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Intel Core i7-1280P testing with a MSI MS-14C6 (E14C6IMS.115 BIOS) and MSI Intel ADL GT2 15GB 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 2310187-NE-DDDS2145567
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October 18 2023
  1 Hour, 33 Minutes
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October 18 2023
  1 Hour, 40 Minutes
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October 18 2023
  1 Hour, 41 Minutes
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ddds Intel Core i7-1280P testing with a MSI MS-14C6 (E14C6IMS.115 BIOS) and MSI Intel ADL GT2 15GB on Ubuntu 23.10 via the Phoronix Test Suite. a: Processor: Intel Core i7-1280P @ 4.70GHz (14 Cores / 20 Threads), Motherboard: MSI MS-14C6 (E14C6IMS.115 BIOS), Chipset: Intel Alder Lake PCH, Memory: 16GB, Disk: 1024GB Micron_3400_MTFDKBA1T0TFH, Graphics: MSI Intel ADL GT2 15GB (1450MHz), Audio: Realtek ALC274, Network: Intel Alder Lake-P PCH CNVi WiFi 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, OpenCL: OpenCL 3.0, Compiler: GCC 13.2.0, File-System: ext4, Screen Resolution: 1920x1080 b: Processor: Intel Core i7-1280P @ 4.70GHz (14 Cores / 20 Threads), Motherboard: MSI MS-14C6 (E14C6IMS.115 BIOS), Chipset: Intel Alder Lake PCH, Memory: 16GB, Disk: 1024GB Micron_3400_MTFDKBA1T0TFH, Graphics: MSI Intel ADL GT2 15GB (1450MHz), Audio: Realtek ALC274, Network: Intel Alder Lake-P PCH CNVi WiFi 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, OpenCL: OpenCL 3.0, Compiler: GCC 13.2.0, File-System: ext4, Screen Resolution: 1920x1080 c: Processor: Intel Core i7-1280P @ 4.70GHz (14 Cores / 20 Threads), Motherboard: MSI MS-14C6 (E14C6IMS.115 BIOS), Chipset: Intel Alder Lake PCH, Memory: 16GB, Disk: 1024GB Micron_3400_MTFDKBA1T0TFH, Graphics: MSI Intel ADL GT2 15GB (1450MHz), Audio: Realtek ALC274, Network: Intel Alder Lake-P PCH CNVi WiFi 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, OpenCL: OpenCL 3.0, Compiler: GCC 13.2.0, File-System: ext4, Screen Resolution: 1920x1080 oneDNN 3.3 Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 11.45740 |================================================================= b . 9.14804 |==================================================== c . 7.77802 |============================================ oneDNN 3.3 Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 5.63896 |===================================================== b . 6.98825 |================================================================== c . 5.85137 |======================================================= oneDNN 3.3 Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 2.27679 |================================================ b . 3.14049 |================================================================== c . 2.49493 |==================================================== easyWave r34 Input: e2Asean Grid + BengkuluSept2007 Source - Time: 240 Seconds < Lower Is Better a . 10.322 |=================================================================== b . 9.919 |================================================================ c . 9.803 |================================================================ oneDNN 3.3 Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 1.71431 |===================================== b . 3.08209 |================================================================== c . 2.25098 |================================================ oneDNN 3.3 Harness: IP Shapes 1D - 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: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 8.75450 |================================================================= b . 8.83358 |================================================================== c . 8.76650 |================================================================= oneDNN 3.3 Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 10.05 |============================================================== b . 10.25 |================================================================ c . 10.95 |==================================================================== oneDNN 3.3 Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 7.79179 |============================================================== b . 7.84281 |============================================================== c . 8.28649 |================================================================== oneDNN 3.3 Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 2.72830 |============================================================ b . 2.57218 |======================================================== c . 3.01476 |================================================================== oneDNN 3.3 Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 3.66772 |================================================================== b . 3.68241 |================================================================== c . 3.68788 |================================================================== easyWave r34 Input: e2Asean Grid + BengkuluSept2007 Source - Time: 2400 Seconds < Lower Is Better a . 555.28 |============================================================= b . 593.05 |================================================================== c . 605.18 |=================================================================== easyWave r34 Input: e2Asean Grid + BengkuluSept2007 Source - Time: 1200 Seconds < Lower Is Better a . 215.08 |======================================================== b . 240.10 |=============================================================== c . 255.60 |=================================================================== oneDNN 3.3 Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 9.51050 |================================================================== b . 9.55980 |================================================================== c . 9.55125 |================================================================== oneDNN 3.3 Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 8810.16 |============================================================= b . 9201.00 |================================================================ c . 9513.20 |================================================================== oneDNN 3.3 Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 4847.73 |============================================================== b . 5161.73 |================================================================== c . 5159.09 |================================================================== oneDNN 3.3 Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 9189.71 |============================================================= b . 9853.70 |================================================================= c . 9946.69 |================================================================== oneDNN 3.3 Harness: Convolution Batch Shapes Auto - 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: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 3.3 Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 4517.55 |================================================================ b . 4625.90 |================================================================== c . 4623.65 |================================================================== oneDNN 3.3 Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better a . 9056.79 |=========================================================== b . 10044.60 |================================================================= c . 9116.27 |=========================================================== oneDNN 3.3 Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better a . 4772.43 |============================================================ b . 5236.44 |================================================================== c . 4733.46 |============================================================ Embree 4.3 Binary: Pathtracer - Model: Crown Frames Per Second > Higher Is Better a . 7.5237 |=================================================================== b . 5.8001 |==================================================== c . 4.9874 |============================================ Embree 4.3 Binary: Pathtracer ISPC - Model: Crown Frames Per Second > Higher Is Better a . 7.7035 |=================================================================== b . 5.8382 |=================================================== c . 5.3040 |============================================== Embree 4.3 Binary: Pathtracer - Model: Asian Dragon Frames Per Second > Higher Is Better a . 9.4849 |=================================================================== b . 7.3744 |==================================================== c . 6.2796 |============================================ Embree 4.3 Binary: Pathtracer - Model: Asian Dragon Obj Frames Per Second > Higher Is Better a . 8.5685 |=================================================================== b . 6.5378 |=================================================== c . 5.6884 |============================================ Embree 4.3 Binary: Pathtracer ISPC - Model: Asian Dragon Frames Per Second > Higher Is Better a . 10.1820 |================================================================== b . 7.8561 |=================================================== c . 7.1952 |=============================================== Embree 4.3 Binary: Pathtracer oneAPI SYCL - Model: Crown Frames Per Second > Higher Is Better Embree 4.3 Binary: Pathtracer ISPC - Model: Asian Dragon Obj Frames Per Second > Higher Is Better a . 8.8968 |=================================================================== b . 6.8812 |==================================================== c . 6.8098 |=================================================== Embree 4.3 Binary: Pathtracer oneAPI SYCL - Model: Asian Dragon Frames Per Second > Higher Is Better Embree 4.3 Binary: Pathtracer oneAPI SYCL - Model: Asian Dragon Obj Frames Per Second > Higher Is Better Intel Open Image Denoise 2.1 Run: RT.hdr_alb_nrm.3840x2160 - Device: CPU-Only Images / Sec > Higher Is Better a . 0.22 |===================================================================== b . 0.18 |======================================================== c . 0.19 |============================================================ Intel Open Image Denoise 2.1 Run: RT.ldr_alb_nrm.3840x2160 - Device: CPU-Only Images / Sec > Higher Is Better a . 0.21 |===================================================================== b . 0.17 |======================================================== c . 0.17 |======================================================== Intel Open Image Denoise 2.1 Run: RTLightmap.hdr.4096x4096 - Device: CPU-Only Images / Sec > Higher Is Better a . 0.10 |===================================================================== b . 0.08 |======================================================= c . 0.08 |======================================================= OpenVKL 2.0.0 Benchmark: vklBenchmarkCPU ISPC Items / Sec > Higher Is Better a . 161 |====================================================================== b . 132 |========================================================= c . 131 |========================================================= OpenVKL 2.0.0 Benchmark: vklBenchmarkCPU Scalar Items / Sec > Higher Is Better a . 74 |======================================================================= b . 57 |======================================================= c . 58 |======================================================== OpenVKL 2.0.0 Benchmark: vklBenchmarkGPU Intel oneAPI SYCL Items / Sec > Higher Is Better a . 137 |================================================================== b . 139 |=================================================================== c . 145 |====================================================================== FluidX3D 2.9 Test: FP32-FP32 MLUPs/s > Higher Is Better a . 369 |====================================================================== b . 365 |===================================================================== c . 368 |====================================================================== FluidX3D 2.9 Test: FP32-FP16C MLUPs/s > Higher Is Better a . 609 |===================================================================== b . 616 |====================================================================== c . 618 |====================================================================== FluidX3D 2.9 Test: FP32-FP16S MLUPs/s > Higher Is Better a . 646 |====================================================================== b . 649 |====================================================================== c . 644 |=====================================================================