ldld Tests for a future article. Intel Core Ultra 7 155H testing with a MTL Swift SFG14-72T Coral_MTH (V1.01 BIOS) and Intel Arc MTL 8GB on Ubuntu 23.10 via the Phoronix Test Suite.
HTML result view exported from: https://openbenchmarking.org/result/2403270-NE-LDLD0728551&grw&sor .
ldld Processor Motherboard Chipset Memory Disk Graphics Audio Network OS Kernel Desktop Display Server OpenGL Compiler File-System Screen Resolution a b Intel Core Ultra 7 155H @ 4.80GHz (16 Cores / 22 Threads) MTL Swift SFG14-72T Coral_MTH (V1.01 BIOS) Intel Device 7e7f 8 x 2GB DRAM-6400MT/s Micron MT62F1G32D2DS-026 1024GB Micron_2550_MTFDKBA1T0TGE Intel Arc MTL 8GB (2250MHz) Intel Meteor Lake-P HD Audio Intel Device 7e40 Ubuntu 23.10 6.8.0-060800rc1daily20240126-generic (x86_64) GNOME Shell 45.2 X Server 1.21.1.7 + Wayland 4.6 Mesa 24.1~git2401200600.ebcab1~oibaf~m (git-ebcab14 2024-01-20 mantic-oibaf-ppa) GCC 13.2.0 ext4 1920x1200 OpenBenchmarking.org Kernel Details - Transparent Huge Pages: madvise Compiler Details - --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,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-defaulted --enable-offload-targets=nvptx-none=/build/gcc-13-XYspKM/gcc-13-13.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-13-XYspKM/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-build-config=bootstrap-lto-lean --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -v Processor Details - Scaling Governor: intel_pstate powersave (EPP: balance_performance) - CPU Microcode: 0x13 - Thermald 2.5.4 Python Details - Python 3.11.6 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 + 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: Not affected + srbds: Not affected + tsx_async_abort: Not affected
ldld tensorflow: CPU - 1 - AlexNet tensorflow: CPU - 16 - AlexNet tensorflow: CPU - 32 - AlexNet tensorflow: CPU - 64 - AlexNet tensorflow: CPU - 1 - GoogLeNet tensorflow: CPU - 1 - ResNet-50 tensorflow: CPU - 16 - GoogLeNet tensorflow: CPU - 16 - ResNet-50 tensorflow: CPU - 32 - GoogLeNet tensorflow: CPU - 32 - ResNet-50 tensorflow: CPU - 64 - GoogLeNet tensorflow: CPU - 64 - ResNet-50 pytorch: CPU - 1 - ResNet-50 pytorch: CPU - 1 - ResNet-152 pytorch: CPU - 16 - ResNet-50 pytorch: CPU - 32 - ResNet-50 pytorch: CPU - 64 - ResNet-50 pytorch: CPU - 16 - ResNet-152 pytorch: CPU - 256 - ResNet-50 pytorch: CPU - 32 - ResNet-152 pytorch: CPU - 64 - ResNet-152 pytorch: CPU - 256 - ResNet-152 pytorch: CPU - 1 - Efficientnet_v2_l pytorch: CPU - 16 - Efficientnet_v2_l pytorch: CPU - 32 - Efficientnet_v2_l pytorch: CPU - 64 - Efficientnet_v2_l pytorch: CPU - 256 - Efficientnet_v2_l blender: BMW27 - CPU-Only blender: Junkshop - CPU-Only blender: Fishy Cat - CPU-Only blender: Barbershop - CPU-Only blender: Pabellon Barcelona - CPU-Only blender: Classroom - CPU-Only build-mesa: Time To Compile a b 15.71 92.7 101.75 104.18 30.89 7.91 48.05 14.33 47.25 14.77 46.51 15.35 29.49 10.34 16.03 16.13 15.93 4.67 15.86 5.93 5.86 6.02 6.60 3.07 3.49 3.78 3.85 163.16 249.71 234.47 1770.95 572.7 405.38 34.889 15.71 91.49 101.38 106.5 29.65 9.41 47.93 14.36 47.45 14.75 46.41 15.37 28.30 9.21 15.93 15.98 15.12 5.42 13.72 6.12 5.59 6.15 6.50 3.68 3.59 3.77 3.69 161.09 249.42 230.59 1757.8 578.85 440.58 32.622 OpenBenchmarking.org
TensorFlow Device: CPU - Batch Size: 1 - Model: AlexNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 1 - Model: AlexNet b a 4 8 12 16 20 15.71 15.71
TensorFlow Device: CPU - Batch Size: 16 - Model: AlexNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 16 - Model: AlexNet a b 20 40 60 80 100 92.70 91.49
TensorFlow Device: CPU - Batch Size: 32 - Model: AlexNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 32 - Model: AlexNet a b 20 40 60 80 100 101.75 101.38
TensorFlow Device: CPU - Batch Size: 64 - Model: AlexNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 64 - Model: AlexNet b a 20 40 60 80 100 106.50 104.18
TensorFlow Device: CPU - Batch Size: 1 - Model: GoogLeNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 1 - Model: GoogLeNet a b 7 14 21 28 35 30.89 29.65
TensorFlow Device: CPU - Batch Size: 1 - Model: ResNet-50 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 1 - Model: ResNet-50 b a 3 6 9 12 15 9.41 7.91
TensorFlow Device: CPU - Batch Size: 16 - Model: GoogLeNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 16 - Model: GoogLeNet a b 11 22 33 44 55 48.05 47.93
TensorFlow Device: CPU - Batch Size: 16 - Model: ResNet-50 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 16 - Model: ResNet-50 b a 4 8 12 16 20 14.36 14.33
TensorFlow Device: CPU - Batch Size: 32 - Model: GoogLeNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 32 - Model: GoogLeNet b a 11 22 33 44 55 47.45 47.25
TensorFlow Device: CPU - Batch Size: 32 - Model: ResNet-50 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 32 - Model: ResNet-50 a b 4 8 12 16 20 14.77 14.75
TensorFlow Device: CPU - Batch Size: 64 - Model: GoogLeNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 64 - Model: GoogLeNet a b 11 22 33 44 55 46.51 46.41
TensorFlow Device: CPU - Batch Size: 64 - Model: ResNet-50 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 64 - Model: ResNet-50 b a 4 8 12 16 20 15.37 15.35
PyTorch Device: CPU - Batch Size: 1 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 1 - Model: ResNet-50 a b 7 14 21 28 35 29.49 28.30 MIN: 21.36 / MAX: 33.16 MIN: 19.22 / MAX: 33.62
PyTorch Device: CPU - Batch Size: 1 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 1 - Model: ResNet-152 a b 3 6 9 12 15 10.34 9.21 MIN: 8.48 / MAX: 12.31 MIN: 5.28 / MAX: 12.19
PyTorch Device: CPU - Batch Size: 16 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 16 - Model: ResNet-50 a b 4 8 12 16 20 16.03 15.93 MIN: 10.94 / MAX: 18.23 MIN: 8.32 / MAX: 18.45
PyTorch Device: CPU - Batch Size: 32 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 32 - Model: ResNet-50 a b 4 8 12 16 20 16.13 15.98 MIN: 14.64 / MAX: 18.27 MIN: 14.51 / MAX: 17.51
PyTorch Device: CPU - Batch Size: 64 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 64 - Model: ResNet-50 a b 4 8 12 16 20 15.93 15.12 MIN: 14.58 / MAX: 17.56 MIN: 11.4 / MAX: 18.4
PyTorch Device: CPU - Batch Size: 16 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 16 - Model: ResNet-152 b a 1.2195 2.439 3.6585 4.878 6.0975 5.42 4.67 MIN: 3.09 / MAX: 7.11 MIN: 3.7 / MAX: 6.87
PyTorch Device: CPU - Batch Size: 256 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 256 - Model: ResNet-50 a b 4 8 12 16 20 15.86 13.72 MIN: 13.05 / MAX: 19.69 MIN: 5.4 / MAX: 20.19
PyTorch Device: CPU - Batch Size: 32 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 32 - Model: ResNet-152 b a 2 4 6 8 10 6.12 5.93 MIN: 4.5 / MAX: 6.7 MIN: 4.07 / MAX: 7.46
PyTorch Device: CPU - Batch Size: 64 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 64 - Model: ResNet-152 a b 1.3185 2.637 3.9555 5.274 6.5925 5.86 5.59 MIN: 4.18 / MAX: 6.58 MIN: 3.08 / MAX: 7.82
PyTorch Device: CPU - Batch Size: 256 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 256 - Model: ResNet-152 b a 2 4 6 8 10 6.15 6.02 MIN: 3.67 / MAX: 6.86 MIN: 5.41 / MAX: 6.78
PyTorch Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l a b 2 4 6 8 10 6.60 6.50 MIN: 2.38 / MAX: 8.69 MIN: 3.03 / MAX: 9.45
PyTorch Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l b a 0.828 1.656 2.484 3.312 4.14 3.68 3.07 MIN: 1.84 / MAX: 4.81 MIN: 1.97 / MAX: 4.84
PyTorch Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_l b a 0.8078 1.6156 2.4234 3.2312 4.039 3.59 3.49 MIN: 2.08 / MAX: 4.37 MIN: 2.11 / MAX: 4.99
PyTorch Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_l a b 0.8505 1.701 2.5515 3.402 4.2525 3.78 3.77 MIN: 3.03 / MAX: 4.36 MIN: 2.09 / MAX: 4.51
PyTorch Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_l a b 0.8663 1.7326 2.5989 3.4652 4.3315 3.85 3.69 MIN: 2.11 / MAX: 4.99 MIN: 2.02 / MAX: 4.78
Blender Blend File: BMW27 - Compute: CPU-Only OpenBenchmarking.org Seconds, Fewer Is Better Blender 4.1 Blend File: BMW27 - Compute: CPU-Only b a 40 80 120 160 200 161.09 163.16
Blender Blend File: Junkshop - Compute: CPU-Only OpenBenchmarking.org Seconds, Fewer Is Better Blender 4.1 Blend File: Junkshop - Compute: CPU-Only b a 50 100 150 200 250 249.42 249.71
Blender Blend File: Fishy Cat - Compute: CPU-Only OpenBenchmarking.org Seconds, Fewer Is Better Blender 4.1 Blend File: Fishy Cat - Compute: CPU-Only b a 50 100 150 200 250 230.59 234.47
Blender Blend File: Barbershop - Compute: CPU-Only OpenBenchmarking.org Seconds, Fewer Is Better Blender 4.1 Blend File: Barbershop - Compute: CPU-Only b a 400 800 1200 1600 2000 1757.80 1770.95
Blender Blend File: Pabellon Barcelona - Compute: CPU-Only OpenBenchmarking.org Seconds, Fewer Is Better Blender 4.1 Blend File: Pabellon Barcelona - Compute: CPU-Only a b 130 260 390 520 650 572.70 578.85
Blender Blend File: Classroom - Compute: CPU-Only OpenBenchmarking.org Seconds, Fewer Is Better Blender 4.1 Blend File: Classroom - Compute: CPU-Only a b 100 200 300 400 500 405.38 440.58
Timed Mesa Compilation Time To Compile OpenBenchmarking.org Seconds, Fewer Is Better Timed Mesa Compilation 24.0 Time To Compile b a 8 16 24 32 40 32.62 34.89
Phoronix Test Suite v10.8.5