Ryzen 7 2700X July

AMD Ryzen 7 2700X Eight-Core testing with a ASUS ROG CROSSHAIR VII HERO (WI-FI) (1201 BIOS) and Sapphire AMD Radeon RX 470/480/570/570X/580/580X/590 4GB on Ubuntu 19.10 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 2007080-NE-RYZEN727054
Jump To Table - Results

Statistics

Remove Outliers Before Calculating Averages

Graph Settings

Prefer Vertical Bar Graphs

Multi-Way Comparison

Condense Multi-Option Tests Into Single Result Graphs

Table

Show Detailed System Result Table

Run Management

Result
Identifier
View Logs
Performance Per
Dollar
Date
Run
  Test
  Duration
Ryzen 7 2700X
July 08 2020
  1 Hour, 10 Minutes
Only show results matching title/arguments (delimit multiple options with a comma):
Do not show results matching title/arguments (delimit multiple options with a comma):

Ryzen 7 2700X July Suite 1.0.0 System Test suite extracted from Ryzen 7 2700X July. system/hugin-1.0.0 Panorama Photo Assistant + Stitching Time pts/montage-1.0.0 Mosaic of M17, K band, 1.5 deg x 1.5 deg pts/daphne-1.0.0 OpenMP ndt_mapping Backend: OpenMP - Kernel: NDT Mapping pts/daphne-1.0.0 OpenMP points2image Backend: OpenMP - Kernel: Points2Image pts/daphne-1.0.0 OpenMP euclidean_cluster Backend: OpenMP - Kernel: Euclidean Cluster pts/onednn-1.5.0 --ip --batch=inputs/ip/ip_1d --cfg=f32 --engine=cpu Harness: IP Batch 1D - Data Type: f32 - Engine: CPU pts/onednn-1.5.0 --ip --batch=inputs/ip/ip_all --cfg=f32 --engine=cpu Harness: IP Batch All - Data Type: f32 - Engine: CPU pts/onednn-1.5.0 --ip --batch=inputs/ip/ip_1d --cfg=u8s8f32 --engine=cpu Harness: IP Batch 1D - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.5.0 --ip --batch=inputs/ip/ip_all --cfg=u8s8f32 --engine=cpu Harness: IP Batch All - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.5.0 --conv --batch=inputs/conv/shapes_auto --cfg=f32 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU pts/onednn-1.5.0 --deconv --batch=inputs/deconv/deconv_1d --cfg=f32 --engine=cpu Harness: Deconvolution Batch deconv_1d - Data Type: f32 - Engine: CPU pts/onednn-1.5.0 --deconv --batch=inputs/deconv/deconv_3d --cfg=f32 --engine=cpu Harness: Deconvolution Batch deconv_3d - Data Type: f32 - Engine: CPU pts/onednn-1.5.0 --conv --batch=inputs/conv/shapes_auto --cfg=u8s8f32 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.5.0 --deconv --batch=inputs/deconv/deconv_1d --cfg=u8s8f32 --engine=cpu Harness: Deconvolution Batch deconv_1d - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.5.0 --deconv --batch=inputs/deconv/deconv_3d --cfg=u8s8f32 --engine=cpu Harness: Deconvolution Batch deconv_3d - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.5.0 --rnn --batch=inputs/rnn/rnn_training --cfg=f32 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU pts/onednn-1.5.0 --rnn --batch=inputs/rnn/rnn_inference --cfg=f32 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU pts/onednn-1.5.0 --matmul --batch=inputs/matmul/shapes_transformer --cfg=f32 --engine=cpu Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU pts/onednn-1.5.0 --matmul --batch=inputs/matmul/shapes_transformer --cfg=u8s8f32 --engine=cpu Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU pts/build-apache-1.6.1 Time To Compile pts/build-linux-kernel-1.10.2 Time To Compile