tensorflow ryzen zen 4

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/2303296-PTS-TENSORFL70&sro&grs.

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: Barbershop - CPU-Onlyblender: Pabellon Barcelona - CPU-Onlyblender: BMW27 - CPU-Onlyblender: Fishy Cat - CPU-Onlyblender: Classroom - CPU-Onlytensorflow: CPU - 512 - AlexNettensorflow: CPU - 256 - AlexNettensorflow: CPU - 64 - AlexNettensorflow: CPU - 32 - GoogLeNettensorflow: CPU - 64 - GoogLeNettensorflow: CPU - 16 - GoogLeNettensorflow: CPU - 32 - AlexNettensorflow: CPU - 256 - GoogLeNettensorflow: CPU - 512 - GoogLeNettensorflow: CPU - 64 - ResNet-50tensorflow: CPU - 32 - ResNet-50tensorflow: CPU - 256 - ResNet-50tensorflow: CPU - 16 - ResNet-50tensorflow: CPU - 16 - AlexNettensorflow: CPU - 512 - ResNet-507600x a7600x b7950x3d a7950x3d b1202.85411.84127.87163.19333.15188.7186.23166.1580.7380.0582.08145.5479.5479.7727.6327.8127.5327.86114.421202.67410.66128.26162.92333.68190.30186.31164.7680.7679.9281.95145.4579.6179.7727.6627.8027.5427.85114.73480.88167.3852.166.56136430.57419.28340.13149.5145.1146.69259.68137.95136.944.1644.3443.8543.14176480.34166.5351.9666.16135.8430.58418.85339.62149.66145.19146.43258.45137.75136.9744.2444.3243.943.24176.37OpenBenchmarking.org

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

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: 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: Classroom - Compute: CPU-Only

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

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: 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: 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: 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: 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: 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: 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: 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: 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

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: 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: 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: 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: 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


Phoronix Test Suite v10.8.5