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
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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/cloverleaf-1.1.0 Lagrangian-Eulerian Hydrodynamics pts/amg-1.1.0 pts/pennant-1.1.0 sedovbig/sedovbig.pnt Test: sedovbig pts/pennant-1.1.0 leblancbig/leblancbig.pnt Test: leblancbig 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/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/renaissance-1.3.0 dotty Test: Scala Dotty pts/renaissance-1.3.0 dec-tree Test: Random Forest pts/renaissance-1.3.0 movie-lens Test: ALS Movie Lens pts/renaissance-1.3.0 als Test: Apache Spark ALS pts/renaissance-1.3.0 naive-bayes Test: Apache Spark Bayes pts/renaissance-1.3.0 reactors Test: Savina Reactors.IO pts/renaissance-1.3.0 page-rank Test: Apache Spark PageRank pts/renaissance-1.3.0 finagle-http Test: Finagle HTTP Requests pts/renaissance-1.3.0 db-shootout Test: In-Memory Database Shootout pts/renaissance-1.3.0 akka-uct Test: Akka Unbalanced Cobwebbed Tree pts/renaissance-1.3.0 future-genetic Test: Genetic Algorithm Using Jenetics + Futures system/gnuradio-1.0.0 Test: Five Back to Back FIR Filters system/gnuradio-1.0.0 Test: Signal Source (Cosine) system/gnuradio-1.0.0 Test: FIR Filter system/gnuradio-1.0.0 Test: IIR Filter system/gnuradio-1.0.0 Test: FM Deemphasis Filter system/gnuradio-1.0.0 Test: Hilbert Transform pts/mt-dgemm-1.2.0 Sustained Floating-Point Rate pts/compress-7zip-1.10.0 Test: Compression Rating pts/compress-7zip-1.10.0 Test: Decompression Rating pts/build-godot-4.0.0 Time To Compile pts/build-linux-kernel-1.15.0 defconfig Build: defconfig pts/build2-1.2.0 Time To Compile 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_3d --cfg=f32 --engine=cpu Harness: IP Shapes 3D - Data Type: f32 - 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/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 --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_3d --cfg=bf16bf16bf16 --engine=cpu Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - 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 --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_3d --cfg=f32 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: f32 - 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 --deconv --batch=inputs/deconv/shapes_1d --cfg=u8s8f32 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - 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/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_inference_lb --cfg=f32 --engine=cpu Harness: Recurrent Neural Network Inference - 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 --conv --batch=inputs/conv/shapes_auto --cfg=bf16bf16bf16 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU 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_3d --cfg=bf16bf16bf16 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU 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_training --cfg=bf16bf16bf16 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - 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/clickhouse-1.2.0 100M Rows Hits Dataset, First Run / Cold Cache pts/clickhouse-1.2.0 100M Rows Hits Dataset, Second Run pts/clickhouse-1.2.0 100M Rows Hits Dataset, Third Run 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/askap-2.1.0 tConvolveMT Test: tConvolve MT - Gridding pts/askap-2.1.0 tConvolveMT Test: tConvolve MT - Degridding pts/askap-2.1.0 tConvolveMPI Test: tConvolve MPI - Degridding pts/askap-2.1.0 tConvolveMPI Test: tConvolve MPI - Gridding pts/askap-2.1.0 tConvolve OpenCL Test: tConvolve OpenCL pts/askap-2.1.0 tConvolveOMP Test: tConvolve OpenMP - Gridding pts/askap-2.1.0 tConvolveOMP Test: tConvolve OpenMP - Degridding pts/askap-2.1.0 tHogbomCleanOMP Test: Hogbom Clean OpenMP pts/astcenc-1.4.0 -fast -repeats 120 Preset: Fast pts/astcenc-1.4.0 -medium -repeats 20 Preset: Medium pts/astcenc-1.4.0 -thorough -repeats 10 Preset: Thorough pts/astcenc-1.4.0 -exhaustive -repeats 2 Preset: Exhaustive pts/tensorflow-2.1.0 --device cpu --batch_size=32 --model=alexnet Device: CPU - Batch Size: 32 - Model: AlexNet pts/tensorflow-2.1.0 --device cpu --batch_size=64 --model=alexnet Device: CPU - Batch Size: 64 - Model: AlexNet 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=32 --model=resnet50 Device: CPU - Batch Size: 32 - Model: ResNet-50 pts/tensorflow-2.1.0 --device cpu --batch_size=64 --model=googlenet Device: CPU - Batch Size: 64 - Model: GoogLeNet pts/tensorflow-2.1.0 --device cpu --batch_size=64 --model=resnet50 Device: CPU - Batch Size: 64 - Model: ResNet-50 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/numenta-nab-1.1.1 -d knncad Detector: KNN CAD pts/numenta-nab-1.1.1 -d relativeEntropy Detector: Relative Entropy pts/numenta-nab-1.1.1 -d windowedGaussian Detector: Windowed Gaussian pts/numenta-nab-1.1.1 -d earthgeckoSkyline Detector: Earthgecko Skyline pts/numenta-nab-1.1.1 -d bayesChangePt Detector: Bayesian Changepoint pts/numenta-nab-1.1.1 -d contextOSE Detector: Contextual Anomaly Detector OSE pts/nginx-3.0.1 -c 100 Connections: 100 pts/nginx-3.0.1 -c 200 Connections: 200 pts/nginx-3.0.1 -c 500 Connections: 500 pts/nginx-3.0.1 -c 1000 Connections: 1000 pts/apache-3.0.0 -c 100 Concurrent Requests: 100 pts/apache-3.0.0 -c 200 Concurrent Requests: 200 pts/apache-3.0.0 -c 500 Concurrent Requests: 500 pts/apache-3.0.0 -c 1000 Concurrent Requests: 1000