AMD EPYC Zen 4C Power Efficiency

AMD EPYC Zen 4C power efficiency benchmarks by Michael Larabel for a future article.

HTML result view exported from: https://openbenchmarking.org/result/2401147-NE-AMDEPYCZE24&grw.

Xmrig

Variant: Monero - Hash Count: 1M

Xmrig

Variant: Wownero - Hash Count: 1M

Xmrig

Variant: CryptoNight-Femto UPX2 - Hash Count: 1M

Xmrig

Variant: CryptoNight-Heavy - Hash Count: 1M

Xmrig

Variant: KawPow - Hash Count: 1M

Xmrig

Variant: GhostRider - Hash Count: 1M

QuantLib

Configuration: Multi-Threaded

miniBUDE

Implementation: OpenMP - Input Deck: BM1

miniBUDE

Implementation: OpenMP - Input Deck: BM1

miniBUDE

Implementation: OpenMP - Input Deck: BM2

miniBUDE

Implementation: OpenMP - Input Deck: BM2

OpenRadioss

Model: Chrysler Neon 1M

easyWave

Input: e2Asean Grid + BengkuluSept2007 Source - Time: 2400

Y-Cruncher

Pi Digits To Calculate: 1B

easyWave

Input: e2Asean Grid + BengkuluSept2007 Source - Time: 1200

TensorFlow

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

CloverLeaf

Input: clover_bm64_short

CloverLeaf

Input: clover_bm16

Neural Magic DeepSparse

Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream

Y-Cruncher

Pi Digits To Calculate: 500M

Quicksilver

Input: CTS2

Quicksilver

Input: CORAL2 P1

Neural Magic DeepSparse

Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream

PyTorch

Device: CPU - Batch Size: 1 - Model: ResNet-50

PyTorch

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

Llama.cpp

Model: llama-2-7b.Q4_0.gguf

Llama.cpp

Model: llama-2-13b.Q4_0.gguf

Llama.cpp

Model: llama-2-70b-chat.Q5_0.gguf

LAMMPS Molecular Dynamics Simulator

Model: 20k Atoms

NAMD

ATPase Simulation - 327,506 Atoms

OpenVINO

Model: Face Detection FP16-INT8 - Device: CPU

OpenVINO

Model: Face Detection FP16-INT8 - Device: CPU

OpenVINO

Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU

OpenVINO

Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU

OpenVINO

Model: Person Detection FP16 - Device: CPU

OpenVINO

Model: Person Detection FP16 - Device: CPU

OpenVINO

Model: Weld Porosity Detection FP16-INT8 - Device: CPU

OpenVINO

Model: Weld Porosity Detection FP16-INT8 - Device: CPU

OpenVINO

Model: Vehicle Detection FP16-INT8 - Device: CPU

OpenVINO

Model: Vehicle Detection FP16-INT8 - Device: CPU

OpenVINO

Model: Person Vehicle Bike Detection FP16 - Device: CPU

OpenVINO

Model: Person Vehicle Bike Detection FP16 - Device: CPU

OpenVINO

Model: Machine Translation EN To DE FP16 - Device: CPU

OpenVINO

Model: Machine Translation EN To DE FP16 - Device: CPU

OpenVINO

Model: Face Detection Retail FP16-INT8 - Device: CPU

OpenVINO

Model: Face Detection Retail FP16-INT8 - Device: CPU

OpenVINO

Model: Handwritten English Recognition FP16-INT8 - Device: CPU

OpenVINO

Model: Handwritten English Recognition FP16-INT8 - Device: CPU

OpenVINO

Model: Road Segmentation ADAS FP16-INT8 - Device: CPU

OpenVINO

Model: Road Segmentation ADAS FP16-INT8 - Device: CPU

ACES DGEMM

Sustained Floating-Point Rate

Kripke

OpenFOAM

Input: drivaerFastback, Small Mesh Size - Execution Time

OpenFOAM

Input: drivaerFastback, Medium Mesh Size - Mesh Time

OpenFOAM

Input: drivaerFastback, Medium Mesh Size - Execution Time

Xcompact3d Incompact3d

Input: input.i3d 193 Cells Per Direction

Xcompact3d Incompact3d

Input: X3D-benchmarking input.i3d

GPAW

Input: Carbon Nanotube

Timed FFmpeg Compilation

Time To Compile

7-Zip Compression

Test: Compression Rating

7-Zip Compression

Test: Decompression Rating

Timed LLVM Compilation

Build System: Ninja

Timed LLVM Compilation

Build System: Unix Makefiles

Timed Linux Kernel Compilation

Build: defconfig

Timed Linux Kernel Compilation

Build: allmodconfig

SVT-AV1

Encoder Mode: Preset 12 - Input: Bosphorus 4K

SVT-AV1

Encoder Mode: Preset 8 - Input: Bosphorus 4K

SVT-AV1

Encoder Mode: Preset 4 - Input: Bosphorus 4K

x265

Video Input: Bosphorus 4K

Chaos Group V-RAY

Mode: CPU

Blender

Blend File: BMW27 - Compute: CPU-Only

Blender

Blend File: Classroom - Compute: CPU-Only

Blender

Blend File: Fishy Cat - Compute: CPU-Only

Blender

Blend File: Pabellon Barcelona - Compute: CPU-Only

Blender

Blend File: Barbershop - Compute: CPU-Only

FFmpeg

Encoder: libx265 - Scenario: Live

FFmpeg

Encoder: libx265 - Scenario: Upload

FFmpeg

Encoder: libx265 - Scenario: Platform

FFmpeg

Encoder: libx265 - Scenario: Video On Demand

uvg266

Video Input: Bosphorus 4K - Video Preset: Very Fast

uvg266

Video Input: Bosphorus 4K - Video Preset: Super Fast

uvg266

Video Input: Bosphorus 4K - Video Preset: Ultra Fast

VVenC

Video Input: Bosphorus 4K - Video Preset: Faster

rav1e

Speed: 10

rav1e

Speed: 6

Embree

Binary: Pathtracer ISPC - Model: Asian Dragon

Embree

Binary: Pathtracer ISPC - Model: Crown

Intel Open Image Denoise

Run: RT.ldr_alb_nrm.3840x2160 - Device: CPU-Only

IndigoBench

Acceleration: CPU - Scene: Supercar

IndigoBench

Acceleration: CPU - Scene: Bedroom

OSPRay

Benchmark: gravity_spheres_volume/dim_512/ao/real_time

OSPRay

Benchmark: gravity_spheres_volume/dim_512/scivis/real_time

OSPRay

Benchmark: gravity_spheres_volume/dim_512/pathtracer/real_time