-10 - Device: CPU - Executor: Standard e d c a b 3 6 9 12 15 SE +/- 0.05, N = 3 11.44 11.46 11.49 11.49 11.55 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: ResNet101_DUC_HDC-12 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: ResNet101_DUC_HDC-12 - Device: CPU - Executor: Parallel c a d b e 200 400 600 800 1000 SE +/- 10.36, N = 5 960.20 993.33 1000.53 1021.66 1033.24 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: ResNet101_DUC_HDC-12 - Device: CPU - Executor: Standard OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: ResNet101_DUC_HDC-12 - Device: CPU - Executor: Standard c d e b a 200 400 600 800 1000 SE +/- 3.76, N = 3 886.33 888.39 889.34 889.88 894.33 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: Faster R-CNN R-50-FPN-int8 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: Faster R-CNN R-50-FPN-int8 - Device: CPU - Executor: Parallel c a e d b 5 10 15 20 25 SE +/- 0.08, N = 3 21.40 21.44 21.69 21.94 22.09 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: Faster R-CNN R-50-FPN-int8 - Device: CPU - Executor: Standard OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: Faster R-CNN R-50-FPN-int8 - Device: CPU - Executor: Standard e b a d c 4 8 12 16 20 SE +/- 0.08, N = 3 16.61 16.66 16.79 16.90 17.06 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
Phoronix Test Suite v10.8.5