tg

Tests for a future article. Intel Core i7-1280P testing with a MSI MS-14C6 (E14C6IMS.115 BIOS) and MSI Intel ADL GT2 15GB 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 2311250-NE-TG983149007
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November 25 2023
  1 Hour, 57 Minutes
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November 25 2023
  2 Hours, 23 Minutes
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tgOpenBenchmarking.orgPhoronix Test SuiteIntel Core i7-1280P @ 4.70GHz (14 Cores / 20 Threads)MSI MS-14C6 (E14C6IMS.115 BIOS)Intel Alder Lake PCH16GB1024GB Micron_3400_MTFDKBA1T0TFHMSI Intel ADL GT2 15GB (1450MHz)Realtek ALC274Intel Alder Lake-P PCH CNVi WiFiUbuntu 23.106.5.0-10-generic (x86_64)GNOME Shell 45.0X Server + Wayland4.6 Mesa 23.2.1-1ubuntu3OpenCL 3.0GCC 13.2.0ext41920x1080ProcessorMotherboardChipsetMemoryDiskGraphicsAudioNetworkOSKernelDesktopDisplay ServerOpenGLOpenCLCompilerFile-SystemScreen ResolutionTg 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: 0x42c - Thermald 2.5.4 - OpenJDK Runtime Environment (build 17.0.9-ea+6-Ubuntu-1)- 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: SW sequence + srbds: Not affected + tsx_async_abort: Not affected

a vs. b ComparisonPhoronix Test SuiteBaseline+67.9%+67.9%+135.8%+135.8%+203.7%+203.7%2.3%BLAS CPU FP32271.5%CPU - 32 - ResNet-15240.6%CPU - 16 - Efficientnet_v2_l34.5%BMW27 - CPU-Only31.9%CPU - 1 - ResNet-5029.5%CPU - 16 - ResNet-5028.1%CPU - 64 - ResNet-5028%CPU - 32 - ResNet-5027.9%CPU - 16 - ResNet-15227.4%CPU - 64 - ResNet-15227%CPU - 1 - ResNet-15225.4%CPU - 1 - Efficientnet_v2_l22.9%CPU - 32 - Efficientnet_v2_l16.4%Q.1.C.E.514.3%CPU - 64 - Efficientnet_v2_l8.8%C.G.CArrayFirePyTorchPyTorchBlenderPyTorchPyTorchPyTorchPyTorchPyTorchPyTorchPyTorchPyTorchPyTorchWebP2 Image EncodePyTorchArrayFireab

tgjava-scimark2: Compositejava-scimark2: Monte Carlojava-scimark2: Fast Fourier Transformjava-scimark2: Sparse Matrix Multiplyjava-scimark2: Dense LU Matrix Factorizationjava-scimark2: Jacobi Successive Over-Relaxationwebp2: Defaultwebp2: Quality 75, Compression Effort 7webp2: Quality 95, Compression Effort 7webp2: Quality 100, Compression Effort 5webp2: Quality 100, Lossless Compressionembree: Pathtracer ISPC - Crownembree: Pathtracer ISPC - Asian Dragonarrayfire: BLAS CPU FP16arrayfire: BLAS CPU FP32arrayfire: Conjugate Gradient CPUpytorch: 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 - 32 - ResNet-152pytorch: CPU - 64 - ResNet-152pytorch: CPU - 1 - Efficientnet_v2_lpytorch: CPU - 16 - Efficientnet_v2_lpytorch: CPU - 32 - Efficientnet_v2_lpytorch: CPU - 64 - Efficientnet_v2_lblender: BMW27 - CPU-Onlyab2908.791245.28719.933708.476529.952340.357.050.090.043.600.015.33667.14364.539394.68613.4220.049.1913.7713.7813.765.445.405.455.523.393.483.23233.562916.231246.36725.123733.826531.952343.937.040.090.043.150.015.33167.11164.2895106.23513.1215.477.3310.7510.7710.754.273.844.294.492.522.992.97308.05OpenBenchmarking.org

Java SciMark

OpenBenchmarking.orgMflops, More Is BetterJava SciMark 2.2Computational Test: Compositeab60012001800240030002908.792916.23

OpenBenchmarking.orgMflops, More Is BetterJava SciMark 2.2Computational Test: Monte Carloab300600900120015001245.281246.36

OpenBenchmarking.orgMflops, More Is BetterJava SciMark 2.2Computational Test: Fast Fourier Transformab160320480640800719.93725.12

OpenBenchmarking.orgMflops, More Is BetterJava SciMark 2.2Computational Test: Sparse Matrix Multiplyab80016002400320040003708.473733.82

OpenBenchmarking.orgMflops, More Is BetterJava SciMark 2.2Computational Test: Dense LU Matrix Factorizationab140028004200560070006529.956531.95

OpenBenchmarking.orgMflops, More Is BetterJava SciMark 2.2Computational Test: Jacobi Successive Over-Relaxationab50010001500200025002340.352343.93

WebP2 Image Encode

OpenBenchmarking.orgMP/s, More Is BetterWebP2 Image Encode 20220823Encode Settings: Defaultab2468107.057.041. (CXX) g++ options: -msse4.2 -fno-rtti -O3 -ldl

OpenBenchmarking.orgMP/s, More Is BetterWebP2 Image Encode 20220823Encode Settings: Quality 75, Compression Effort 7ab0.02030.04060.06090.08120.10150.090.091. (CXX) g++ options: -msse4.2 -fno-rtti -O3 -ldl

OpenBenchmarking.orgMP/s, More Is BetterWebP2 Image Encode 20220823Encode Settings: Quality 95, Compression Effort 7ab0.0090.0180.0270.0360.0450.040.041. (CXX) g++ options: -msse4.2 -fno-rtti -O3 -ldl

OpenBenchmarking.orgMP/s, More Is BetterWebP2 Image Encode 20220823Encode Settings: Quality 100, Compression Effort 5ab0.811.622.433.244.053.603.151. (CXX) g++ options: -msse4.2 -fno-rtti -O3 -ldl

OpenBenchmarking.orgMP/s, More Is BetterWebP2 Image Encode 20220823Encode Settings: Quality 100, Lossless Compressionab0.00230.00460.00690.00920.01150.010.011. (CXX) g++ options: -msse4.2 -fno-rtti -O3 -ldl

Embree

OpenBenchmarking.orgFrames Per Second, More Is BetterEmbree 4.3Binary: Pathtracer ISPC - Model: Crownab1.20072.40143.60214.80286.00355.33665.3316MIN: 5.2 / MAX: 5.49MIN: 5.2 / MAX: 5.49

OpenBenchmarking.orgFrames Per Second, More Is BetterEmbree 4.3Binary: Pathtracer ISPC - Model: Asian Dragonab2468107.1437.111MIN: 7.04 / MAX: 7.25MIN: 6.99 / MAX: 7.23

ArrayFire

OpenBenchmarking.orgGFLOPS, More Is BetterArrayFire 3.9Test: BLAS CPU FP16ab142842567064.5464.291. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOPS, More Is BetterArrayFire 3.9Test: BLAS CPU FP32ab90180270360450394.69106.241. (CXX) g++ options: -O3

OpenBenchmarking.orgms, Fewer Is BetterArrayFire 3.9Test: Conjugate Gradient CPUab369121513.4213.121. (CXX) g++ options: -O3

PyTorch

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 1 - Model: ResNet-50ab51015202520.0415.47MIN: 17.17 / MAX: 35.57MIN: 14.08 / MAX: 22.48

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 1 - Model: ResNet-152ab36912159.197.33MIN: 8.48 / MAX: 12.97MIN: 7.08 / MAX: 11.65

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: ResNet-50ab4812162013.7710.75MIN: 12.94 / MAX: 18.53MIN: 10.17 / MAX: 15.41

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 32 - Model: ResNet-50ab4812162013.7810.77MIN: 13.15 / MAX: 18.24MIN: 10.43 / MAX: 14.76

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 64 - Model: ResNet-50ab4812162013.7610.75MIN: 13.09 / MAX: 18.12MIN: 10.57 / MAX: 15.02

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: ResNet-152ab1.2242.4483.6724.8966.125.444.27MIN: 5.3 / MAX: 7.11MIN: 4.17 / MAX: 5.87

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 32 - Model: ResNet-152ab1.2152.433.6454.866.0755.403.84MIN: 5.26 / MAX: 7.05MIN: 3.77 / MAX: 5.24

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 64 - Model: ResNet-152ab1.22632.45263.67894.90526.13155.454.29MIN: 5.17 / MAX: 7.1MIN: 4.22 / MAX: 5.81

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_lab1.2422.4843.7264.9686.215.524.49MIN: 5.16 / MAX: 8.36MIN: 4.05 / MAX: 6.6

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_lab0.76281.52562.28843.05123.8143.392.52MIN: 3.16 / MAX: 5.04MIN: 2.46 / MAX: 3.36

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_lab0.7831.5662.3493.1323.9153.482.99MIN: 3.2 / MAX: 4.03MIN: 2.89 / MAX: 4.23

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_lab0.72681.45362.18042.90723.6343.232.97MIN: 3.15 / MAX: 4.09MIN: 2.92 / MAX: 4.19

Blender

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 4.0Blend File: BMW27 - Compute: CPU-Onlyab70140210280350233.56308.05