AMD EPYC Zen 1

AMD EPYC 7601 32-Core testing with a TYAN B8026T70AE24HR (V1.02.B10 BIOS) and llvmpipe on Ubuntu 23.10 via the Phoronix Test Suite.

HTML result view exported from: https://openbenchmarking.org/result/2401063-NE-AMDEPYCZE11&grr.

Xcompact3d Incompact3d

Input: X3D-benchmarking input.i3d

Timed Gem5 Compilation

Time To Compile

easyWave

Input: e2Asean Grid + BengkuluSept2007 Source - Time: 2400

CloverLeaf

Input: clover_bm16

Xmrig

Variant: GhostRider - Hash Count: 1M

Blender

Blend File: Barbershop - Compute: CPU-Only

Timed Linux Kernel Compilation

Build: allmodconfig

LAMMPS Molecular Dynamics Simulator

Model: 20k Atoms

Xmrig

Variant: Monero - Hash Count: 1M

Xmrig

Variant: CryptoNight-Heavy - Hash Count: 1M

Xmrig

Variant: KawPow - Hash Count: 1M

OpenRadioss

Model: Chrysler Neon 1M

Timed LLVM Compilation

Build System: Unix Makefiles

easyWave

Input: e2Asean Grid + BengkuluSept2007 Source - Time: 1200

OpenFOAM

Input: drivaerFastback, Medium Mesh Size - Execution Time

OpenFOAM

Input: drivaerFastback, Medium Mesh Size - Mesh Time

Timed LLVM Compilation

Build System: Ninja

FFmpeg

Encoder: libx265 - Scenario: Video On Demand

FFmpeg

Encoder: libx265 - Scenario: Platform

Timed Node.js Compilation

Time To Compile

miniBUDE

Implementation: OpenMP - Input Deck: BM2

miniBUDE

Implementation: OpenMP - Input Deck: BM2

OSPRay Studio

Camera: 3 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path Tracer - Acceleration: CPU

FFmpeg

Encoder: libx265 - Scenario: Upload

DuckDB

Benchmark: TPC-H Parquet

Apache IoTDB

Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 400

Apache IoTDB

Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 400

Apache IoTDB

Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 100

Apache IoTDB

Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 100

ACES DGEMM

Sustained Floating-Point Rate

DuckDB

Benchmark: IMDB

OSPRay Studio

Camera: 1 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path Tracer - Acceleration: CPU

Blender

Blend File: Pabellon Barcelona - Compute: CPU-Only

GPAW

Input: Carbon Nanotube

Apache IoTDB

Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 400

Apache IoTDB

Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 400

Apache IoTDB

Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 100

Apache IoTDB

Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 100

Apache IoTDB

Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 400

Apache IoTDB

Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 400

Apache IoTDB

Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 100

Apache IoTDB

Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 100

x265

Video Input: Bosphorus 4K

Blender

Blend File: Classroom - Compute: CPU-Only

Timed MrBayes Analysis

Primate Phylogeny Analysis

OSPRay Studio

Camera: 3 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path Tracer - Acceleration: CPU

TensorFlow

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

VVenC

Video Input: Bosphorus 4K - Video Preset: Fast

OpenSSL

Algorithm: ChaCha20-Poly1305

OpenSSL

Algorithm: ChaCha20

OpenSSL

Algorithm: AES-256-GCM

OpenSSL

Algorithm: AES-128-GCM

OpenSSL

Algorithm: SHA512

OpenSSL

Algorithm: SHA256

OSPRay

Benchmark: particle_volume/scivis/real_time

Apache IoTDB

Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 400

Apache IoTDB

Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 400

Apache IoTDB

Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 100

Apache IoTDB

Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 100

OSPRay Studio

Camera: 1 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path Tracer - Acceleration: CPU

OSPRay

Benchmark: particle_volume/pathtracer/real_time

Xmrig

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

rav1e

Speed: 1

Apache IoTDB

Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 200 - Client Number: 400

Apache IoTDB

Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 200 - Client Number: 400

Apache IoTDB

Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 200 - Client Number: 100

Apache IoTDB

Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 200 - Client Number: 100

Apache Cassandra

Test: Writes

OSPRay

Benchmark: particle_volume/ao/real_time

FFmpeg

Encoder: libx265 - Scenario: Live

Apache IoTDB

Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 200 - Client Number: 400

Apache IoTDB

Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 200 - Client Number: 400

Apache IoTDB

Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 200 - Client Number: 100

Apache IoTDB

Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 200 - Client Number: 100

CloverLeaf

Input: clover_bm64_short

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

Blender

Blend File: Fishy Cat - Compute: CPU-Only

Speedb

Test: Read While Writing

Xmrig

Variant: Wownero - Hash Count: 1M

QuantLib

Configuration: Multi-Threaded

PyTorch

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

GROMACS

Implementation: MPI CPU - Input: water_GMX50_bare

OSPRay Studio

Camera: 3 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path Tracer - Acceleration: CPU

VVenC

Video Input: Bosphorus 4K - Video Preset: Faster

OSPRay Studio

Camera: 1 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path Tracer - Acceleration: CPU

nginx

Connections: 1000

nginx

Connections: 500

Apache HTTP Server

Concurrent Requests: 1000

rav1e

Speed: 5

SVT-AV1

Encoder Mode: Preset 12 - Input: Bosphorus 4K

rav1e

Speed: 10

Chaos Group V-RAY

Mode: CPU

SPECFEM3D

Model: Layered Halfspace

SPECFEM3D

Model: Water-layered Halfspace

OSPRay

Benchmark: gravity_spheres_volume/dim_512/pathtracer/real_time

Timed Linux Kernel Compilation

Build: defconfig

Blender

Blend File: BMW27 - Compute: CPU-Only

SVT-AV1

Encoder Mode: Preset 13 - Input: Bosphorus 4K

uvg266

Video Input: Bosphorus 4K - Video Preset: Medium

OpenVINO

Model: Face Detection FP16-INT8 - Device: CPU

OpenVINO

Model: Face Detection FP16-INT8 - Device: CPU

rav1e

Speed: 6

Redis 7.0.12 + memtier_benchmark

Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:10

Redis 7.0.12 + memtier_benchmark

Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:5

IndigoBench

Acceleration: CPU - Scene: Bedroom

Intel Open Image Denoise

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

IndigoBench

Acceleration: CPU - Scene: Supercar

NAMD

ATPase Simulation - 327,506 Atoms

OpenVINO

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

OpenVINO

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

OpenVINO

Model: Person Detection FP16 - Device: CPU

OpenVINO

Model: Person Detection FP16 - Device: CPU

SVT-AV1

Encoder Mode: Preset 4 - Input: Bosphorus 4K

OpenVINO

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

OpenVINO

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

OpenVINO

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

OpenVINO

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

OpenVINO

Model: Person Vehicle Bike Detection FP16 - Device: CPU

OpenVINO

Model: Person Vehicle Bike Detection FP16 - Device: CPU

OpenVINO

Model: Vehicle Detection FP16-INT8 - Device: CPU

OpenVINO

Model: Vehicle Detection FP16-INT8 - Device: CPU

Speedb

Test: Read Random Write Random

OpenVINO

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

OpenVINO

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

OpenVINO

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

OpenVINO

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

Speedb

Test: Update Random

OpenVINO

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

OpenVINO

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

RocksDB

Test: Update Random

Speedb

Test: Random Read

RocksDB

Test: Read Random Write Random

RocksDB

Test: Read While Writing

OpenSSL

Algorithm: RSA4096

OpenSSL

Algorithm: RSA4096

RocksDB

Test: Random Read

miniBUDE

Implementation: OpenMP - Input Deck: BM1

miniBUDE

Implementation: OpenMP - Input Deck: BM1

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

OpenFOAM

Input: drivaerFastback, Small Mesh Size - Execution Time

OpenFOAM

Input: drivaerFastback, Small Mesh Size - Mesh Time

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

OSPRay

Benchmark: gravity_spheres_volume/dim_512/scivis/real_time

OSPRay

Benchmark: gravity_spheres_volume/dim_512/ao/real_time

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

7-Zip Compression

Test: Decompression Rating

7-Zip Compression

Test: Compression Rating

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

PyTorch

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

Kripke

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

Neural Magic DeepSparse

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

SPECFEM3D

Model: Homogeneous Halfspace

Embree

Binary: Pathtracer ISPC - Model: Crown

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

Neural Magic DeepSparse

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

Timed FFmpeg Compilation

Time To Compile

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

Neural Magic DeepSparse

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

Xcompact3d Incompact3d

Input: input.i3d 193 Cells Per Direction

Embree

Binary: Pathtracer ISPC - Model: Asian Dragon

SPECFEM3D

Model: Tomographic Model