new-tests

Tests for a future article. AMD EPYC 8324P 32-Core testing with a AMD Cinnabar (RCB1009C BIOS) and ASPEED 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 2401110-NE-NEWTESTS900
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C++ Boost Tests 3 Tests
Timed Code Compilation 3 Tests
C/C++ Compiler Tests 3 Tests
CPU Massive 7 Tests
Creator Workloads 6 Tests
Encoding 2 Tests
HPC - High Performance Computing 6 Tests
Machine Learning 5 Tests
Multi-Core 10 Tests
Intel oneAPI 3 Tests
Programmer / Developer System Benchmarks 3 Tests
Python Tests 6 Tests
Renderers 2 Tests
Server 2 Tests
Server CPU Tests 5 Tests
Video Encoding 2 Tests

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Zen 1 - EPYC 7601
January 07
  46 Minutes
b
January 10
  12 Minutes
c
January 10
  12 Minutes
32
January 11
  2 Hours, 56 Minutes
32 z
January 11
  2 Hours, 56 Minutes
32 c
January 11
  3 Hours, 14 Minutes
32 d
January 11
  2 Hours, 55 Minutes
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new-tests Suite 1.0.0 System Test suite extracted from new-tests. pts/y-cruncher-1.4.0 1b Pi Digits To Calculate: 1B pts/y-cruncher-1.4.0 500m Pi Digits To Calculate: 500M pts/quicksilver-1.0.0 ../Examples/CORAL2_Benchmark/Problem1/Coral2_P1.inp Input: CORAL2 P1 pts/quicksilver-1.0.0 ../Examples/CTS2_Benchmark/CTS2.inp Input: CTS2 pts/svt-av1-2.11.1 --preset 8 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 8 - Input: Bosphorus 4K pts/rocksdb-1.5.0 --benchmarks="readrandom" Test: Random Read pts/dacapobench-1.1.0 pmd Java Test: PMD Source Code Analyzer pts/speedb-1.0.1 --benchmarks="readrandom" Test: Random Read pts/dacapobench-1.1.0 fop Java Test: FOP Print Formatter pts/speedb-1.0.1 --benchmarks="readwhilewriting" Test: Read While Writing pts/quantlib-1.2.0 --mp Configuration: Multi-Threaded pts/openfoam-1.2.0 incompressible/simpleFoam/drivaerFastback/ -m S Input: drivaerFastback, Small Mesh Size - Mesh Time 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/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/quicksilver-1.0.0 ../Examples/CORAL2_Benchmark/Problem2/Coral2_P2.inp Input: CORAL2 P2 pts/build-gem5-1.1.0 Time To Compile 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/dacapobench-1.1.0 zxing Java Test: Zxing 1D/2D Barcode Image Processing 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/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/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/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/dacapobench-1.1.0 h2o Java Test: H2O In-Memory Platform For Machine Learning pts/openvino-1.4.0 -m models/intel/handwritten-english-recognition-0001/FP16/handwritten-english-recognition-0001.xml -d CPU Model: Handwritten English Recognition FP16 - 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/road-segmentation-adas-0001/FP16-INT8/road-segmentation-adas-0001.xml -d CPU Model: Road Segmentation ADAS FP16-INT8 - Device: CPU pts/dacapobench-1.1.0 h2 Java Test: H2 Database Engine 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/dacapobench-1.1.0 tradesoap Java Test: Tradesoap pts/openvino-1.4.0 -m models/intel/weld-porosity-detection-0001/FP16/weld-porosity-detection-0001.xml -d CPU Model: Weld Porosity Detection FP16 - Device: CPU pts/openvino-1.4.0 -m models/intel/road-segmentation-adas-0001/FP16/road-segmentation-adas-0001.xml -d CPU Model: Road Segmentation ADAS FP16 - Device: CPU pts/build-linux-kernel-1.15.0 allmodconfig Build: allmodconfig pts/tensorflow-2.1.1 --device cpu --batch_size=1 --model=googlenet Device: CPU - Batch Size: 1 - Model: GoogLeNet 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/rocksdb-1.5.0 --benchmarks="readwhilewriting" Test: Read While Writing pts/openvino-1.4.0 -m models/intel/face-detection-0206/FP16/face-detection-0206.xml -d CPU Model: Face Detection FP16 - Device: CPU pts/pytorch-1.0.1 cpu 1 efficientnet_v2_l Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l pts/dacapobench-1.1.0 lusearch Java Test: Apache Lucene Search Engine 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 pts/tensorflow-2.1.1 --device cpu --batch_size=1 --model=alexnet Device: CPU - Batch Size: 1 - Model: AlexNet 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/build-ffmpeg-6.1.0 Time To Compile pts/embree-1.6.1 pathtracer -c crown/crown.ecs Binary: Pathtracer - Model: Crown pts/dacapobench-1.1.0 spring Java Test: Spring Boot 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/dacapobench-1.1.0 graphchi Java Test: GraphChi pts/openvino-1.4.0 -m models/intel/face-detection-retail-0005/FP16/face-detection-retail-0005.xml -d CPU Model: Face Detection Retail FP16 - Device: CPU pts/dacapobench-1.1.0 avrora Java Test: Avrora AVR Simulation Framework pts/svt-av1-2.11.1 --preset 12 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 12 - Input: Bosphorus 4K pts/build-linux-kernel-1.15.0 defconfig Build: defconfig pts/openvino-1.4.0 -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/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/tensorflow-2.1.1 --device cpu --batch_size=16 --model=vgg16 Device: CPU - Batch Size: 16 - Model: VGG-16 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/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 2 2 --resolution 3840 2160 --spp 32 --renderer pathtracer Camera: 2 - Resolution: 4K - Samples Per Pixel: 32 - 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/openvino-1.4.0 -m models/intel/vehicle-detection-0202/FP16/vehicle-detection-0202.xml -d CPU Model: Vehicle Detection FP16 - Device: 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/dacapobench-1.1.0 tradebeans Java Test: Tradebeans 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/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/xmrig-1.2.0 -a gr --bench=1M Variant: GhostRider - Hash Count: 1M pts/dacapobench-1.1.0 jython Java Test: Jython pts/ospray-studio-1.2.0 --cameras 2 2 --resolution 3840 2160 --spp 16 --renderer pathtracer Camera: 2 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path Tracer - Acceleration: CPU pts/openfoam-1.2.0 incompressible/simpleFoam/drivaerFastback/ -m S Input: drivaerFastback, Small Mesh Size - Execution Time pts/dacapobench-1.1.0 xalan Java Test: Apache Xalan XSLT pts/ospray-studio-1.2.0 --cameras 2 2 --resolution 3840 2160 --spp 1 --renderer pathtracer Camera: 2 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path Tracer - Acceleration: CPU pts/xmrig-1.2.0 -a rx/wow --bench=1M Variant: Wownero - Hash Count: 1M 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/tensorflow-2.1.1 --device cpu --batch_size=1 --model=resnet50 Device: CPU - Batch Size: 1 - Model: ResNet-50 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/embree-1.6.1 pathtracer_ispc -c crown/crown.ecs Binary: Pathtracer ISPC - Model: Crown pts/rocksdb-1.5.0 --benchmarks="readrandomwriterandom" Test: Read Random Write Random pts/speedb-1.0.1 --benchmarks="readrandomwriterandom" Test: Read Random Write Random pts/pytorch-1.0.1 cpu 16 resnet152 Device: CPU - Batch Size: 16 - Model: ResNet-152 pts/embree-1.6.1 pathtracer_ispc -c asian_dragon/asian_dragon.ecs Binary: Pathtracer ISPC - Model: Asian Dragon pts/tensorflow-2.1.1 --device cpu --batch_size=16 --model=googlenet Device: CPU - Batch Size: 16 - Model: GoogLeNet pts/pytorch-1.0.1 cpu 1 resnet50 Device: CPU - Batch Size: 1 - Model: ResNet-50 pts/embree-1.6.1 pathtracer -c asian_dragon_obj/asian_dragon.ecs Binary: Pathtracer - Model: Asian Dragon Obj pts/dacapobench-1.1.0 tomcat Java Test: Apache Tomcat pts/dacapobench-1.1.0 eclipse Java Test: Eclipse pts/speedb-1.0.1 --benchmarks="updaterandom" Test: Update Random pts/tensorflow-2.1.1 --device cpu --batch_size=16 --model=alexnet Device: CPU - Batch Size: 16 - Model: AlexNet pts/llama-cpp-1.0.0 -m ../llama-2-13b.Q4_0.gguf Model: llama-2-13b.Q4_0.gguf pts/xmrig-1.2.0 -a cn-heavy/0 --bench=1M Variant: CryptoNight-Heavy - Hash Count: 1M 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/dacapobench-1.1.0 batik Java Test: Batik SVG Toolkit 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/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: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/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/pytorch-1.0.1 cpu 16 efficientnet_v2_l Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l pts/xmrig-1.2.0 -a kawpow --bench=1M Variant: KawPow - Hash Count: 1M pts/svt-av1-2.11.1 --preset 13 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 13 - Input: Bosphorus 4K pts/pytorch-1.0.1 cpu 1 resnet152 Device: CPU - Batch Size: 1 - Model: ResNet-152 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/rocksdb-1.5.0 --benchmarks="updaterandom" Test: Update Random pts/pytorch-1.0.1 cpu 16 resnet50 Device: CPU - Batch Size: 16 - Model: ResNet-50 pts/compress-7zip-1.10.0 Test: Compression Rating 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:cv/detection/yolov5-s/pytorch/ultralytics/coco/pruned85-none --scenario async Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream pts/cachebench-1.2.0 -b Test: Read / Modify / Write pts/dacapobench-1.1.0 luindex Java Test: Apache Lucene Search Index pts/xmrig-1.2.0 --bench=1M Variant: Monero - Hash Count: 1M 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/embree-1.6.1 pathtracer -c asian_dragon/asian_dragon.ecs Binary: Pathtracer - Model: Asian Dragon pts/dacapobench-1.1.0 biojava Java Test: BioJava Biological Data Framework pts/llama-cpp-1.0.0 -m ../llama-2-7b.Q4_0.gguf Model: llama-2-7b.Q4_0.gguf pts/embree-1.6.1 pathtracer_ispc -c asian_dragon_obj/asian_dragon.ecs Binary: Pathtracer ISPC - Model: Asian Dragon Obj pts/ffmpeg-6.1.0 --encoder=libx265 vod Encoder: libx265 - Scenario: Video On Demand pts/ffmpeg-6.1.0 --encoder=libx265 live Encoder: libx265 - Scenario: Live pts/xmrig-1.2.0 -a cn/upx2 --bench=1M Variant: CryptoNight-Femto UPX2 - Hash Count: 1M pts/dacapobench-1.1.0 cassandra Java Test: Apache Cassandra pts/tensorflow-2.1.1 --device cpu --batch_size=16 --model=resnet50 Device: CPU - Batch Size: 16 - Model: ResNet-50 pts/tensorflow-2.1.1 --device cpu --batch_size=1 --model=vgg16 Device: CPU - Batch Size: 1 - Model: VGG-16 pts/openvino-1.4.0 -m models/intel/person-detection-0303/FP32/person-detection-0303.xml -d CPU Model: Person Detection FP32 - Device: CPU pts/compress-7zip-1.10.0 Test: Decompression Rating 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/llama-cpp-1.0.0 -m ../llama-2-70b-chat.Q5_0.gguf Model: llama-2-70b-chat.Q5_0.gguf pts/dacapobench-1.1.0 kafka Java Test: Apache Kafka 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/dacapobench-1.1.0 jme Java Test: jMonkeyEngine pts/cachebench-1.2.0 -w Test: Write pts/cachebench-1.2.0 -r Test: Read