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.

Compare your own system(s) to this result file with the Phoronix Test Suite by running the command: phoronix-test-suite benchmark 2401063-NE-AMDEPYCZE11
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Result
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Date
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  Test
  Duration
EPYC 7601
January 05
  1 Day, 4 Hours, 15 Minutes
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AMD EPYC Zen 1 Suite 1.0.0 System Test suite extracted from AMD EPYC Zen 1. pts/pytorch-1.0.0 cpu 1 resnet50 Device: CPU - Batch Size: 1 - Model: ResNet-50 pts/pytorch-1.0.0 cpu 256 resnet50 Device: CPU - Batch Size: 256 - Model: ResNet-50 pts/minibude-1.0.0 --deck ../data/bm1 --iterations 500 Implementation: OpenMP - Input Deck: BM1 pts/minibude-1.0.0 --deck ../data/bm2 --iterations 10 Implementation: OpenMP - Input Deck: BM2 pts/openssl-3.1.0 sha256 Algorithm: SHA256 pts/openssl-3.1.0 sha512 Algorithm: SHA512 pts/openssl-3.1.0 -evp aes-128-gcm Algorithm: AES-128-GCM pts/openssl-3.1.0 -evp aes-256-gcm Algorithm: AES-256-GCM pts/openssl-3.1.0 -evp chacha20 Algorithm: ChaCha20 pts/openssl-3.1.0 -evp chacha20-poly1305 Algorithm: ChaCha20-Poly1305 pts/ffmpeg-6.1.0 --encoder=libx265 live Encoder: libx265 - Scenario: Live pts/ffmpeg-6.1.0 --encoder=libx265 upload Encoder: libx265 - Scenario: Upload pts/ffmpeg-6.1.0 --encoder=libx265 platform Encoder: libx265 - Scenario: Platform pts/ffmpeg-6.1.0 --encoder=libx265 vod Encoder: libx265 - Scenario: Video On Demand pts/openvino-1.4.0 -m models/intel/face-detection-0206/FP16-INT8/face-detection-0206.xml -d CPU Model: Face Detection FP16-INT8 - Device: CPU pts/openvino-1.4.0 -m models/intel/age-gender-recognition-retail-0013/FP16-INT8/age-gender-recognition-retail-0013.xml -d CPU Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU pts/openvino-1.4.0 -m models/intel/person-detection-0303/FP16/person-detection-0303.xml -d CPU Model: Person Detection FP16 - Device: CPU pts/openvino-1.4.0 -m models/intel/weld-porosity-detection-0001/FP16-INT8/weld-porosity-detection-0001.xml -d CPU Model: Weld Porosity Detection FP16-INT8 - Device: CPU pts/openvino-1.4.0 -m models/intel/vehicle-detection-0202/FP16-INT8/vehicle-detection-0202.xml -d CPU Model: Vehicle Detection FP16-INT8 - Device: CPU pts/openvino-1.4.0 -m models/intel/person-vehicle-bike-detection-2004/FP16/person-vehicle-bike-detection-2004.xml -d CPU Model: Person Vehicle Bike Detection FP16 - Device: CPU pts/openvino-1.4.0 -m models/intel/machine-translation-nar-en-de-0002/FP16/machine-translation-nar-en-de-0002.xml -d CPU Model: Machine Translation EN To DE FP16 - Device: CPU pts/openvino-1.4.0 -m models/intel/face-detection-retail-0005/FP16-INT8/face-detection-retail-0005.xml -d CPU Model: Face Detection Retail FP16-INT8 - Device: CPU pts/openvino-1.4.0 -m models/intel/road-segmentation-adas-0001/FP16-INT8/road-segmentation-adas-0001.xml -d CPU Model: Road Segmentation ADAS FP16-INT8 - Device: CPU pts/openvino-1.4.0 -m models/intel/handwritten-english-recognition-0001/FP16-INT8/handwritten-english-recognition-0001.xml -d CPU Model: Handwritten English Recognition FP16-INT8 - Device: CPU pts/embree-1.6.1 pathtracer_ispc -c asian_dragon/asian_dragon.ecs Binary: Pathtracer ISPC - Model: Asian Dragon pts/embree-1.6.1 pathtracer_ispc -c crown/crown.ecs Binary: Pathtracer ISPC - Model: Crown pts/svt-av1-2.11.1 --preset 13 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 13 - Input: Bosphorus 4K pts/svt-av1-2.11.1 --preset 12 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 12 - Input: Bosphorus 4K pts/svt-av1-2.11.1 --preset 8 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 8 - Input: Bosphorus 4K pts/svt-av1-2.11.1 --preset 4 -n 160 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 4 - Input: Bosphorus 4K pts/rav1e-1.8.0 -s 10 -l 600 Speed: 10 pts/rav1e-1.8.0 -s 6 -l 200 Speed: 6 pts/rav1e-1.8.0 -s 5 -l 200 Speed: 5 pts/rav1e-1.8.0 -s 1 -l 80 Speed: 1 pts/x265-1.3.0 Bosphorus_3840x2160.y4m Video Input: Bosphorus 4K pts/uvg266-1.0.0 -i Bosphorus_3840x2160.y4m --preset medium Video Input: Bosphorus 4K - Video Preset: Medium pts/uvg266-1.0.0 -i Bosphorus_3840x2160.y4m --preset veryfast Video Input: Bosphorus 4K - Video Preset: Very Fast pts/uvg266-1.0.0 -i Bosphorus_3840x2160.y4m --preset superfast Video Input: Bosphorus 4K - Video Preset: Super Fast pts/uvg266-1.0.0 -i Bosphorus_3840x2160.y4m --preset ultrafast Video Input: Bosphorus 4K - Video Preset: Ultra Fast pts/vvenc-1.9.1 -i Bosphorus_3840x2160.y4m --preset fast Video Input: Bosphorus 4K - Video Preset: Fast pts/vvenc-1.9.1 -i Bosphorus_3840x2160.y4m --preset faster Video Input: Bosphorus 4K - Video Preset: Faster pts/mt-dgemm-1.2.0 Sustained Floating-Point Rate pts/xmrig-1.2.0 --bench=1M Variant: Monero - Hash Count: 1M pts/xmrig-1.2.0 -a rx/wow --bench=1M Variant: Wownero - Hash Count: 1M pts/xmrig-1.2.0 -a cn/upx2 --bench=1M Variant: CryptoNight-Femto UPX2 - Hash Count: 1M pts/xmrig-1.2.0 -a cn-heavy/0 --bench=1M Variant: CryptoNight-Heavy - Hash Count: 1M pts/xmrig-1.2.0 -a kawpow --bench=1M Variant: KawPow - Hash Count: 1M pts/xmrig-1.2.0 -a gr --bench=1M Variant: GhostRider - Hash Count: 1M pts/oidn-2.1.0 -r RT.ldr_alb_nrm.3840x2160 -d cpu Run: RT.ldr_alb_nrm.3840x2160 - Device: CPU-Only pts/tensorflow-2.1.0 --device cpu --batch_size=16 --model=resnet50 Device: CPU - Batch Size: 16 - Model: ResNet-50 pts/ospray-2.12.0 --benchmark_filter=gravity_spheres_volume/dim_512/ao/real_time Benchmark: gravity_spheres_volume/dim_512/ao/real_time pts/ospray-2.12.0 --benchmark_filter=gravity_spheres_volume/dim_512/scivis/real_time Benchmark: gravity_spheres_volume/dim_512/scivis/real_time pts/ospray-2.12.0 --benchmark_filter=gravity_spheres_volume/dim_512/pathtracer/real_time Benchmark: gravity_spheres_volume/dim_512/pathtracer/real_time pts/ospray-2.12.0 --benchmark_filter=particle_volume/ao/real_time Benchmark: particle_volume/ao/real_time pts/ospray-2.12.0 --benchmark_filter=particle_volume/scivis/real_time Benchmark: particle_volume/scivis/real_time pts/ospray-2.12.0 --benchmark_filter=particle_volume/pathtracer/real_time Benchmark: particle_volume/pathtracer/real_time pts/deepsparse-1.6.0 zoo:nlp/sentiment_analysis/oberta-base/pytorch/huggingface/sst2/pruned90_quant-none --input_shapes='[1,128]' --scenario async Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.6.0 zoo:nlp/text_classification/distilbert-none/pytorch/huggingface/mnli/base-none --scenario async Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.6.0 zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/base-none --scenario async Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.6.0 zoo:nlp/token_classification/bert-base/pytorch/huggingface/conll2003/base-none --scenario async Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.6.0 zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/base-none --scenario async Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.6.0 zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/pruned85-none --scenario async Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.6.0 zoo:nlp/document_classification/obert-base/pytorch/huggingface/imdb/base-none --scenario async Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.6.0 zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/pruned90-none --scenario async Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.6.0 zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/base-none --scenario async Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.6.0 zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned95_uniform_quant-none --scenario async Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.6.0 zoo:nlp/question_answering/obert-large/pytorch/huggingface/squad/base-none --input_shapes='[1,128]' --scenario async Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.6.0 zoo:nlp/question_answering/obert-large/pytorch/huggingface/squad/pruned97_quant-none --input_shapes='[1,128]' --scenario async Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream pts/indigobench-1.1.0 --cpuonly --scenes supercar Acceleration: CPU - Scene: Supercar pts/indigobench-1.1.0 --cpuonly --scenes bedroom Acceleration: CPU - Scene: Bedroom pts/quantlib-1.2.0 --mp Configuration: Multi-Threaded pts/compress-7zip-1.10.0 Test: Compression Rating pts/compress-7zip-1.10.0 Test: Decompression Rating pts/gromacs-1.8.0 mpi-build water-cut1.0_GMX50_bare/1536 Implementation: MPI CPU - Input: water_GMX50_bare pts/lammps-1.4.0 benchmark_20k_atoms.in Model: 20k Atoms pts/speedb-1.0.1 --benchmarks="readrandom" Test: Random Read pts/speedb-1.0.1 --benchmarks="readwhilewriting" Test: Read While Writing pts/rocksdb-1.5.0 --benchmarks="updaterandom" Test: Update Random pts/speedb-1.0.1 --benchmarks="readrandomwriterandom" Test: Read Random Write Random pts/speedb-1.0.1 --benchmarks="updaterandom" Test: Update Random pts/rocksdb-1.5.0 --benchmarks="readrandom" Test: Random Read pts/cassandra-1.2.0 WRITE Test: Writes pts/rocksdb-1.5.0 --benchmarks="readwhilewriting" Test: Read While Writing pts/rocksdb-1.5.0 --benchmarks="readrandomwriterandom" Test: Read Random Write Random pts/memtier-benchmark-1.5.0 -P redis -c 100 --ratio=1:10 Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:10 pts/memtier-benchmark-1.5.0 -P redis -c 100 --ratio=1:5 Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:5 pts/apache-iotdb-1.2.0 500 100 200 100 Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 200 - Client Number: 100 pts/apache-iotdb-1.2.0 500 100 200 400 Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 200 - Client Number: 400 pts/apache-iotdb-1.2.0 500 100 500 100 Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 100 pts/apache-iotdb-1.2.0 500 100 500 400 Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 400 pts/apache-iotdb-1.2.0 500 100 800 100 Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 100 pts/apache-iotdb-1.2.0 500 100 800 400 Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 400 pts/apache-iotdb-1.2.0 800 100 200 100 Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 200 - Client Number: 100 pts/apache-iotdb-1.2.0 800 100 200 400 Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 200 - Client Number: 400 pts/apache-iotdb-1.2.0 800 100 500 100 Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 100 pts/apache-iotdb-1.2.0 800 100 500 400 Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 400 pts/apache-iotdb-1.2.0 800 100 800 100 Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 100 pts/apache-iotdb-1.2.0 800 100 800 400 Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 400 pts/nginx-3.0.1 -c 500 Connections: 500 pts/nginx-3.0.1 -c 1000 Connections: 1000 pts/apache-3.0.0 -c 1000 Concurrent Requests: 1000 pts/openssl-3.1.0 rsa4096 Algorithm: RSA4096 pts/kripke-1.2.0 pts/v-ray-1.4.0 -m vray Mode: CPU pts/namd-1.2.1 ATPase Simulation - 327,506 Atoms pts/ospray-studio-1.2.0 --cameras 1 1 --resolution 3840 2160 --spp 1 --renderer pathtracer Camera: 1 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path Tracer - Acceleration: CPU pts/ospray-studio-1.2.0 --cameras 1 1 --resolution 3840 2160 --spp 16 --renderer pathtracer Camera: 1 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path Tracer - Acceleration: CPU pts/ospray-studio-1.2.0 --cameras 1 1 --resolution 3840 2160 --spp 32 --renderer pathtracer Camera: 1 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path Tracer - Acceleration: CPU pts/ospray-studio-1.2.0 --cameras 3 3 --resolution 3840 2160 --spp 1 --renderer pathtracer Camera: 3 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path Tracer - Acceleration: CPU pts/ospray-studio-1.2.0 --cameras 3 3 --resolution 3840 2160 --spp 16 --renderer pathtracer Camera: 3 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path Tracer - Acceleration: CPU pts/ospray-studio-1.2.0 --cameras 3 3 --resolution 3840 2160 --spp 32 --renderer pathtracer Camera: 3 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path Tracer - Acceleration: CPU pts/duckdb-1.0.0 benchmark/imdb Benchmark: IMDB pts/duckdb-1.0.0 benchmark/tpch/parquet Benchmark: TPC-H Parquet pts/build-linux-kernel-1.15.0 defconfig Build: defconfig pts/build-linux-kernel-1.15.0 allmodconfig Build: allmodconfig pts/build-llvm-1.5.0 Ninja Build System: Ninja pts/build-llvm-1.5.0 Build System: Unix Makefiles pts/build-nodejs-1.3.0 Time To Compile pts/build-gem5-1.1.0 Time To Compile pts/build-ffmpeg-6.1.0 Time To Compile pts/specfem3d-1.0.0 layered_halfspace Model: Layered Halfspace pts/specfem3d-1.0.0 waterlayered_halfspace Model: Water-layered Halfspace pts/specfem3d-1.0.0 homogeneous_halfspace Model: Homogeneous Halfspace pts/specfem3d-1.0.0 Mount_StHelens Model: Mount St. Helens pts/specfem3d-1.0.0 tomographic_model Model: Tomographic Model pts/openfoam-1.2.0 incompressible/simpleFoam/drivaerFastback/ -m S Input: drivaerFastback, Small Mesh Size - Mesh Time pts/openfoam-1.2.0 incompressible/simpleFoam/drivaerFastback/ -m S Input: drivaerFastback, Small Mesh Size - Execution Time pts/openfoam-1.2.0 incompressible/simpleFoam/drivaerFastback/ -m M Input: drivaerFastback, Medium Mesh Size - Mesh Time pts/openfoam-1.2.0 incompressible/simpleFoam/drivaerFastback/ -m M Input: drivaerFastback, Medium Mesh Size - Execution Time pts/openradioss-1.1.1 NEON1M11_0000.rad NEON1M11_0001.rad Model: Chrysler Neon 1M pts/easywave-1.0.0 -grid examples/e2Asean.grd -source examples/BengkuluSept2007.flt -time 1200 Input: e2Asean Grid + BengkuluSept2007 Source - Time: 1200 pts/easywave-1.0.0 -grid examples/e2Asean.grd -source examples/BengkuluSept2007.flt -time 2400 Input: e2Asean Grid + BengkuluSept2007 Source - Time: 2400 pts/gpaw-1.2.0 carbon-nanotube Input: Carbon Nanotube pts/cloverleaf-1.2.0 clover_bm64_short Input: clover_bm64_short pts/cloverleaf-1.2.0 clover_bm16 Input: clover_bm16 pts/incompact3d-2.0.2 input_193_nodes.i3d Input: input.i3d 193 Cells Per Direction pts/incompact3d-2.0.2 input.i3d Input: X3D-benchmarking input.i3d pts/mrbayes-1.5.0 Primate Phylogeny Analysis pts/blender-4.0.0 -b ../bmw27_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: BMW27 - Compute: CPU-Only pts/blender-4.0.0 -b ../classroom_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: Classroom - Compute: CPU-Only pts/blender-4.0.0 -b ../fishy_cat_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: Fishy Cat - Compute: CPU-Only pts/blender-4.0.0 -b ../pavillon_barcelone_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: Pabellon Barcelona - Compute: CPU-Only pts/blender-4.0.0 -b ../barbershop_interior_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: Barbershop - Compute: CPU-Only