AMD EPYC Genoa Memory Scaling

Benchmarks by Michael Larabel for a future article.

Compare your own system(s) to this result file with the Phoronix Test Suite by running the command: phoronix-test-suite benchmark 2212240-NE-AMDEPYCGE62
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12c
December 21 2022
  11 Hours, 55 Minutes
10c
December 21 2022
  12 Hours, 59 Minutes
8c
December 22 2022
  13 Hours, 22 Minutes
6c
December 23 2022
  15 Hours, 14 Minutes
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AMD EPYC Genoa Memory Scaling Suite 1.0.0 System Test suite extracted from AMD EPYC Genoa Memory Scaling. pts/minibude-1.0.0 --deck ../data/bm2 --iterations 10 Implementation: OpenMP - Input Deck: BM2 pts/nekrs-1.0.0 turbPipePeriodic turbPipe.par Input: TurboPipe Periodic pts/openvino-1.2.0 -m models/intel/face-detection-0206/FP16/face-detection-0206.xml -d CPU Model: Face Detection FP16 - Device: CPU pts/openvino-1.2.0 -m models/intel/person-detection-0106/FP16/person-detection-0106.xml -d CPU Model: Person Detection FP16 - Device: CPU pts/openvino-1.2.0 -m models/intel/person-detection-0106/FP32/person-detection-0106.xml -d CPU Model: Person Detection FP32 - Device: CPU pts/openvino-1.2.0 -m models/intel/vehicle-detection-0202/FP16/vehicle-detection-0202.xml -d CPU Model: Vehicle Detection FP16 - Device: CPU pts/openvino-1.2.0 -m models/intel/face-detection-0206/FP16-INT8/face-detection-0206.xml -d CPU Model: Face Detection FP16-INT8 - Device: CPU pts/openvino-1.2.0 -m models/intel/vehicle-detection-0202/FP16-INT8/vehicle-detection-0202.xml -d CPU Model: Vehicle Detection FP16-INT8 - Device: CPU pts/openvino-1.2.0 -m models/intel/weld-porosity-detection-0001/FP16/weld-porosity-detection-0001.xml -d CPU Model: Weld Porosity Detection FP16 - Device: CPU pts/openvino-1.2.0 -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-1.2.0 -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-1.2.0 -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-1.2.0 -m models/intel/age-gender-recognition-retail-0013/FP16/age-gender-recognition-retail-0013.xml -d CPU Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU pts/openvino-1.2.0 -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/embree-1.2.1 pathtracer_ispc -c crown/crown.ecs Binary: Pathtracer ISPC - Model: Crown pts/embree-1.2.1 pathtracer_ispc -c asian_dragon/asian_dragon.ecs Binary: Pathtracer ISPC - Model: Asian Dragon pts/kvazaar-1.1.1 -i Bosphorus_3840x2160.y4m --preset medium Video Input: Bosphorus 4K - Video Preset: Medium pts/kvazaar-1.1.1 -i Bosphorus_3840x2160.y4m --preset veryfast Video Input: Bosphorus 4K - Video Preset: Very Fast pts/kvazaar-1.1.1 -i Bosphorus_3840x2160.y4m --preset ultrafast Video Input: Bosphorus 4K - Video Preset: Ultra Fast pts/svt-av1-2.7.0 --preset 12 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 12 - Input: Bosphorus 4K pts/simdjson-2.0.1 kostya Throughput Test: Kostya pts/simdjson-2.0.1 top_tweet Throughput Test: TopTweet pts/simdjson-2.0.1 large_random Throughput Test: LargeRandom pts/simdjson-2.0.1 partial_tweets Throughput Test: PartialTweets pts/simdjson-2.0.1 distinct_user_id Throughput Test: DistinctUserID pts/hpcg-1.2.1 pts/mt-dgemm-1.2.0 Sustained Floating-Point Rate pts/xmrig-1.1.0 --bench=1M Variant: Monero - Hash Count: 1M pts/xmrig-1.1.0 -a rx/wow --bench=1M Variant: Wownero - Hash Count: 1M pts/oidn-1.4.0 -r RT.hdr_alb_nrm.3840x2160 Run: RT.hdr_alb_nrm.3840x2160 pts/oidn-1.4.0 -r RTLightmap.hdr.4096x4096 Run: RTLightmap.hdr.4096x4096 pts/tensorflow-2.0.0 --device cpu --batch_size=256 --model=resnet50 Device: CPU - Batch Size: 256 - Model: ResNet-50 pts/onnx-1.5.0 fcn-resnet101-11/model.onnx -e cpu Model: fcn-resnet101-11 - Device: CPU - Executor: Standard pts/openvkl-1.3.0 vklBenchmark --benchmark_filter=ispc Benchmark: vklBenchmark ISPC pts/ospray-2.10.0 --benchmark_filter=particle_volume/ao/real_time Benchmark: particle_volume/ao/real_time pts/ospray-2.10.0 --benchmark_filter=particle_volume/scivis/real_time Benchmark: particle_volume/scivis/real_time pts/ospray-2.10.0 --benchmark_filter=particle_volume/pathtracer/real_time Benchmark: particle_volume/pathtracer/real_time pts/ospray-2.10.0 --benchmark_filter=gravity_spheres_volume/dim_512/ao/real_time Benchmark: gravity_spheres_volume/dim_512/ao/real_time pts/ospray-2.10.0 --benchmark_filter=gravity_spheres_volume/dim_512/scivis/real_time Benchmark: gravity_spheres_volume/dim_512/scivis/real_time pts/ospray-2.10.0 --benchmark_filter=gravity_spheres_volume/dim_512/pathtracer/real_time Benchmark: gravity_spheres_volume/dim_512/pathtracer/real_time pts/deepsparse-1.0.1 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-1.0.1 zoo:nlp/question_answering/bert-base/pytorch/huggingface/squad/12layer_pruned90-none --scenario async Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.0.1 zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/base-none --scenario async Model: CV Detection,YOLOv5s COCO - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.0.1 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-1.0.1 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-1.0.1 zoo:nlp/text_classification/bert-base/pytorch/huggingface/sst2/base-none --scenario async Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.0.1 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/luxcorerender-1.4.0 DanishMood/LuxCoreScene/render.cfg -D renderengine.type PATHCPU Scene: Danish Mood - Acceleration: CPU pts/luxcorerender-1.4.0 OrangeJuice/LuxCoreScene/render.cfg -D renderengine.type PATHCPU Scene: Orange Juice - Acceleration: CPU pts/compress-7zip-1.10.0 Test: Compression Rating pts/compress-7zip-1.10.0 Test: Decompression Rating pts/astcenc-1.4.0 -thorough -repeats 10 Preset: Thorough pts/astcenc-1.4.0 -exhaustive -repeats 2 Preset: Exhaustive pts/gromacs-1.7.0 mpi-build water-cut1.0_GMX50_bare/1536 Implementation: MPI CPU - Input: water_GMX50_bare pts/cassandra-1.1.1 WRITE Test: Writes pts/cockroach-1.0.2 movr --concurrency 512 Workload: MoVR - Concurrency: 512 pts/cockroach-1.0.2 movr --concurrency 1024 Workload: MoVR - Concurrency: 1024 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 10 --concurrency 512 Workload: KV, 10% Reads - Concurrency: 512 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 50 --concurrency 512 Workload: KV, 50% Reads - Concurrency: 512 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 60 --concurrency 512 Workload: KV, 60% Reads - Concurrency: 512 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 95 --concurrency 512 Workload: KV, 95% Reads - Concurrency: 512 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 10 --concurrency 1024 Workload: KV, 10% Reads - Concurrency: 1024 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 50 --concurrency 1024 Workload: KV, 50% Reads - Concurrency: 1024 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 60 --concurrency 1024 Workload: KV, 60% Reads - Concurrency: 1024 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 95 --concurrency 1024 Workload: KV, 95% Reads - Concurrency: 1024 pts/stargate-1.1.0 96000 1024 Sample Rate: 96000 - Buffer Size: 1024 pts/stargate-1.1.0 192000 1024 Sample Rate: 192000 - Buffer Size: 1024 pts/nginx-3.0.0 -c 500 Connections: 500 pts/liquid-dsp-1.0.0 -n 256 -b 256 -f 57 Threads: 256 - Buffer Length: 256 - Filter Length: 57 pts/liquid-dsp-1.0.0 -n 384 -b 256 -f 57 Threads: 384 - Buffer Length: 256 - Filter Length: 57 pts/graph500-1.0.1 26 Scale: 26 pts/npb-1.4.5 cg.C Test / Class: CG.C pts/npb-1.4.5 is.D Test / Class: IS.D pts/npb-1.4.5 lu.C Test / Class: LU.C pts/npb-1.4.5 mg.C Test / Class: MG.C pts/npb-1.4.5 sp.C Test / Class: SP.C pts/namd-1.2.1 ATPase Simulation - 327,506 Atoms pts/onednn-3.0.0 --ip --batch=inputs/ip/shapes_3d --cfg=bf16bf16bf16 --engine=cpu Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.0.0 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=u8s8f32 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.0.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=u8s8f32 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.0.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=bf16bf16bf16 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.0.0 --matmul --batch=inputs/matmul/shapes_transformer --cfg=u8s8f32 --engine=cpu Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU pts/dacapobench-1.0.1 h2 Java Test: H2 pts/dacapobench-1.0.1 jython Java Test: Jython pts/rodinia-1.3.2 OMP_CFD Test: OpenMP CFD Solver pts/rodinia-1.3.2 OMP_STREAMCLUSTER Test: OpenMP Streamcluster pts/nwchem-1.1.1 Input: C240 Buckyball pts/incompact3d-2.0.2 input.i3d Input: X3D-benchmarking input.i3d pts/openfoam-1.2.0 incompressible/simpleFoam/drivaerFastback/ -m M Input: drivaerFastback, Medium Mesh Size - Execution Time pts/openradioss-1.0.0 Bumper_Beam_AP_meshed_0000.rad Bumper_Beam_AP_meshed_0001.rad Model: Bumper Beam pts/openradioss-1.0.0 BIRD_WINDSHIELD_v1_0000.rad BIRD_WINDSHIELD_v1_0001.rad Model: Bird Strike on Windshield pts/openradioss-1.0.0 fsi_drop_container_0000.rad fsi_drop_container_0001.rad Model: INIVOL and Fluid Structure Interaction Drop Container pts/relion-1.0.1 --iter 1 --cpu --j 2 Test: Basic - Device: CPU pts/avifenc-1.3.0 -s 0 Encoder Speed: 0 pts/avifenc-1.3.0 -s 2 Encoder Speed: 2 pts/avifenc-1.3.0 -s 6 Encoder Speed: 6 pts/avifenc-1.3.0 -s 6 -l Encoder Speed: 6, Lossless pts/avifenc-1.3.0 -s 10 -l Encoder Speed: 10, Lossless pts/build-apache-1.6.1 Time To Compile pts/build-gdb-1.1.0 Time To Compile pts/build-gem5-1.0.0 Time To Compile pts/build-godot-1.0.0 Time To Compile pts/build-linux-kernel-1.15.0 defconfig Build: defconfig pts/build-linux-kernel-1.15.0 allmodconfig Build: allmodconfig pts/build-llvm-1.4.0 Ninja Build System: Ninja pts/build-mesa-1.0.0 Time To Compile pts/build-mplayer-1.5.0 Time To Compile pts/build-nodejs-1.2.0 Time To Compile pts/build-php-1.6.0 Time To Compile pts/build2-1.1.0 Time To Compile pts/wrf-1.0.1 -i conus 2.5km Input: conus 2.5km pts/gpaw-1.1.0 carbon-nanotube Input: Carbon Nanotube pts/blender-3.4.0 -b ../bmw27_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: BMW27 - Compute: CPU-Only pts/blender-3.4.0 -b ../classroom_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: Classroom - Compute: CPU-Only pts/blender-3.4.0 -b ../barbershop_interior_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: Barbershop - Compute: CPU-Only