ldld

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/2403279-NE-LDLD8328551&sor&grs.

ldldProcessorMotherboardChipsetMemoryDiskGraphicsAudioNetworkOSKernelDesktopDisplay ServerOpenGLCompilerFile-SystemScreen ResolutionabIntel Core Ultra 7 155H @ 4.80GHz (16 Cores / 22 Threads)MTL Swift SFG14-72T Coral_MTH (V1.01 BIOS)Intel Device 7e7f8 x 2GB DRAM-6400MT/s Micron MT62F1G32D2DS-0261024GB Micron_2550_MTFDKBA1T0TGEIntel Arc MTL 8GB (2250MHz)Intel Meteor Lake-P HD AudioIntel Device 7e40Ubuntu 23.106.8.0-060800rc1daily20240126-generic (x86_64)GNOME Shell 45.2X Server 1.21.1.7 + Wayland4.6 Mesa 24.1~git2401200600.ebcab1~oibaf~m (git-ebcab14 2024-01-20 mantic-oibaf-ppa)GCC 13.2.0ext41920x1200OpenBenchmarking.orgKernel Details- Transparent Huge Pages: madviseCompiler 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 3.11.6Security 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

ldldpytorch: CPU - 16 - Efficientnet_v2_ltensorflow: CPU - 1 - ResNet-50pytorch: CPU - 16 - ResNet-152pytorch: CPU - 256 - ResNet-50pytorch: CPU - 1 - ResNet-152blender: Classroom - CPU-Onlybuild-mesa: Time To Compilepytorch: CPU - 64 - ResNet-50pytorch: CPU - 64 - ResNet-152pytorch: CPU - 256 - Efficientnet_v2_lpytorch: CPU - 1 - ResNet-50tensorflow: CPU - 1 - GoogLeNetpytorch: CPU - 32 - ResNet-152pytorch: CPU - 32 - Efficientnet_v2_ltensorflow: CPU - 64 - AlexNetpytorch: CPU - 256 - ResNet-152blender: Fishy Cat - CPU-Onlypytorch: CPU - 1 - Efficientnet_v2_ltensorflow: CPU - 16 - AlexNetblender: BMW27 - CPU-Onlyblender: Pabellon Barcelona - CPU-Onlypytorch: CPU - 32 - ResNet-50blender: Barbershop - CPU-Onlypytorch: CPU - 16 - ResNet-50tensorflow: CPU - 32 - GoogLeNettensorflow: CPU - 32 - AlexNetpytorch: CPU - 64 - Efficientnet_v2_ltensorflow: CPU - 16 - GoogLeNettensorflow: CPU - 64 - GoogLeNettensorflow: CPU - 16 - ResNet-50tensorflow: CPU - 32 - ResNet-50tensorflow: CPU - 64 - ResNet-50blender: Junkshop - CPU-Onlytensorflow: CPU - 1 - AlexNetab3.077.914.6715.8610.34405.3834.88915.935.863.8529.4930.895.933.49104.186.02234.476.6092.7163.16572.716.131770.9516.0347.25101.753.7848.0546.5114.3314.7715.35249.7115.713.689.415.4213.729.21440.5832.62215.125.593.6928.3029.656.123.59106.56.15230.596.5091.49161.09578.8515.981757.815.9347.45101.383.7747.9346.4114.3614.7515.37249.4215.71OpenBenchmarking.org

PyTorch

Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_lba0.8281.6562.4843.3124.143.683.07MIN: 1.84 / MAX: 4.81MIN: 1.97 / MAX: 4.84

TensorFlow

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 1 - Model: ResNet-50ba36912159.417.91

PyTorch

Device: CPU - Batch Size: 16 - Model: ResNet-152

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 16 - Model: ResNet-152ba1.21952.4393.65854.8786.09755.424.67MIN: 3.09 / MAX: 7.11MIN: 3.7 / MAX: 6.87

PyTorch

Device: CPU - Batch Size: 256 - Model: ResNet-50

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 256 - Model: ResNet-50ab4812162015.8613.72MIN: 13.05 / MAX: 19.69MIN: 5.4 / MAX: 20.19

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 1 - Model: ResNet-152ab369121510.349.21MIN: 8.48 / MAX: 12.31MIN: 5.28 / MAX: 12.19

Blender

Blend File: Classroom - Compute: CPU-Only

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 4.1Blend File: Classroom - Compute: CPU-Onlyab100200300400500405.38440.58

Timed Mesa Compilation

Time To Compile

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed Mesa Compilation 24.0Time To Compileba81624324032.6234.89

PyTorch

Device: CPU - Batch Size: 64 - Model: ResNet-50

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 64 - Model: ResNet-50ab4812162015.9315.12MIN: 14.58 / MAX: 17.56MIN: 11.4 / MAX: 18.4

PyTorch

Device: CPU - Batch Size: 64 - Model: ResNet-152

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 64 - Model: ResNet-152ab1.31852.6373.95555.2746.59255.865.59MIN: 4.18 / MAX: 6.58MIN: 3.08 / MAX: 7.82

PyTorch

Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_l

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_lab0.86631.73262.59893.46524.33153.853.69MIN: 2.11 / MAX: 4.99MIN: 2.02 / MAX: 4.78

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 1 - Model: ResNet-50ab71421283529.4928.30MIN: 21.36 / MAX: 33.16MIN: 19.22 / MAX: 33.62

TensorFlow

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 1 - Model: GoogLeNetab71421283530.8929.65

PyTorch

Device: CPU - Batch Size: 32 - Model: ResNet-152

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 32 - Model: ResNet-152ba2468106.125.93MIN: 4.5 / MAX: 6.7MIN: 4.07 / MAX: 7.46

PyTorch

Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_l

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_lba0.80781.61562.42343.23124.0393.593.49MIN: 2.08 / MAX: 4.37MIN: 2.11 / MAX: 4.99

TensorFlow

Device: CPU - Batch Size: 64 - Model: AlexNet

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 64 - Model: AlexNetba20406080100106.50104.18

PyTorch

Device: CPU - Batch Size: 256 - Model: ResNet-152

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 256 - Model: ResNet-152ba2468106.156.02MIN: 3.67 / MAX: 6.86MIN: 5.41 / MAX: 6.78

Blender

Blend File: Fishy Cat - Compute: CPU-Only

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 4.1Blend File: Fishy Cat - Compute: CPU-Onlyba50100150200250230.59234.47

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_lab2468106.606.50MIN: 2.38 / MAX: 8.69MIN: 3.03 / MAX: 9.45

TensorFlow

Device: CPU - Batch Size: 16 - Model: AlexNet

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 16 - Model: AlexNetab2040608010092.7091.49

Blender

Blend File: BMW27 - Compute: CPU-Only

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 4.1Blend File: BMW27 - Compute: CPU-Onlyba4080120160200161.09163.16

Blender

Blend File: Pabellon Barcelona - Compute: CPU-Only

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 4.1Blend File: Pabellon Barcelona - Compute: CPU-Onlyab130260390520650572.70578.85

PyTorch

Device: CPU - Batch Size: 32 - Model: ResNet-50

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 32 - Model: ResNet-50ab4812162016.1315.98MIN: 14.64 / MAX: 18.27MIN: 14.51 / MAX: 17.51

Blender

Blend File: Barbershop - Compute: CPU-Only

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 4.1Blend File: Barbershop - Compute: CPU-Onlyba4008001200160020001757.801770.95

PyTorch

Device: CPU - Batch Size: 16 - Model: ResNet-50

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 16 - Model: ResNet-50ab4812162016.0315.93MIN: 10.94 / MAX: 18.23MIN: 8.32 / MAX: 18.45

TensorFlow

Device: CPU - Batch Size: 32 - Model: GoogLeNet

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 32 - Model: GoogLeNetba112233445547.4547.25

TensorFlow

Device: CPU - Batch Size: 32 - Model: AlexNet

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 32 - Model: AlexNetab20406080100101.75101.38

PyTorch

Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_l

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_lab0.85051.7012.55153.4024.25253.783.77MIN: 3.03 / MAX: 4.36MIN: 2.09 / MAX: 4.51

TensorFlow

Device: CPU - Batch Size: 16 - Model: GoogLeNet

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 16 - Model: GoogLeNetab112233445548.0547.93

TensorFlow

Device: CPU - Batch Size: 64 - Model: GoogLeNet

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 64 - Model: GoogLeNetab112233445546.5146.41

TensorFlow

Device: CPU - Batch Size: 16 - Model: ResNet-50

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 16 - Model: ResNet-50ba4812162014.3614.33

TensorFlow

Device: CPU - Batch Size: 32 - Model: ResNet-50

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 32 - Model: ResNet-50ab4812162014.7714.75

TensorFlow

Device: CPU - Batch Size: 64 - Model: ResNet-50

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 64 - Model: ResNet-50ba4812162015.3715.35

Blender

Blend File: Junkshop - Compute: CPU-Only

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 4.1Blend File: Junkshop - Compute: CPU-Onlyba50100150200250249.42249.71

TensorFlow

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 1 - Model: AlexNetba4812162015.7115.71

Phoronix Test Suite v10.8.5