sun AMD Ryzen 9 7950X 16-Core testing with a ASUS ROG STRIX X670E-E GAMING WIFI (1416 BIOS) and AMD Radeon RX 7900 XTX on Ubuntu 23.04 via the Phoronix Test Suite. a: Processor: AMD Ryzen 9 7950X 16-Core @ 4.50GHz (16 Cores / 32 Threads), Motherboard: ASUS ROG STRIX X670E-E GAMING WIFI (1416 BIOS), Chipset: AMD Device 14d8, Memory: 32GB, Disk: Western Digital WD_BLACK SN850X 1000GB + 2000GB, Graphics: AMD Radeon RX 7900 XTX (2304/1249MHz), Audio: AMD Device ab30, Monitor: ASUS MG28U, Network: Intel I225-V + Intel Wi-Fi 6 AX210/AX211/AX411 OS: Ubuntu 23.04, Kernel: 6.2.0-20-generic (x86_64), Desktop: GNOME Shell 44.1, Display Server: X Server 1.21.1.7 + Wayland, OpenGL: 4.6 Mesa 23.2.0-devel (git-926e97d 2023-06-12 lunar-oibaf-ppa) (LLVM 15.0.7 DRM 3.49), Compiler: GCC 12.2.0, File-System: ext4, Screen Resolution: 3840x2160 b: Processor: AMD Ryzen 9 7950X 16-Core @ 4.50GHz (16 Cores / 32 Threads), Motherboard: ASUS ROG STRIX X670E-E GAMING WIFI (1416 BIOS), Chipset: AMD Device 14d8, Memory: 32GB, Disk: Western Digital WD_BLACK SN850X 1000GB + 2000GB, Graphics: AMD Radeon RX 7900 XTX (2304/1249MHz), Audio: AMD Device ab30, Monitor: ASUS MG28U, Network: Intel I225-V + Intel Wi-Fi 6 AX210/AX211/AX411 OS: Ubuntu 23.04, Kernel: 6.2.0-20-generic (x86_64), Desktop: GNOME Shell 44.1, Display Server: X Server 1.21.1.7 + Wayland, OpenGL: 4.6 Mesa 23.2.0-devel (git-926e97d 2023-06-12 lunar-oibaf-ppa) (LLVM 15.0.7 DRM 3.49), Compiler: GCC 12.2.0, File-System: ext4, Screen Resolution: 3840x2160 c: Processor: AMD Ryzen 9 7950X 16-Core @ 4.50GHz (16 Cores / 32 Threads), Motherboard: ASUS ROG STRIX X670E-E GAMING WIFI (1416 BIOS), Chipset: AMD Device 14d8, Memory: 32GB, Disk: Western Digital WD_BLACK SN850X 1000GB + 2000GB, Graphics: AMD Radeon RX 7900 XTX (2304/1249MHz), Audio: AMD Device ab30, Monitor: ASUS MG28U, Network: Intel I225-V + Intel Wi-Fi 6 AX210/AX211/AX411 OS: Ubuntu 23.04, Kernel: 6.2.0-20-generic (x86_64), Desktop: GNOME Shell 44.1, Display Server: X Server 1.21.1.7 + Wayland, OpenGL: 4.6 Mesa 23.2.0-devel (git-926e97d 2023-06-12 lunar-oibaf-ppa) (LLVM 15.0.7 DRM 3.49), Compiler: GCC 12.2.0, File-System: ext4, Screen Resolution: 3840x2160 GPAW 23.6 Input: Carbon Nanotube Seconds < Lower Is Better a . 139.38 |=================================================================== b . 139.20 |=================================================================== Palabos 2.3 Grid Size: 100 Mega Site Updates Per Second > Higher Is Better a . 92.33 |=================================================================== b . 93.98 |==================================================================== Palabos 2.3 Grid Size: 400 Mega Site Updates Per Second > Higher Is Better a . 103.95 |=================================================================== b . 104.60 |=================================================================== Palabos 2.3 Grid Size: 500 Mega Site Updates Per Second > Higher Is Better a . 106.88 |=================================================================== b . 107.17 |=================================================================== HeFFTe - Highly Efficient FFT for Exascale 2.3 Test: c2c - Backend: Stock - Precision: double - X Y Z: 256 GFLOP/s > Higher Is Better a . 9.40970 |=========================================================== b . 10.37630 |================================================================= HeFFTe - Highly Efficient FFT for Exascale 2.3 Test: c2c - Backend: FFTW - Precision: double - X Y Z: 256 GFLOP/s > Higher Is Better a . 9.31815 |=========================================================== b . 10.27890 |================================================================= c . 10.27950 |================================================================= Monte Carlo Simulations of Ionised Nebulae 2.02.73.3 Input: Dust 2D tau100.0 Seconds < Lower Is Better a . 64.18 |==================================================================== b . 63.45 |=================================================================== CP2K Molecular Dynamics 2023.1 Input: Fayalite-FIST Seconds < Lower Is Better a . 80.08 |=================================================================== b . 81.60 |==================================================================== HeFFTe - Highly Efficient FFT for Exascale 2.3 Test: r2c - Backend: FFTW - Precision: double - X Y Z: 256 GFLOP/s > Higher Is Better a . 17.26 |============================================================= b . 19.30 |==================================================================== c . 19.06 |=================================================================== HeFFTe - Highly Efficient FFT for Exascale 2.3 Test: c2c - Backend: FFTW - Precision: float - X Y Z: 256 GFLOP/s > Higher Is Better a . 18.58 |============================================================= b . 20.59 |==================================================================== c . 20.68 |==================================================================== HeFFTe - Highly Efficient FFT for Exascale 2.3 Test: c2c - Backend: Stock - Precision: float - X Y Z: 256 GFLOP/s > Higher Is Better a . 18.61 |============================================================ b . 20.80 |=================================================================== c . 20.99 |==================================================================== CP2K Molecular Dynamics 2023.1 Input: H20-64 Seconds < Lower Is Better a . 44.18 |==================================================================== b . 43.86 |==================================================================== HeFFTe - Highly Efficient FFT for Exascale 2.3 Test: c2c - Backend: FFTW - Precision: double-long - X Y Z: 256 GFLOP/s > Higher Is Better a . 10.22 |=================================================================== b . 10.30 |==================================================================== HeFFTe - Highly Efficient FFT for Exascale 2.3 Test: c2c - Backend: Stock - Precision: double-long - X Y Z: 256 GFLOP/s > Higher Is Better a . 10.29 |==================================================================== b . 10.36 |==================================================================== HeFFTe - Highly Efficient FFT for Exascale 2.3 Test: r2c - Backend: Stock - Precision: float - X Y Z: 256 GFLOP/s > Higher Is Better a . 36.71 |======================================================= b . 45.70 |==================================================================== HeFFTe - Highly Efficient FFT for Exascale 2.3 Test: r2c - Backend: FFTW - Precision: float - X Y Z: 256 GFLOP/s > Higher Is Better a . 35.43 |========================================================== b . 41.36 |==================================================================== c . 41.49 |==================================================================== HeFFTe - Highly Efficient FFT for Exascale 2.3 Test: r2c - Backend: FFTW - Precision: double-long - X Y Z: 256 GFLOP/s > Higher Is Better a . 19.00 |=================================================================== b . 19.25 |==================================================================== HeFFTe - Highly Efficient FFT for Exascale 2.3 Test: c2c - Backend: FFTW - Precision: float-long - X Y Z: 256 GFLOP/s > Higher Is Better a . 20.22 |=================================================================== b . 20.55 |==================================================================== HeFFTe - Highly Efficient FFT for Exascale 2.3 Test: c2c - Backend: Stock - Precision: float-long - X Y Z: 256 GFLOP/s > Higher Is Better a . 20.56 |=================================================================== b . 20.94 |==================================================================== HeFFTe - Highly Efficient FFT for Exascale 2.3 Test: r2c - Backend: Stock - Precision: double-long - X Y Z: 256 GFLOP/s > Higher Is Better a . 20.66 |=================================================================== b . 21.02 |==================================================================== HeFFTe - Highly Efficient FFT for Exascale 2.3 Test: r2c - Backend: Stock - Precision: double - X Y Z: 256 GFLOP/s > Higher Is Better a . 20.67 |=================================================================== b . 20.99 |==================================================================== Monte Carlo Simulations of Ionised Nebulae 2.02.73.3 Input: Gas HII40 Seconds < Lower Is Better a . 9.905 |==================================================================== b . 9.875 |==================================================================== CP2K Molecular Dynamics 2023.1 Input: H2O-DFT-LS Seconds < Lower Is Better HeFFTe - Highly Efficient FFT for Exascale 2.3 Test: r2c - Backend: FFTW - Precision: float-long - X Y Z: 256 GFLOP/s > Higher Is Better a . 41.25 |==================================================================== b . 41.30 |==================================================================== HeFFTe - Highly Efficient FFT for Exascale 2.3 Test: c2c - Backend: FFTW - Precision: double - X Y Z: 128 GFLOP/s > Higher Is Better a . 13.68 |=================================================== b . 18.05 |==================================================================== c . 18.13 |==================================================================== Palabos 2.3 Grid Size: 1000 Mega Site Updates Per Second > Higher Is Better HeFFTe - Highly Efficient FFT for Exascale 2.3 Test: r2c - Backend: Stock - Precision: float-long - X Y Z: 256 GFLOP/s > Higher Is Better a . 45.42 |=================================================================== b . 45.78 |==================================================================== HeFFTe - Highly Efficient FFT for Exascale 2.3 Test: r2c - Backend: FFTW - Precision: double - X Y Z: 128 GFLOP/s > Higher Is Better a . 36.52 |============================================ b . 54.66 |================================================================== c . 56.25 |==================================================================== HeFFTe - Highly Efficient FFT for Exascale 2.3 Test: c2c - Backend: Stock - Precision: float - X Y Z: 128 GFLOP/s > Higher Is Better a . 46.57 |========================================= b . 78.12 |==================================================================== c . 69.61 |============================================================= HeFFTe - Highly Efficient FFT for Exascale 2.3 Test: r2c - Backend: Stock - Precision: float - X Y Z: 128 GFLOP/s > Higher Is Better a . 93.33 |=============================================== b . 132.48 |=================================================================== c . 129.84 |================================================================== HeFFTe - Highly Efficient FFT for Exascale 2.3 Test: c2c - Backend: Stock - Precision: float-long - X Y Z: 128 GFLOP/s > Higher Is Better a . 69.20 |================================================================== b . 71.48 |==================================================================== HeFFTe - Highly Efficient FFT for Exascale 2.3 Test: c2c - Backend: FFTW - Precision: float - X Y Z: 128 GFLOP/s > Higher Is Better a . 66.05 |================================================= b . 90.39 |==================================================================== c . 91.02 |==================================================================== HeFFTe - Highly Efficient FFT for Exascale 2.3 Test: r2c - Backend: FFTW - Precision: float - X Y Z: 128 GFLOP/s > Higher Is Better a . 111.47 |================================================= b . 153.22 |=================================================================== c . 151.06 |================================================================== HeFFTe - Highly Efficient FFT for Exascale 2.3 Test: c2c - Backend: Stock - Precision: double - X Y Z: 128 GFLOP/s > Higher Is Better a . 12.91 |================================================== b . 17.66 |==================================================================== HeFFTe - Highly Efficient FFT for Exascale 2.3 Test: r2c - Backend: FFTW - Precision: float-long - X Y Z: 128 GFLOP/s > Higher Is Better a . 144.16 |======================================================== b . 172.67 |=================================================================== HeFFTe - Highly Efficient FFT for Exascale 2.3 Test: c2c - Backend: Stock - Precision: double-long - X Y Z: 128 GFLOP/s > Higher Is Better a . 17.05 |================================================================= b . 17.95 |==================================================================== HeFFTe - Highly Efficient FFT for Exascale 2.3 Test: c2c - Backend: FFTW - Precision: double-long - X Y Z: 128 GFLOP/s > Higher Is Better a . 17.72 |=================================================================== b . 18.09 |==================================================================== HeFFTe - Highly Efficient FFT for Exascale 2.3 Test: r2c - Backend: Stock - Precision: double - X Y Z: 128 GFLOP/s > Higher Is Better a . 44.82 |========================================================= b . 53.91 |==================================================================== HeFFTe - Highly Efficient FFT for Exascale 2.3 Test: r2c - Backend: FFTW - Precision: double-long - X Y Z: 128 GFLOP/s > Higher Is Better a . 51.20 |================================================================ b . 54.47 |==================================================================== HeFFTe - Highly Efficient FFT for Exascale 2.3 Test: r2c - Backend: Stock - Precision: double-long - X Y Z: 128 GFLOP/s > Higher Is Better a . 53.39 |================================================================= b . 55.89 |==================================================================== HeFFTe - Highly Efficient FFT for Exascale 2.3 Test: c2c - Backend: FFTW - Precision: float-long - X Y Z: 128 GFLOP/s > Higher Is Better a . 85.16 |============================================================== b . 93.48 |==================================================================== HeFFTe - Highly Efficient FFT for Exascale 2.3 Test: r2c - Backend: Stock - Precision: float-long - X Y Z: 128 GFLOP/s > Higher Is Better a . 138.93 |=========================================================== b . 158.11 |===================================================================