tensorflow ryzen zen 4

Tests for a future article. AMD Ryzen 9 7950X3D 16-Core testing with a ASUS ROG CROSSHAIR X670E HERO (9927 BIOS) and AMD Radeon RX 7900 XTX on Ubuntu 23.04 via the Phoronix Test Suite.

HTML result view exported from: https://openbenchmarking.org/result/2303294-PTS-TENSORFL34&grt&sor.

tensorflow ryzen zen 4ProcessorMotherboardChipsetMemoryDiskGraphicsAudioMonitorNetworkOSKernelDesktopDisplay ServerOpenGLOpenCLCompilerFile-SystemScreen Resolution7600x a7600x b7950x3d a7950x3d bAMD Ryzen 5 7600X 6-Core @ 4.70GHz (6 Cores / 12 Threads)ASUS ROG CROSSHAIR X670E HERO (9927 BIOS)AMD Device 14d832GBWestern Digital WD_BLACK SN850X 1000GB + 2000GBAMD Radeon RX 7900 XTX (2304/1249MHz)AMD Device ab30ASUS MG28UIntel I225-V + Intel Wi-Fi 6 AX210/AX211/AX411Ubuntu 23.046.2.8-060208-generic (x86_64)GNOME Shell 44.0X Server 1.21.1.7 + Wayland4.6 Mesa 23.1.0-devel (git-de8b14f 2023-03-24 lunar-oibaf-ppa) (LLVM 15.0.7 DRM 3.49)OpenCL 2.1 AMD-APP (3513.0)GCC 12.2.0ext43840x2160AMD Ryzen 9 7950X3D 16-Core @ 4.20GHz (16 Cores / 32 Threads)OpenBenchmarking.orgKernel Details- Transparent Huge Pages: madviseProcessor Details- Scaling Governor: acpi-cpufreq performance (Boost: Enabled) - CPU Microcode: 0xa601203Python Details- Python 3.11.2Security Details- itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + retbleed: 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 Retpolines IBPB: conditional IBRS_FW STIBP: always-on RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected

tensorflow ryzen zen 4blender: BMW27 - CPU-Onlyblender: Classroom - CPU-Onlyblender: Fishy Cat - CPU-Onlyblender: Barbershop - CPU-Onlyblender: Pabellon Barcelona - CPU-Onlytensorflow: CPU - 16 - AlexNettensorflow: CPU - 32 - AlexNettensorflow: CPU - 64 - AlexNettensorflow: CPU - 256 - AlexNettensorflow: CPU - 512 - AlexNettensorflow: CPU - 16 - GoogLeNettensorflow: CPU - 16 - ResNet-50tensorflow: CPU - 32 - GoogLeNettensorflow: CPU - 32 - ResNet-50tensorflow: CPU - 64 - GoogLeNettensorflow: CPU - 64 - ResNet-50tensorflow: CPU - 256 - GoogLeNettensorflow: CPU - 256 - ResNet-50tensorflow: CPU - 512 - GoogLeNettensorflow: CPU - 512 - ResNet-507600x a7600x b7950x3d a7950x3d b127.87333.15163.191202.85411.84114.42145.54166.15186.23188.782.0827.8680.7327.8180.0527.6379.5427.5379.77128.26333.68162.921202.67410.66114.73145.45164.76186.31190.3081.9527.8580.7627.8079.9227.6679.6127.5479.7752.113666.56480.88167.38176259.68340.13419.28430.57146.6943.14149.544.34145.144.16137.9543.85136.951.96135.866.16480.34166.53176.37258.45339.62418.85430.58146.4343.24149.6644.32145.1944.24137.7543.9136.97OpenBenchmarking.org

Blender

Blend File: BMW27 - Compute: CPU-Only

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 3.5Blend File: BMW27 - Compute: CPU-Only7950x3d b7950x3d a7600x a7600x b30609012015051.9652.10127.87128.26

Blender

Blend File: Classroom - Compute: CPU-Only

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 3.5Blend File: Classroom - Compute: CPU-Only7950x3d b7950x3d a7600x a7600x b70140210280350135.80136.00333.15333.68

Blender

Blend File: Fishy Cat - Compute: CPU-Only

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 3.5Blend File: Fishy Cat - Compute: CPU-Only7950x3d b7950x3d a7600x b7600x a408012016020066.1666.56162.92163.19

Blender

Blend File: Barbershop - Compute: CPU-Only

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 3.5Blend File: Barbershop - Compute: CPU-Only7950x3d b7950x3d a7600x b7600x a30060090012001500480.34480.881202.671202.85

Blender

Blend File: Pabellon Barcelona - Compute: CPU-Only

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 3.5Blend File: Pabellon Barcelona - Compute: CPU-Only7950x3d b7950x3d a7600x b7600x a90180270360450166.53167.38410.66411.84

TensorFlow

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 16 - Model: AlexNet7950x3d b7950x3d a7600x b7600x a4080120160200SE +/- 0.04, N = 3176.37176.00114.73114.42

TensorFlow

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 32 - Model: AlexNet7950x3d a7950x3d b7600x a7600x b60120180240300SE +/- 0.21, N = 3259.68258.45145.54145.45

TensorFlow

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 64 - Model: AlexNet7950x3d a7950x3d b7600x a7600x b70140210280350SE +/- 0.14, N = 3340.13339.62166.15164.76

TensorFlow

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 256 - Model: AlexNet7950x3d a7950x3d b7600x b7600x a90180270360450SE +/- 0.42, N = 3419.28418.85186.31186.23

TensorFlow

Device: CPU - Batch Size: 512 - Model: AlexNet

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 512 - Model: AlexNet7950x3d b7950x3d a7600x b7600x a90180270360450SE +/- 1.32, N = 3430.58430.57190.30188.70

TensorFlow

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 16 - Model: GoogLeNet7950x3d a7950x3d b7600x a7600x b306090120150SE +/- 0.08, N = 3146.69146.4382.0881.95

TensorFlow

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 16 - Model: ResNet-507950x3d b7950x3d a7600x a7600x b1020304050SE +/- 0.01, N = 343.2443.1427.8627.85

TensorFlow

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 32 - Model: GoogLeNet7950x3d b7950x3d a7600x b7600x a306090120150SE +/- 0.02, N = 3149.66149.5080.7680.73

TensorFlow

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 32 - Model: ResNet-507950x3d a7950x3d b7600x a7600x b1020304050SE +/- 0.01, N = 344.3444.3227.8127.80

TensorFlow

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 64 - Model: GoogLeNet7950x3d b7950x3d a7600x a7600x b306090120150SE +/- 0.04, N = 3145.19145.1080.0579.92

TensorFlow

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 64 - Model: ResNet-507950x3d b7950x3d a7600x b7600x a1020304050SE +/- 0.00, N = 344.2444.1627.6627.63

TensorFlow

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 256 - Model: GoogLeNet7950x3d a7950x3d b7600x b7600x a306090120150SE +/- 0.05, N = 3137.95137.7579.6179.54

TensorFlow

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 256 - Model: ResNet-507950x3d b7950x3d a7600x b7600x a102030405043.9043.8527.5427.53

TensorFlow

Device: CPU - Batch Size: 512 - Model: GoogLeNet

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 512 - Model: GoogLeNet7950x3d b7950x3d a7600x b7600x a306090120150136.97136.9079.7779.77


Phoronix Test Suite v10.8.5