AMD Ryzen 7 7800X3D Linux

Tests for a future article. AMD Ryzen 7 7800X3D 8-Core testing with a ASUS ROG CROSSHAIR X670E HERO (9927 BIOS) and AMD Radeon RX 7900 XTX on Ubuntu 23.04 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 2304054-PTS-AMDRYZEN15
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Timed Code Compilation 3 Tests
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HPC - High Performance Computing 9 Tests
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Multi-Core 9 Tests
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Programmer / Developer System Benchmarks 5 Tests
Python Tests 4 Tests
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Server 4 Tests
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April 05 2023
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AMD Ryzen 7 7800X3D Linux Suite 1.0.0 System Test suite extracted from AMD Ryzen 7 7800X3D Linux. pts/openfoam-1.2.0 incompressible/simpleFoam/drivaerFastback/ -m M Input: drivaerFastback, Medium Mesh Size - Execution Time pts/openfoam-1.2.0 incompressible/simpleFoam/drivaerFastback/ -m M Input: drivaerFastback, Medium Mesh Size - Mesh Time system/gnuradio-1.0.0 Test: Hilbert Transform system/gnuradio-1.0.0 Test: FM Deemphasis Filter system/gnuradio-1.0.0 Test: IIR Filter system/gnuradio-1.0.0 Test: FIR Filter system/gnuradio-1.0.0 Test: Signal Source (Cosine) system/gnuradio-1.0.0 Test: Five Back to Back FIR Filters pts/build-godot-4.0.0 Time To Compile pts/tensorflow-2.1.0 --device cpu --batch_size=64 --model=resnet50 Device: CPU - Batch Size: 64 - Model: ResNet-50 pts/clickhouse-1.2.0 100M Rows Hits Dataset, Third Run pts/clickhouse-1.2.0 100M Rows Hits Dataset, Second Run pts/clickhouse-1.2.0 100M Rows Hits Dataset, First Run / Cold Cache 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 S Input: drivaerFastback, Small Mesh Size - Mesh Time pts/numenta-nab-1.1.1 -d knncad Detector: KNN CAD pts/renaissance-1.3.0 movie-lens Test: ALS Movie Lens pts/build2-1.2.0 Time To Compile pts/renaissance-1.3.0 akka-uct Test: Akka Unbalanced Cobwebbed Tree pts/tensorflow-2.1.0 --device cpu --batch_size=32 --model=resnet50 Device: CPU - Batch Size: 32 - Model: ResNet-50 pts/xmrig-1.1.0 --bench=1M Variant: Monero - Hash Count: 1M pts/blender-3.5.0 -b ../bmw27_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: BMW27 - Compute: CPU-Only pts/xmrig-1.1.0 -a rx/wow --bench=1M Variant: Wownero - Hash Count: 1M pts/nginx-3.0.1 -c 500 Connections: 500 pts/nginx-3.0.1 -c 1000 Connections: 1000 pts/apache-3.0.0 -c 500 Concurrent Requests: 500 pts/apache-3.0.0 -c 1000 Concurrent Requests: 1000 pts/nginx-3.0.1 -c 200 Connections: 200 pts/nginx-3.0.1 -c 100 Connections: 100 pts/apache-3.0.0 -c 200 Concurrent Requests: 200 pts/apache-3.0.0 -c 100 Concurrent Requests: 100 pts/onednn-3.1.0 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=f32 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU pts/onednn-3.1.0 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=u8s8f32 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.1.0 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=bf16bf16bf16 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU pts/build-linux-kernel-1.15.0 defconfig Build: defconfig pts/onednn-3.1.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=u8s8f32 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.1.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=bf16bf16bf16 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.1.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=f32 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU pts/tensorflow-2.1.0 --device cpu --batch_size=64 --model=googlenet Device: CPU - Batch Size: 64 - Model: GoogLeNet pts/memcached-1.2.0 --ratio=1:5 Set To Get Ratio: 1:5 pts/memcached-1.2.0 --ratio=1:10 Set To Get Ratio: 1:10 pts/memcached-1.2.0 --ratio=1:100 Set To Get Ratio: 1:100 pts/renaissance-1.3.0 als Test: Apache Spark ALS pts/numenta-nab-1.1.1 -d earthgeckoSkyline Detector: Earthgecko Skyline pts/astcenc-1.4.0 -exhaustive -repeats 2 Preset: Exhaustive pts/renaissance-1.3.0 page-rank Test: Apache Spark PageRank pts/askap-2.1.0 tConvolveMT Test: tConvolve MT - Degridding pts/askap-2.1.0 tConvolveMT Test: tConvolve MT - Gridding pts/renaissance-1.3.0 future-genetic Test: Genetic Algorithm Using Jenetics + Futures pts/pennant-1.1.0 sedovbig/sedovbig.pnt Test: sedovbig pts/renaissance-1.3.0 reactors Test: Savina Reactors.IO pts/cloverleaf-1.1.0 Lagrangian-Eulerian Hydrodynamics pts/mt-dgemm-1.2.0 Sustained Floating-Point Rate pts/tensorflow-2.1.0 --device cpu --batch_size=32 --model=googlenet Device: CPU - Batch Size: 32 - Model: GoogLeNet pts/tensorflow-2.1.0 --device cpu --batch_size=64 --model=alexnet Device: CPU - Batch Size: 64 - Model: AlexNet pts/renaissance-1.3.0 dotty Test: Scala Dotty pts/astcenc-1.4.0 -thorough -repeats 10 Preset: Thorough pts/pennant-1.1.0 leblancbig/leblancbig.pnt Test: leblancbig pts/renaissance-1.3.0 naive-bayes Test: Apache Spark Bayes pts/numenta-nab-1.1.1 -d contextOSE Detector: Contextual Anomaly Detector OSE pts/renaissance-1.3.0 finagle-http Test: Finagle HTTP Requests pts/tensorflow-2.1.0 --device cpu --batch_size=32 --model=alexnet Device: CPU - Batch Size: 32 - Model: AlexNet pts/renaissance-1.3.0 dec-tree Test: Random Forest pts/onednn-3.1.0 --deconv --batch=inputs/deconv/shapes_1d --cfg=bf16bf16bf16 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.1.0 --deconv --batch=inputs/deconv/shapes_1d --cfg=f32 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU pts/onednn-3.1.0 --deconv --batch=inputs/deconv/shapes_1d --cfg=u8s8f32 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU pts/astcenc-1.4.0 -fast -repeats 120 Preset: Fast pts/compress-7zip-1.10.0 Test: Decompression Rating pts/compress-7zip-1.10.0 Test: Compression Rating pts/numenta-nab-1.1.1 -d bayesChangePt Detector: Bayesian Changepoint pts/onednn-3.1.0 --ip --batch=inputs/ip/shapes_1d --cfg=f32 --engine=cpu Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU pts/onednn-3.1.0 --ip --batch=inputs/ip/shapes_1d --cfg=bf16bf16bf16 --engine=cpu Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.1.0 --ip --batch=inputs/ip/shapes_1d --cfg=u8s8f32 --engine=cpu Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU pts/askap-2.1.0 tConvolveMPI Test: tConvolve MPI - Gridding pts/askap-2.1.0 tConvolveMPI Test: tConvolve MPI - Degridding pts/amg-1.1.0 pts/askap-2.1.0 tHogbomCleanOMP Test: Hogbom Clean OpenMP pts/astcenc-1.4.0 -medium -repeats 20 Preset: Medium pts/numenta-nab-1.1.1 -d relativeEntropy Detector: Relative Entropy pts/onednn-3.1.0 --ip --batch=inputs/ip/shapes_3d --cfg=bf16bf16bf16 --engine=cpu Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.1.0 --ip --batch=inputs/ip/shapes_3d --cfg=f32 --engine=cpu Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU pts/onednn-3.1.0 --ip --batch=inputs/ip/shapes_3d --cfg=u8s8f32 --engine=cpu Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.1.0 --conv --batch=inputs/conv/shapes_auto --cfg=u8s8f32 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.1.0 --conv --batch=inputs/conv/shapes_auto --cfg=f32 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU pts/onednn-3.1.0 --conv --batch=inputs/conv/shapes_auto --cfg=bf16bf16bf16 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU pts/numenta-nab-1.1.1 -d windowedGaussian Detector: Windowed Gaussian pts/askap-2.1.0 tConvolveOMP Test: tConvolve OpenMP - Degridding pts/askap-2.1.0 tConvolveOMP Test: tConvolve OpenMP - Gridding pts/onednn-3.1.0 --deconv --batch=inputs/deconv/shapes_3d --cfg=bf16bf16bf16 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.1.0 --deconv --batch=inputs/deconv/shapes_3d --cfg=f32 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU pts/onednn-3.1.0 --deconv --batch=inputs/deconv/shapes_3d --cfg=u8s8f32 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU pts/renaissance-1.3.0 db-shootout Test: In-Memory Database Shootout pts/askap-2.1.0 tConvolve OpenCL Test: tConvolve OpenCL