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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.

Compare your own system(s) to this result file with the Phoronix Test Suite by running the command: phoronix-test-suite benchmark 2403279-NE-LDLD8328551
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March 27
  2 Hours, 27 Minutes
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March 27
  2 Hours, 26 Minutes
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ldldOpenBenchmarking.orgPhoronix Test SuiteIntel 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 13.2.0ext41920x1200ProcessorMotherboardChipsetMemoryDiskGraphicsAudioNetworkOSKernelDesktopDisplay ServerOpenGLCompilerFile-SystemScreen ResolutionLdld BenchmarksSystem Logs- Transparent Huge Pages: madvise- --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 - Scaling Governor: intel_pstate powersave (EPP: balance_performance) - CPU Microcode: 0x13 - Thermald 2.5.4- Python 3.11.6- 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

a vs. b ComparisonPhoronix Test SuiteBaseline+5%+5%+10%+10%+15%+15%19.9%19%16.1%6.9%3.2%2.9%2.2%2.2%CPU - 16 - Efficientnet_v2_lCPU - 1 - ResNet-50CPU - 16 - ResNet-152CPU - 256 - ResNet-5015.6%CPU - 1 - ResNet-15212.3%Classroom - CPU-Only8.7%Time To CompileCPU - 64 - ResNet-505.4%CPU - 64 - ResNet-1524.8%CPU - 256 - Efficientnet_v2_l4.3%CPU - 1 - ResNet-504.2%CPU - 1 - GoogLeNet4.2%CPU - 32 - ResNet-152CPU - 32 - Efficientnet_v2_lCPU - 64 - AlexNetCPU - 256 - ResNet-152PyTorchTensorFlowPyTorchPyTorchPyTorchBlenderTimed Mesa CompilationPyTorchPyTorchPyTorchPyTorchTensorFlowPyTorchPyTorchTensorFlowPyTorchab

ldldbuild-mesa: Time To Compilepytorch: CPU - 1 - ResNet-50pytorch: CPU - 1 - ResNet-152pytorch: CPU - 16 - ResNet-50pytorch: CPU - 32 - ResNet-50pytorch: CPU - 64 - ResNet-50pytorch: CPU - 16 - ResNet-152pytorch: CPU - 256 - ResNet-50pytorch: CPU - 32 - ResNet-152pytorch: CPU - 64 - ResNet-152pytorch: CPU - 256 - ResNet-152pytorch: CPU - 1 - Efficientnet_v2_lpytorch: CPU - 16 - Efficientnet_v2_lpytorch: CPU - 32 - Efficientnet_v2_lpytorch: CPU - 64 - Efficientnet_v2_lpytorch: CPU - 256 - Efficientnet_v2_ltensorflow: CPU - 1 - AlexNettensorflow: CPU - 16 - AlexNettensorflow: CPU - 32 - AlexNettensorflow: CPU - 64 - AlexNettensorflow: CPU - 1 - GoogLeNettensorflow: CPU - 1 - ResNet-50tensorflow: CPU - 16 - GoogLeNettensorflow: CPU - 16 - ResNet-50tensorflow: CPU - 32 - GoogLeNettensorflow: CPU - 32 - ResNet-50tensorflow: CPU - 64 - GoogLeNettensorflow: CPU - 64 - ResNet-50blender: BMW27 - CPU-Onlyblender: Junkshop - CPU-Onlyblender: Fishy Cat - CPU-Onlyblender: Barbershop - CPU-Onlyblender: Pabellon Barcelona - CPU-Onlyblender: Classroom - CPU-Onlyab34.88929.4910.3416.0316.1315.934.6715.865.935.866.026.603.073.493.783.8515.7192.7101.75104.1830.897.9148.0514.3347.2514.7746.5115.35163.16249.71234.471770.95572.7405.3832.62228.309.2115.9315.9815.125.4213.726.125.596.156.503.683.593.773.6915.7191.49101.38106.529.659.4147.9314.3647.4514.7546.4115.37161.09249.42230.591757.8578.85440.58OpenBenchmarking.org

Timed Mesa Compilation

This test profile times how long it takes to compile Mesa with Meson/Ninja. For minimizing build dependencies and avoid versioning conflicts, test this is just the core Mesa build without LLVM or the extra Gallium3D/Mesa drivers enabled. Learn more via the OpenBenchmarking.org test page.

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

PyTorch

This is a benchmark of PyTorch making use of pytorch-benchmark [https://github.com/LukasHedegaard/pytorch-benchmark]. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 1 - Model: ResNet-50ba71421283528.3029.49MIN: 19.22 / MAX: 33.62MIN: 21.36 / MAX: 33.16

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 1 - Model: ResNet-152ba36912159.2110.34MIN: 5.28 / MAX: 12.19MIN: 8.48 / MAX: 12.31

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 16 - Model: ResNet-50ba4812162015.9316.03MIN: 8.32 / MAX: 18.45MIN: 10.94 / MAX: 18.23

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 32 - Model: ResNet-50ba4812162015.9816.13MIN: 14.51 / MAX: 17.51MIN: 14.64 / MAX: 18.27

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 64 - Model: ResNet-50ba4812162015.1215.93MIN: 11.4 / MAX: 18.4MIN: 14.58 / MAX: 17.56

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 256 - Model: ResNet-50ba4812162013.7215.86MIN: 5.4 / MAX: 20.19MIN: 13.05 / MAX: 19.69

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 64 - Model: ResNet-152ba1.31852.6373.95555.2746.59255.595.86MIN: 3.08 / MAX: 7.82MIN: 4.18 / MAX: 6.58

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_lba2468106.506.60MIN: 3.03 / MAX: 9.45MIN: 2.38 / MAX: 8.69

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

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_lba0.85051.7012.55153.4024.25253.773.78MIN: 2.09 / MAX: 4.51MIN: 3.03 / MAX: 4.36

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_lba0.86631.73262.59893.46524.33153.693.85MIN: 2.02 / MAX: 4.78MIN: 2.11 / MAX: 4.99

TensorFlow

This is a benchmark of the TensorFlow deep learning framework using the TensorFlow reference benchmarks (tensorflow/benchmarks with tf_cnn_benchmarks.py). Note with the Phoronix Test Suite there is also pts/tensorflow-lite for benchmarking the TensorFlow Lite binaries if desired for complementary metrics. Learn more via the OpenBenchmarking.org test page.

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 16 - Model: AlexNetba2040608010091.4992.70

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 32 - Model: AlexNetba20406080100101.38101.75

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 1 - Model: GoogLeNetba71421283529.6530.89

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 16 - Model: GoogLeNetba112233445547.9348.05

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

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 32 - Model: ResNet-50ba4812162014.7514.77

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 64 - Model: GoogLeNetba112233445546.4146.51

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

Blender

Blender is an open-source 3D creation and modeling software project. This test is of Blender's Cycles performance with various sample files. GPU computing via NVIDIA OptiX and NVIDIA CUDA is currently supported as well as HIP for AMD Radeon GPUs and Intel oneAPI for Intel Graphics. Learn more via the OpenBenchmarking.org test page.

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

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

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

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

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 4.1Blend File: Pabellon Barcelona - Compute: CPU-Onlyba130260390520650578.85572.70

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 4.1Blend File: Classroom - Compute: CPU-Onlyba100200300400500440.58405.38