AMD EPYC 7763 1P spec_rstack_overflow

Benchmarks by Michael Larabel for a future article looking at AMD Inception impact.

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August 10 2023
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AMD EPYC 7763 1P spec_rstack_overflow Suite 1.0.0 System Test suite extracted from AMD EPYC 7763 1P spec_rstack_overflow. pts/mysqlslap-1.4.0 --concurrency=4096 Clients: 4096 pts/sqlite-2.2.0 16 Threads / Copies: 16 pts/sqlite-2.2.0 8 Threads / Copies: 8 pts/build-linux-kernel-1.15.0 defconfig Build: defconfig pts/build-linux-kernel-1.15.0 allmodconfig Build: allmodconfig pts/nginx-3.0.1 -c 500 Connections: 500 pts/nginx-3.0.1 -c 1000 Connections: 1000 pts/spark-1.0.1 -r 1000000 -p 100 Row Count: 1000000 - Partitions: 100 - Inner Join Test Time pts/mysqlslap-1.4.0 --concurrency=8192 Clients: 8192 pts/pgbench-1.13.0 -s 100 -c 800 -S Scaling Factor: 100 - Clients: 800 - Mode: Read Only - Average Latency pts/pgbench-1.13.0 -s 100 -c 800 -S Scaling Factor: 100 - Clients: 800 - Mode: Read Only pts/apache-iotdb-1.0.1 500 1 500 Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 500 pts/compress-7zip-1.10.0 Test: Compression Rating pts/tensorflow-2.1.0 --device cpu --batch_size=64 --model=resnet50 Device: CPU - Batch Size: 64 - Model: ResNet-50 pts/pgbench-1.13.0 -s 100 -c 800 Scaling Factor: 100 - Clients: 800 - Mode: Read Write pts/pgbench-1.13.0 -s 100 -c 800 Scaling Factor: 100 - Clients: 800 - Mode: Read Write - Average Latency pts/clickhouse-1.2.0 100M Rows Hits Dataset, Third Run pts/openradioss-1.0.0 Cell_Phone_Drop_0000.rad Cell_Phone_Drop_0001.rad Model: Cell Phone Drop Test pts/clickhouse-1.2.0 100M Rows Hits Dataset, First Run / Cold Cache pts/openradioss-1.0.0 RUBBER_SEAL_IMPDISP_GEOM_0000.rad RUBBER_SEAL_IMPDISP_GEOM_0001.rad Model: Rubber O-Ring Seal Installation pts/numpy-1.2.1 pts/spark-1.0.1 -r 1000000 -p 100 Row Count: 1000000 - Partitions: 100 - Broadcast Inner Join Test Time pts/rocksdb-1.5.0 --benchmarks="updaterandom" Test: Update Random pts/apache-iotdb-1.0.1 500 100 200 Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 200 pts/apache-iotdb-1.0.1 200 100 200 Device Count: 200 - Batch Size Per Write: 100 - Sensor Count: 200 pts/apache-iotdb-1.0.1 200 1 500 Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 500 pts/clickhouse-1.2.0 100M Rows Hits Dataset, Second Run pts/openradioss-1.0.0 Bumper_Beam_AP_meshed_0000.rad Bumper_Beam_AP_meshed_0001.rad Model: Bumper Beam pts/apache-iotdb-1.0.1 200 1 200 Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 200 pts/openradioss-1.0.0 BIRD_WINDSHIELD_v1_0000.rad BIRD_WINDSHIELD_v1_0001.rad Model: Bird Strike on Windshield pts/build-nodejs-1.3.0 Time To Compile pts/spark-1.0.1 -r 1000000 -p 100 Row Count: 1000000 - Partitions: 100 - Group By Test Time pts/apache-iotdb-1.0.1 500 1 200 Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 200 pts/apache-iotdb-1.0.1 200 100 500 Device Count: 200 - Batch Size Per Write: 100 - Sensor Count: 500 pts/apache-iotdb-1.0.1 500 100 500 Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 500 pts/mt-dgemm-1.2.0 Sustained Floating-Point Rate pts/cockroach-1.0.2 kv --ramp 10s --read-percent 50 --concurrency 128 Workload: KV, 50% Reads - Concurrency: 128 pts/rocksdb-1.5.0 --benchmarks="readrandomwriterandom" Test: Read Random Write Random pts/dacapobench-1.0.1 tradebeans Java Test: Tradebeans pts/memtier-benchmark-1.5.0 -P redis -c 50 --ratio=1:5 Protocol: Redis - Clients: 50 - Set To Get Ratio: 1:5 pts/remhos-1.0.0 -m ./data/inline-quad.mesh -p 14 -rs 2 -rp 1 -dt 0.0005 -tf 0.6 -ho 1 -lo 2 -fct 3 Test: Sample Remap Example pts/build-llvm-1.5.0 Ninja Build System: Ninja pts/openfoam-1.2.0 incompressible/simpleFoam/drivaerFastback/ -m M Input: drivaerFastback, Medium Mesh Size - Mesh Time pts/build-godot-4.0.0 Time To Compile pts/specfem3d-1.0.0 waterlayered_halfspace Model: Water-layered Halfspace pts/cockroach-1.0.2 kv --ramp 10s --read-percent 95 --concurrency 128 Workload: KV, 95% Reads - Concurrency: 128 pts/memtier-benchmark-1.5.0 -P redis -c 100 --ratio=1:5 Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:5 pts/cassandra-1.2.0 WRITE Test: Writes pts/spark-1.0.1 -r 1000000 -p 100 Row Count: 1000000 - Partitions: 100 - SHA-512 Benchmark Time pts/memtier-benchmark-1.5.0 -P redis -c 100 --ratio=1:10 Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:10 pts/specfem3d-1.0.0 tomographic_model Model: Tomographic Model pts/spark-1.0.1 -r 1000000 -p 100 Row Count: 1000000 - Partitions: 100 - Calculate Pi Benchmark pts/specfem3d-1.0.0 Mount_StHelens Model: Mount St. Helens pts/ospray-2.12.0 --benchmark_filter=particle_volume/pathtracer/real_time Benchmark: particle_volume/pathtracer/real_time pts/openfoam-1.2.0 incompressible/simpleFoam/drivaerFastback/ -m M Input: drivaerFastback, Medium Mesh Size - Execution Time pts/mrbayes-1.5.0 Primate Phylogeny Analysis pts/specfem3d-1.0.0 homogeneous_halfspace Model: Homogeneous Halfspace 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/amg-1.1.0 pts/dacapobench-1.0.1 jython Java Test: Jython pts/openradioss-1.0.0 fsi_drop_container_0000.rad fsi_drop_container_0001.rad Model: INIVOL and Fluid Structure Interaction Drop Container 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/gromacs-1.8.0 mpi-build water-cut1.0_GMX50_bare/1536 Implementation: MPI CPU - Input: water_GMX50_bare pts/blender-3.6.0 -b ../bmw27_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: BMW27 - Compute: CPU-Only pts/compress-7zip-1.10.0 Test: Decompression Rating pts/deepsparse-1.5.2 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/specfem3d-1.0.0 layered_halfspace Model: Layered Halfspace pts/deepsparse-1.5.2 zoo:nlp/sentiment_analysis/bert-base/pytorch/huggingface/sst2/12layer_pruned90-none --scenario async Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.5.2 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/embree-1.5.0 pathtracer_ispc -c asian_dragon/asian_dragon.ecs Binary: Pathtracer ISPC - Model: Asian Dragon 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=particle_volume/ao/real_time Benchmark: particle_volume/ao/real_time pts/deepsparse-1.5.2 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/blender-3.6.0 -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/embree-1.5.0 pathtracer_ispc -c crown/crown.ecs Binary: Pathtracer ISPC - Model: Crown pts/openvkl-1.3.0 vklBenchmark --benchmark_filter=ispc Benchmark: vklBenchmark ISPC pts/memtier-benchmark-1.5.0 -P redis -c 50 --ratio=1:10 Protocol: Redis - Clients: 50 - Set To Get Ratio: 1:10 pts/deepsparse-1.5.2 zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/base-none --scenario async Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream 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/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=particle_volume/scivis/real_time Benchmark: particle_volume/scivis/real_time pts/namd-1.2.1 ATPase Simulation - 327,506 Atoms 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/deepsparse-1.5.2 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-1.5.2 zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/pruned90-none --scenario async Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream pts/spark-1.0.1 -r 1000000 -p 100 Row Count: 1000000 - Partitions: 100 - Repartition Test Time