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&rdt&grt.

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-Only7600x a7600x b7950x3d a7950x3d b306090120150127.87128.2652.1051.96

Blender

Blend File: Classroom - Compute: CPU-Only

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 3.5Blend File: Classroom - Compute: CPU-Only7600x a7600x b7950x3d a7950x3d b70140210280350333.15333.68136.00135.80

Blender

Blend File: Fishy Cat - Compute: CPU-Only

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 3.5Blend File: Fishy Cat - Compute: CPU-Only7600x a7600x b7950x3d a7950x3d b4080120160200163.19162.9266.5666.16

Blender

Blend File: Barbershop - Compute: CPU-Only

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 3.5Blend File: Barbershop - Compute: CPU-Only7600x a7600x b7950x3d a7950x3d b300600900120015001202.851202.67480.88480.34

Blender

Blend File: Pabellon Barcelona - Compute: CPU-Only

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 3.5Blend File: Pabellon Barcelona - Compute: CPU-Only7600x a7600x b7950x3d a7950x3d b90180270360450411.84410.66167.38166.53

TensorFlow

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 16 - Model: AlexNet7600x a7600x b7950x3d a7950x3d b4080120160200SE +/- 0.04, N = 3114.42114.73176.00176.37

TensorFlow

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 32 - Model: AlexNet7600x a7600x b7950x3d a7950x3d b60120180240300SE +/- 0.21, N = 3145.54145.45259.68258.45

TensorFlow

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 64 - Model: AlexNet7600x a7600x b7950x3d a7950x3d b70140210280350SE +/- 0.14, N = 3166.15164.76340.13339.62

TensorFlow

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 256 - Model: AlexNet7600x a7600x b7950x3d a7950x3d b90180270360450SE +/- 0.42, N = 3186.23186.31419.28418.85

TensorFlow

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 512 - Model: AlexNet7600x a7600x b7950x3d a7950x3d b90180270360450SE +/- 1.32, N = 3188.70190.30430.57430.58

TensorFlow

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 16 - Model: GoogLeNet7600x a7600x b7950x3d a7950x3d b306090120150SE +/- 0.08, N = 382.0881.95146.69146.43

TensorFlow

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 16 - Model: ResNet-507600x a7600x b7950x3d a7950x3d b1020304050SE +/- 0.01, N = 327.8627.8543.1443.24

TensorFlow

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 32 - Model: GoogLeNet7600x a7600x b7950x3d a7950x3d b306090120150SE +/- 0.02, N = 380.7380.76149.50149.66

TensorFlow

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 32 - Model: ResNet-507600x a7600x b7950x3d a7950x3d b1020304050SE +/- 0.01, N = 327.8127.8044.3444.32

TensorFlow

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 64 - Model: GoogLeNet7600x a7600x b7950x3d a7950x3d b306090120150SE +/- 0.04, N = 380.0579.92145.10145.19

TensorFlow

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 64 - Model: ResNet-507600x a7600x b7950x3d a7950x3d b1020304050SE +/- 0.00, N = 327.6327.6644.1644.24

TensorFlow

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 256 - Model: GoogLeNet7600x a7600x b7950x3d a7950x3d b306090120150SE +/- 0.05, N = 379.5479.61137.95137.75

TensorFlow

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 256 - Model: ResNet-507600x a7600x b7950x3d a7950x3d b102030405027.5327.5443.8543.90

TensorFlow

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

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 512 - Model: GoogLeNet7600x a7600x b7950x3d a7950x3d b30609012015079.7779.77136.90136.97


Phoronix Test Suite v10.8.5