n305 Intel Core i3-N305 testing with a MSI MS-CF03 v1.0 (ECF03IMS.100 BIOS) and MSI Intel Alder Lake-N [UHD ] on Ubuntu 24.04 via the Phoronix Test Suite. 15W: Processor: Intel Core i3-N305 @ 3.80GHz (8 Cores), Motherboard: MSI MS-CF03 v1.0 (ECF03IMS.100 BIOS), Chipset: Intel Alder Lake-N PCH, Memory: 1 x 8GB DDR5-4800MT/s SK Hynix HMCG66MEBSA092N, Disk: 64GB SATA SSD, Graphics: MSI Intel Alder Lake-N [UHD ], Audio: Realtek ALC897, Monitor: DELL P2317H, Network: 2 x Intel I225-V OS: Ubuntu 24.04, Kernel: 6.8.0-49-generic (x86_64), Desktop: GNOME Shell 46.0, Display Server: X Server + Wayland, OpenGL: 4.6 Mesa 24.0.9-0ubuntu0.2, Compiler: GCC 13.2.0, File-System: ext4, Screen Resolution: 1920x1080 Intel Core i3-N305: Processor: Intel Core i3-N305 @ 3.80GHz (8 Cores), Motherboard: MSI MS-CF03 v1.0 (ECF03IMS.100 BIOS), Chipset: Intel Alder Lake-N PCH, Memory: 1 x 8GB DDR5-4800MT/s SK Hynix HMCG66MEBSA092N, Disk: 64GB SATA SSD, Graphics: MSI Intel Alder Lake-N [UHD ], Audio: Realtek ALC897, Monitor: DELL P2317H, Network: 2 x Intel I225-V OS: Ubuntu 24.04, Kernel: 6.8.0-49-generic (x86_64), Desktop: GNOME Shell 46.0, Display Server: X Server + Wayland, OpenGL: 4.6 Mesa 24.0.9-0ubuntu0.2, Compiler: GCC 13.2.0, File-System: ext4, Screen Resolution: 1920x1080 PyPerformance 1.11 Benchmark: asyncio_tcp_ssl Milliseconds < Lower Is Better 15W . 1.7 |==================================================================== PyPerformance 1.11 Benchmark: async_tree_io Milliseconds < Lower Is Better 15W . 1.14 |=================================================================== dav1d 1.4.2 Video Input: Chimera 1080p FPS > Higher Is Better Intel Core i3-N305 . 288.16 |================================================== PyPerformance 1.11 Benchmark: xml_etree Milliseconds < Lower Is Better 15W . 59.5 |=================================================================== PyPerformance 1.11 Benchmark: python_startup Milliseconds < Lower Is Better 15W . 10 |===================================================================== PyPerformance 1.11 Benchmark: asyncio_websockets Milliseconds < Lower Is Better 15W . 381 |==================================================================== PyPerformance 1.11 Benchmark: gc_collect Milliseconds < Lower Is Better 15W . 1.50 |=================================================================== PyPerformance 1.11 Benchmark: raytrace Milliseconds < Lower Is Better 15W . 284 |==================================================================== PyPerformance 1.11 Benchmark: django_template Milliseconds < Lower Is Better 15W . 45.6 |=================================================================== dav1d 1.4.2 Video Input: Summer Nature 1080p FPS > Higher Is Better Intel Core i3-N305 . 388.91 |================================================== PyPerformance 1.11 Benchmark: pathlib Milliseconds < Lower Is Better 15W . 24.1 |=================================================================== dav1d 1.4.2 Video Input: Summer Nature 4K FPS > Higher Is Better Intel Core i3-N305 . 88.85 |=================================================== dav1d 1.4.2 Video Input: Chimera 1080p 10-bit FPS > Higher Is Better Intel Core i3-N305 . 222.16 |================================================== PyPerformance 1.11 Benchmark: pickle_pure_python Milliseconds < Lower Is Better 15W . 277 |==================================================================== FinanceBench 2016-07-25 Benchmark: Repo OpenMP ms < Lower Is Better PyPerformance 1.11 Benchmark: regex_compile Milliseconds < Lower Is Better 15W . 136 |==================================================================== PyPerformance 1.11 Benchmark: nbody Milliseconds < Lower Is Better 15W . 75.7 |=================================================================== FinanceBench 2016-07-25 Benchmark: Bonds OpenMP ms < Lower Is Better PyPerformance 1.11 Benchmark: float Milliseconds < Lower Is Better 15W . 72.4 |=================================================================== PyPerformance 1.11 Benchmark: go Milliseconds < Lower Is Better 15W . 130 |==================================================================== PyPerformance 1.11 Benchmark: crypto_pyaes Milliseconds < Lower Is Better 15W . 67.5 |=================================================================== PyPerformance 1.11 Benchmark: chaos Milliseconds < Lower Is Better 15W . 63.3 |=================================================================== SciMark 2.0 Computational Test: Composite Mflops > Higher Is Better 15W . 558.89 |================================================================= PyPerformance 1.11 Benchmark: json_loads Milliseconds < Lower Is Better 15W . 23.5 |=================================================================== FFTE 7.0 N=256, 3D Complex FFT Routine MFLOPS > Higher Is Better 15W . 26758.34 |=============================================================== SciMark 2.0 Computational Test: Jacobi Successive Over-Relaxation Mflops > Higher Is Better 15W . 1017.46 |================================================================ SciMark 2.0 Computational Test: Dense LU Matrix Factorization Mflops > Higher Is Better 15W . 664.05 |================================================================= SciMark 2.0 Computational Test: Sparse Matrix Multiply Mflops > Higher Is Better 15W . 765.98 |================================================================= SciMark 2.0 Computational Test: Fast Fourier Transform Mflops > Higher Is Better 15W . 170.61 |================================================================= SciMark 2.0 Computational Test: Monte Carlo Mflops > Higher Is Better 15W . 176.35 |=================================================================