new one

AMD Ryzen 7 PRO 6850U testing with a LENOVO 21CM0001US (R22ET51W 1.21 BIOS) and AMD Radeon 680M 1GB 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 2310166-NE-NEWONE35909
Jump To Table - Results

View

Do Not Show Noisy Results
Do Not Show Results With Incomplete Data
Do Not Show Results With Little Change/Spread
List Notable Results
Show Result Confidence Charts

Limit displaying results to tests within:

Creator Workloads 4 Tests
Game Development 2 Tests
Multi-Core 4 Tests
Intel oneAPI 4 Tests

Statistics

Show Overall Harmonic Mean(s)
Show Overall Geometric Mean
Show Geometric Means Per-Suite/Category
Show Wins / Losses Counts (Pie Chart)
Normalize Results
Remove Outliers Before Calculating Averages

Graph Settings

Force Line Graphs Where Applicable
Convert To Scalar Where Applicable
Prefer Vertical Bar Graphs

Multi-Way Comparison

Condense Multi-Option Tests Into Single Result Graphs

Table

Show Detailed System Result Table

Run Management

Highlight
Result
Hide
Result
Result
Identifier
View Logs
Performance Per
Dollar
Date
Run
  Test
  Duration
a
October 16 2023
  1 Hour, 9 Minutes
b
October 16 2023
  1 Hour, 8 Minutes
c
October 16 2023
  1 Hour, 8 Minutes
Invert Hiding All Results Option
  1 Hour, 8 Minutes

Only show results where is faster than
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):

new one AMD Ryzen 7 PRO 6850U testing with a LENOVO 21CM0001US (R22ET51W 1.21 BIOS) and AMD Radeon 680M 1GB on Ubuntu 23.10 via the Phoronix Test Suite. a: Processor: AMD Ryzen 7 PRO 6850U @ 2.70GHz (8 Cores / 16 Threads), Motherboard: LENOVO 21CM0001US (R22ET51W 1.21 BIOS), Chipset: AMD 17h-19h PCIe Root Complex, Memory: 16GB, Disk: 512GB Micron MTFDKBA512TFK, Graphics: AMD Radeon 680M 1GB (2200/400MHz), Audio: AMD Rembrandt Radeon HD Audio, Network: Qualcomm QCNFA765 OS: Ubuntu 23.10, Kernel: 6.3.0-7-generic (x86_64), Desktop: GNOME Shell, Display Server: X Server + Wayland, OpenGL: 4.6 Mesa 23.1.7-1ubuntu1 (LLVM 15.0.7 DRM 3.52), Compiler: GCC 13.2.0, File-System: ext4, Screen Resolution: 1920x1200 b: Processor: AMD Ryzen 7 PRO 6850U @ 2.70GHz (8 Cores / 16 Threads), Motherboard: LENOVO 21CM0001US (R22ET51W 1.21 BIOS), Chipset: AMD 17h-19h PCIe Root Complex, Memory: 16GB, Disk: 512GB Micron MTFDKBA512TFK, Graphics: AMD Radeon 680M 1GB (2200/400MHz), Audio: AMD Rembrandt Radeon HD Audio, Network: Qualcomm QCNFA765 OS: Ubuntu 23.10, Kernel: 6.3.0-7-generic (x86_64), Desktop: GNOME Shell, Display Server: X Server + Wayland, OpenGL: 4.6 Mesa 23.2.1-1ubuntu3 (LLVM 15.0.7 DRM 3.52), Compiler: GCC 13.2.0, File-System: ext4, Screen Resolution: 1920x1200 c: Processor: AMD Ryzen 7 PRO 6850U @ 2.70GHz (8 Cores / 16 Threads), Motherboard: LENOVO 21CM0001US (R22ET51W 1.21 BIOS), Chipset: AMD 17h-19h PCIe Root Complex, Memory: 16GB, Disk: 512GB Micron MTFDKBA512TFK, Graphics: AMD Radeon 680M 1GB (2200/400MHz), Audio: AMD Rembrandt Radeon HD Audio, Network: Qualcomm QCNFA765 OS: Ubuntu 23.10, Kernel: 6.3.0-7-generic (x86_64), Desktop: GNOME Shell, Display Server: X Server + Wayland, OpenGL: 4.6 Mesa 23.2.1-1ubuntu3 (LLVM 15.0.7 DRM 3.52), Compiler: GCC 13.2.0, File-System: ext4, Screen Resolution: 1920x1200 Embree 4.3 Binary: Pathtracer ISPC - Model: Crown Frames Per Second > Higher Is Better a . 5.7412 |============================================================== b . 6.2439 |=================================================================== c . 6.2000 |=================================================================== Embree 4.3 Binary: Pathtracer - Model: Crown Frames Per Second > Higher Is Better a . 6.5897 |=============================================================== b . 6.9878 |=================================================================== c . 6.9056 |================================================================== OpenVKL 2.0.0 Benchmark: vklBenchmarkCPU ISPC Items / Sec > Higher Is Better a . 111 |=================================================================== b . 116 |====================================================================== c . 116 |====================================================================== Embree 4.3 Binary: Pathtracer - Model: Asian Dragon Obj Frames Per Second > Higher Is Better a . 7.4507 |================================================================= b . 7.7328 |=================================================================== c . 7.6607 |================================================================== OpenVKL 2.0.0 Benchmark: vklBenchmarkCPU Scalar Items / Sec > Higher Is Better a . 57 |===================================================================== b . 59 |======================================================================= c . 59 |======================================================================= Embree 4.3 Binary: Pathtracer - Model: Asian Dragon Frames Per Second > Higher Is Better a . 8.3210 |================================================================= b . 8.5531 |=================================================================== c . 8.4530 |================================================================== oneDNN 3.3 Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 11.28 |==================================================================== b . 10.99 |================================================================== c . 11.20 |==================================================================== oneDNN 3.3 Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 3.5173 |=================================================================== b . 3.4339 |================================================================= c . 3.4494 |================================================================== Embree 4.3 Binary: Pathtracer ISPC - Model: Asian Dragon Obj Frames Per Second > Higher Is Better a . 6.5408 |================================================================== b . 6.6885 |=================================================================== c . 6.6641 |=================================================================== oneDNN 3.3 Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 6333.19 |================================================================== b . 6202.46 |================================================================= c . 6200.82 |================================================================= oneDNN 3.3 Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 2.24757 |================================================================== b . 2.20351 |================================================================= c . 2.20167 |================================================================= oneDNN 3.3 Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 4.96016 |================================================================== b . 4.86017 |================================================================= c . 4.87865 |================================================================= Embree 4.3 Binary: Pathtracer ISPC - Model: Asian Dragon Frames Per Second > Higher Is Better a . 7.5532 |================================================================== b . 7.7030 |=================================================================== c . 7.6536 |=================================================================== oneDNN 3.3 Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 6.26012 |================================================================== b . 6.17242 |================================================================= c . 6.26704 |================================================================== oneDNN 3.3 Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 10.31 |==================================================================== b . 10.16 |=================================================================== c . 10.18 |=================================================================== oneDNN 3.3 Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 6307.93 |================================================================== b . 6255.44 |================================================================= c . 6285.76 |================================================================== oneDNN 3.3 Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 3308.35 |================================================================== b . 3286.82 |================================================================== c . 3283.97 |================================================================== oneDNN 3.3 Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 13.13 |==================================================================== b . 13.12 |==================================================================== c . 13.21 |==================================================================== easyWave r34 Input: e2Asean Grid + BengkuluSept2007 Source - Time: 2400 Seconds < Lower Is Better a . 591.35 |=================================================================== b . 593.54 |=================================================================== c . 594.86 |=================================================================== easyWave r34 Input: e2Asean Grid + BengkuluSept2007 Source - Time: 1200 Seconds < Lower Is Better a . 237.90 |=================================================================== b . 238.09 |=================================================================== c . 239.05 |=================================================================== oneDNN 3.3 Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 2.51621 |================================================================== b . 2.51250 |================================================================== c . 2.50565 |================================================================== oneDNN 3.3 Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 3309.44 |================================================================== b . 3296.00 |================================================================== c . 3297.18 |================================================================== oneDNN 3.3 Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 12.23 |==================================================================== b . 12.27 |==================================================================== c . 12.27 |==================================================================== oneDNN 3.3 Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better a . 6274.48 |================================================================== b . 6254.99 |================================================================== c . 6265.68 |================================================================== oneDNN 3.3 Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better a . 3316.68 |================================================================== b . 3323.67 |================================================================== c . 3319.67 |================================================================== oneDNN 3.3 Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 8.16955 |================================================================== b . 8.15304 |================================================================== c . 8.15973 |================================================================== easyWave r34 Input: e2Asean Grid + BengkuluSept2007 Source - Time: 240 Seconds < Lower Is Better a . 11.17 |==================================================================== b . 11.19 |==================================================================== c . 11.17 |==================================================================== Intel Open Image Denoise 2.1 Run: RTLightmap.hdr.4096x4096 - Device: CPU-Only Images / Sec > Higher Is Better a . 0.11 |===================================================================== b . 0.11 |===================================================================== c . 0.11 |===================================================================== Intel Open Image Denoise 2.1 Run: RT.ldr_alb_nrm.3840x2160 - Device: CPU-Only Images / Sec > Higher Is Better a . 0.21 |===================================================================== b . 0.21 |===================================================================== c . 0.21 |===================================================================== Intel Open Image Denoise 2.1 Run: RT.hdr_alb_nrm.3840x2160 - Device: CPU-Only Images / Sec > Higher Is Better a . 0.21 |===================================================================== b . 0.21 |===================================================================== c . 0.21 |===================================================================== oneDNN 3.3 Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 3.3 Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 3.3 Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 3.3 Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 3.3 Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better