{"id":15036092,"url":"https://github.com/projectphysx/fluidx3d","last_synced_at":"2025-05-13T23:09:24.368Z","repository":{"id":53210109,"uuid":"521191759","full_name":"ProjectPhysX/FluidX3D","owner":"ProjectPhysX","description":"The fastest and most memory efficient lattice Boltzmann CFD software, running on all GPUs and CPUs via OpenCL. 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Free for non-commercial use.\n\n\u003ca href=\"https://youtu.be/-MkRBeQkLk8\"\u003e\u003cimg src=\"https://img.youtube.com/vi/o3TPN142HxM/maxresdefault.jpg\" width=\"50%\"\u003e\u003c/img\u003e\u003c/a\u003e\u003ca href=\"https://youtu.be/oC6U1M0Fsug\"\u003e\u003cimg src=\"https://img.youtube.com/vi/oC6U1M0Fsug/maxresdefault.jpg\" width=\"50%\"\u003e\u003c/img\u003e\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://youtu.be/XOfXHgP4jnQ\"\u003e\u003cimg src=\"https://img.youtube.com/vi/XOfXHgP4jnQ/maxresdefault.jpg\" width=\"50%\"\u003e\u003c/img\u003e\u003c/a\u003e\u003ca href=\"https://youtu.be/K5eKxzklXDA\"\u003e\u003cimg src=\"https://img.youtube.com/vi/K5eKxzklXDA/maxresdefault.jpg\" width=\"50%\"\u003e\u003c/img\u003e\u003c/a\u003e\n(click on images to show videos on YouTube)\n\n\u003cdetails\u003e\u003csummary\u003eUpdate History\u003c/summary\u003e\n\n- [v1.0](https://github.com/ProjectPhysX/FluidX3D/releases/tag/v1.0) (04.08.2022) [changes](https://github.com/ProjectPhysX/FluidX3D/commit/768073501af725e392a4b85885009e2fa6400e48) (public release)\n - public release\n- [v1.1](https://github.com/ProjectPhysX/FluidX3D/releases/tag/v1.1) (29.09.2022) [changes](https://github.com/ProjectPhysX/FluidX3D/compare/v1.0...v1.1) (GPU voxelization)\n - added solid voxelization on GPU (slow algorithm)\n - added tool to print current camera position (key \u003ckbd\u003eG\u003c/kbd\u003e)\n - minor bug fix (workaround for Intel iGPU driver bug with triangle rendering)\n- [v1.2](https://github.com/ProjectPhysX/FluidX3D/releases/tag/v1.2) (24.10.2022) [changes](https://github.com/ProjectPhysX/FluidX3D/compare/v1.1...v1.2) (force/torque compuatation)\n - added functions to compute force/torque on objects\n - added function to translate Mesh\n - added Stokes drag validation setup\n- [v1.3](https://github.com/ProjectPhysX/FluidX3D/releases/tag/v1.3) (10.11.2022) [changes](https://github.com/ProjectPhysX/FluidX3D/compare/v1.2...v1.3) (minor bug fixes)\n - added unit conversion functions for torque\n - `FORCE_FIELD` and `VOLUME_FORCE` can now be used independently\n - minor bug fix (workaround for AMD legacy driver bug with binary number literals)\n- [v1.4](https://github.com/ProjectPhysX/FluidX3D/releases/tag/v1.4) (14.12.2022) [changes](https://github.com/ProjectPhysX/FluidX3D/compare/v1.3...v1.4) (Linux graphics)\n - complete rewrite of C++ graphics library to minimize API dependencies\n - added interactive graphics mode on Linux with X11\n - fixed streamline visualization bug in 2D\n- [v2.0](https://github.com/ProjectPhysX/FluidX3D/releases/tag/v2.0) (09.01.2023) [changes](https://github.com/ProjectPhysX/FluidX3D/compare/v1.4...v2.0) (multi-GPU upgrade)\n - added (cross-vendor) multi-GPU support on a single node (PC/laptop/server)\n- [v2.1](https://github.com/ProjectPhysX/FluidX3D/releases/tag/v2.1) (15.01.2023) [changes](https://github.com/ProjectPhysX/FluidX3D/compare/v2.0...v2.1) (fast voxelization)\n - made solid voxelization on GPU lightning fast (new algorithm, from minutes to milliseconds)\n- [v2.2](https://github.com/ProjectPhysX/FluidX3D/releases/tag/v2.0) (20.01.2023) [changes](https://github.com/ProjectPhysX/FluidX3D/compare/v2.1...v2.2) (velocity voxelization)\n - added option to voxelize moving/rotating geometry on GPU, with automatic velocity initialization for each grid point based on center of rotation, linear velocity and rotational velocity\n - cells that are converted from solid-\u003efluid during re-voxelization now have their DDFs properly initialized\n - added option to not auto-scale mesh during `read_stl(...)`, with negative `size` parameter\n - added kernel for solid boundary rendering with marching-cubes\n- [v2.3](https://github.com/ProjectPhysX/FluidX3D/releases/tag/v2.3) (30.01.2023) [changes](https://github.com/ProjectPhysX/FluidX3D/compare/v2.2...v2.3) (particles)\n - added particles with immersed-boundary method (either passive or 2-way-coupled, only supported with single-GPU)\n - minor optimization to GPU voxelization algorithm (workgroup threads outside mesh bounding-box return after ray-mesh intersections have been found)\n - displayed GPU memory allocation size is now fully accurate\n - fixed bug in `write_line()` function in `src/utilities.hpp`\n - removed `.exe` file extension for Linux/macOS\n- [v2.4](https://github.com/ProjectPhysX/FluidX3D/releases/tag/v2.4) (11.03.2023) [changes](https://github.com/ProjectPhysX/FluidX3D/compare/v2.3...v2.4) (UI improvements)\n - added a help menu with key \u003ckbd\u003eH\u003c/kbd\u003e that shows keyboard/mouse controls, visualization settings and simulation stats\n - improvements to keyboard/mouse control (\u003ckbd\u003e+\u003c/kbd\u003e/\u003ckbd\u003e-\u003c/kbd\u003e for zoom, \u003ckbd\u003emouseclick\u003c/kbd\u003e frees/locks cursor)\n - added suggestion of largest possible grid resolution if resolution is set larger than memory allows\n - minor optimizations in multi-GPU communication (insignificant performance difference)\n - fixed bug in temperature equilibrium function for temperature extension\n - fixed erroneous double literal for Intel iGPUs in skybox color functions\n - fixed bug in make.sh where multi-GPU device IDs would not get forwarded to the executable\n - minor bug fixes in graphics engine (free cursor not centered during rotation, labels in VR mode)\n - fixed bug in `LBM::voxelize_stl()` size parameter standard initialization\n- [v2.5](https://github.com/ProjectPhysX/FluidX3D/releases/tag/v2.5) (11.04.2023) [changes](https://github.com/ProjectPhysX/FluidX3D/compare/v2.4...v2.5) (raytracing overhaul)\n - implemented light absorption in fluid for raytracing graphics (no performance impact)\n - improved raytracing framerate when camera is inside fluid\n - fixed skybox pole flickering artifacts\n - fixed bug where moving objects during re-voxelization would leave an erroneous trail of solid grid cells behind\n- [v2.6](https://github.com/ProjectPhysX/FluidX3D/releases/tag/v2.6) (16.04.2023) [changes](https://github.com/ProjectPhysX/FluidX3D/compare/v2.5...v2.6) (Intel Arc patch)\n - patched OpenCL issues of Intel Arc GPUs: now VRAM allocations \u003e4GB are possible and correct VRAM capacity is reported\n- [v2.7](https://github.com/ProjectPhysX/FluidX3D/releases/tag/v2.7) (29.05.2023) [changes](https://github.com/ProjectPhysX/FluidX3D/compare/v2.6...v2.7) (visualization upgrade)\n - added slice visualization (key \u003ckbd\u003e2\u003c/kbd\u003e / key \u003ckbd\u003e3\u003c/kbd\u003e modes, then switch through slice modes with key \u003ckbd\u003eT\u003c/kbd\u003e, move slice with keys \u003ckbd\u003eQ\u003c/kbd\u003e/\u003ckbd\u003eE\u003c/kbd\u003e)\n - made flag wireframe / solid surface visualization kernels toggleable with key \u003ckbd\u003e1\u003c/kbd\u003e\n - added surface pressure visualization (key \u003ckbd\u003e1\u003c/kbd\u003e when `FORCE_FIELD` is enabled and `lbm.calculate_force_on_boundaries();` is called)\n - added binary `.vtk` export function for meshes with `lbm.write_mesh_to_vtk(Mesh* mesh);`\n - added `time_step_multiplicator` for `integrate_particles()` function in PARTICLES extension\n - made correction of wrong memory reporting on Intel Arc more robust\n - fixed bug in `write_file()` template functions\n - reverted back to separate `cl::Context` for each OpenCL device, as the shared Context otherwise would allocate extra VRAM on all other unused Nvidia GPUs\n - removed Debug and x86 configurations from Visual Studio solution file (one less complication for compiling)\n - fixed bug that particles could get too close to walls and get stuck, or leave the fluid phase (added boundary force)\n- [v2.8](https://github.com/ProjectPhysX/FluidX3D/releases/tag/v2.8) (24.06.2023) [changes](https://github.com/ProjectPhysX/FluidX3D/compare/v2.7...v2.8) (documentation + polish)\n - finally added more [documentation](DOCUMENTATION.md)\n - cleaned up all sample setups in `setup.cpp` for more beginner-friendliness, and added required extensions in `defines.hpp` as comments to all setups\n - improved loading of composite `.stl` geometries, by adding an option to omit automatic mesh repositioning, added more functionality to `Mesh` struct in `utilities.hpp`\n - added `uint3 resolution(float3 box_aspect_ratio, uint memory)` function to compute simulation box resolution based on box aspect ratio and VRAM occupation in MB\n - added `bool lbm.graphics.next_frame(...)` function to export images for a specified video length in the `main_setup` compute loop\n - added `VIS_...` macros to ease setting visualization modes in headless graphics mode in `lbm.graphics.visualization_modes`\n - simulation box dimensions are now automatically made equally divisible by domains for multi-GPU simulations\n - fixed Info/Warning/Error message formatting for loading files and made Info/Warning/Error message labels colored\n - added Ahmed body setup as an example on how body forces and drag coefficient are computed\n - added Cessna 172 and Bell 222 setups to showcase loading composite .stl geometries and revoxelization of moving parts\n - added optional semi-transparent rendering mode (`#define GRAPHICS_TRANSPARENCY 0.7f` in `defines.hpp`)\n - fixed flickering of streamline visualization in interactive graphics\n - improved smooth positioning of streamlines in slice mode\n - fixed bug where `mass` and `massex` in `SURFACE` extension were also allocated in CPU RAM (not required)\n - fixed bug in Q-criterion rendering of halo data in multi-GPU mode, reduced gap width between domains\n - removed shared memory optimization from mesh voxelization kernel, as it crashes on Nvidia GPUs with new GPU drivers and is incompatible with old OpenCL 1.0 GPUs\n - fixed raytracing attenuation color when no surface is at the simulation box walls with periodic boundaries\n- [v2.9](https://github.com/ProjectPhysX/FluidX3D/releases/tag/v2.9) (31.07.2023) [changes](https://github.com/ProjectPhysX/FluidX3D/compare/v2.8...v2.9) (multithreading)\n - added cross-platform `parallel_for` implementation in `utilities.hpp` using `std::threads`\n - significantly (\u003e4x) faster simulation startup with multithreaded geometry initialization and sanity checks\n - faster `calculate_force_on_object()` and `calculate_torque_on_object()` functions with multithreading\n - added total runtime and LBM runtime to `lbm.write_status()`\n - fixed bug in voxelization ray direction for re-voxelizing rotating objects\n - fixed bug in `Mesh::get_bounding_box_size()`\n - fixed bug in `print_message()` function in `utilities.hpp`\n- [v2.10](https://github.com/ProjectPhysX/FluidX3D/releases/tag/v2.10) (05.11.2023) [changes](https://github.com/ProjectPhysX/FluidX3D/compare/v2.9...v2.10) (frustrum culling)\n - improved rasterization performance via frustrum culling when only part of the simulation box is visible\n - improved switching between centered/free camera mode\n - refactored OpenCL rendering library\n - unit conversion factors are now automatically printed in console when `units.set_m_kg_s(...)` is used\n - faster startup time for FluidX3D benchmark\n - miner bug fix in `voxelize_mesh(...)` kernel\n - fixed bug in `shading(...)`\n - replaced slow (in multithreading) `std::rand()` function with standard C99 LCG\n - more robust correction of wrong VRAM capacity reporting on Intel Arc GPUs\n - fixed some minor compiler warnings\n- [v2.11](https://github.com/ProjectPhysX/FluidX3D/releases/tag/v2.11) (07.12.2023) [changes](https://github.com/ProjectPhysX/FluidX3D/compare/v2.10...v2.11) (improved Linux graphics)\n - interactive graphics on Linux are now in fullscreen mode too, fully matching Windows\n - made CPU/GPU buffer initialization significantly faster with `std::fill` and `enqueueFillBuffer` (overall ~8% faster simulation startup)\n - added operating system info to OpenCL device driver version printout\n - fixed flickering with frustrum culling at very small field of view\n - fixed bug where rendered/exported frame was not updated when `visualization_modes` changed\n- [v2.12](https://github.com/ProjectPhysX/FluidX3D/releases/tag/v2.12) (18.01.2024) [changes](https://github.com/ProjectPhysX/FluidX3D/compare/v2.11...v2.12) (faster startup)\n - ~3x faster source code compiling on Linux using multiple CPU cores if [`make`](https://www.gnu.org/software/make/) is installed\n - significantly faster simulation initialization (~40% single-GPU, ~15% multi-GPU)\n - minor bug fix in `Memory_Container::reset()` function\n- [v2.13](https://github.com/ProjectPhysX/FluidX3D/releases/tag/v2.13) (11.02.2024) [changes](https://github.com/ProjectPhysX/FluidX3D/compare/v2.12...v2.13) (improved .vtk export)\n - data in exported `.vtk` files is now automatically converted to SI units\n - ~2x faster `.vtk` export with multithreading\n - added unit conversion functions for `TEMPERATURE` extension\n - fixed graphical artifacts with axis-aligned camera in raytracing\n - fixed `get_exe_path()` for macOS\n - fixed X11 multi-monitor issues on Linux\n - workaround for Nvidia driver bug: `enqueueFillBuffer` is broken for large buffers on Nvidia GPUs\n - fixed slow numeric drift issues caused by `-cl-fast-relaxed-math`\n - fixed wrong Maximum Allocation Size reporting in `LBM::write_status()`\n - fixed missing scaling of coordinates to SI units in `LBM::write_mesh_to_vtk()`\n- [v2.14](https://github.com/ProjectPhysX/FluidX3D/releases/tag/v2.14) (03.03.2024) [changes](https://github.com/ProjectPhysX/FluidX3D/compare/v2.13...v2.14) (visualization upgrade)\n - coloring can now be switched between velocity/density/temperature with key \u003ckbd\u003eZ\u003c/kbd\u003e\n - uniform improved color palettes for velocity/density/temperature visualization\n - color scale with automatic unit conversion can now be shown with key \u003ckbd\u003eH\u003c/kbd\u003e\n - slice mode for field visualization now draws fully filled-in slices instead of only lines for velocity vectors\n - shading in `VIS_FLAG_SURFACE` and `VIS_PHI_RASTERIZE` modes is smoother now\n - `make.sh` now automatically detects operating system and X11 support on Linux and only runs FluidX3D if last compilation was successful\n - fixed compiler warnings on Android\n - fixed `make.sh` failing on some systems due to nonstandard interpreter path\n - fixed that `make` would not compile with multiple cores on some systems\n- [v2.15](https://github.com/ProjectPhysX/FluidX3D/releases/tag/v2.15) (09.04.2024) [changes](https://github.com/ProjectPhysX/FluidX3D/compare/v2.14...v2.15) (framerate boost)\n - eliminated one frame memory copy and one clear frame operation in rendering chain, for 20-70% higher framerate on both Windows and Linux\n - enabled `g++` compiler optimizations for faster startup and higher rendering framerate\n - fixed bug in multithreaded sanity checks\n - fixed wrong unit conversion for thermal expansion coefficient\n - fixed density to pressure conversion in LBM units\n - fixed bug that raytracing kernel could lock up simulation\n - fixed minor visual artifacts with raytracing\n - fixed that console sometimes was not cleared before `INTERACTIVE_GRAPHICS_ASCII` rendering starts\n- [v2.16](https://github.com/ProjectPhysX/FluidX3D/releases/tag/v2.16) (02.05.2024) [changes](https://github.com/ProjectPhysX/FluidX3D/compare/v2.15...v2.16) (bug fixes)\n - simplified 10% faster marching-cubes implementation with 1D interpolation on edges instead of 3D interpolation, allowing to get rid of edge table\n - added faster, simplified marching-cubes variant for solid surface rendering where edges are always halfway between grid cells\n - refactoring in OpenCL rendering kernels\n - fixed that voxelization failed in Intel OpenCL CPU Runtime due to array out-of-bounds access\n - fixed that voxelization did not always produce binary identical results in multi-GPU compared to single-GPU\n - fixed that velocity voxelization failed for free surface simulations\n - fixed terrible performance on ARM GPUs by macro-replacing fused-multiply-add (`fma`) with `a*b+c`\n - fixed that \u003ckbd\u003eY\u003c/kbd\u003e/\u003ckbd\u003eZ\u003c/kbd\u003e keys were incorrect for `QWERTY` keyboard layout in Linux\n - fixed that free camera movement speed in help overlay was not updated in stationary image when scrolling\n - fixed that cursor would sometimes flicker when scrolling on trackpads with Linux-X11 interactive graphics\n - fixed flickering of interactive rendering with multi-GPU when camera is not moved\n - fixed missing `XInitThreads()` call that could crash Linux interactive graphics on some systems\n - fixed z-fighting between `graphics_rasterize_phi()` and `graphics_flags_mc()` kernels\n- [v2.17](https://github.com/ProjectPhysX/FluidX3D/releases/tag/v2.17) (05.06.2024) [changes](https://github.com/ProjectPhysX/FluidX3D/compare/v2.16...v2.17) (unlimited domain resolution)\n - domains are no longer limited to 4.29 billion (2³², 1624³) grid cells or 225 GB memory; if more are used, the OpenCL code will automatically compile with 64-bit indexing\n - new, faster raytracing-based field visualization for single-GPU simulations\n - added [GPU Driver and OpenCL Runtime installation instructions](DOCUMENTATION.md#0-install-gpu-drivers-and-opencl-runtime) to documentation\n - refactored `INTERACTIVE_GRAPHICS_ASCII`\n - fixed memory leak in destructors of `floatN`, `floatNxN`, `doubleN`, `doubleNxN` (all unused)\n - made camera movement/rotation/zoom behavior independent of framerate\n - fixed that `smart_device_selection()` would print a wrong warning if device reports 0 MHz clock speed\n- [v2.18](https://github.com/ProjectPhysX/FluidX3D/releases/tag/v2.18) (21.07.2024) [changes](https://github.com/ProjectPhysX/FluidX3D/compare/v2.17...v2.18) (more bug fixes)\n - added support for high refresh rate monitors on Linux\n - more compact OpenCL Runtime installation scripts in Documentation\n - driver/runtime installation instructions will now be printed to console if no OpenCL devices are available\n - added domain information to `LBM::write_status()`\n - added `LBM::index` function for `uint3` input parameter\n - fixed that very large simulations sometimes wouldn't render properly by increasing maximum render distance from 10k to 2.1M\n - fixed mouse input stuttering at high screen refresh rate on Linux\n - fixed graphical artifacts in free surface raytracing on Intel CPU Runtime for OpenCL\n - fixed runtime estimation printed in console for setups with multiple `lbm.run(...)` calls\n - fixed density oscillations in sample setups (too large `lbm_u`)\n - fixed minor graphical artifacts in `raytrace_phi()`\n - fixed minor graphical artifacts in `ray_grid_traverse_sum()`\n - fixed wrong printed time step count on raindrop sample setup\n- [v2.19](https://github.com/ProjectPhysX/FluidX3D/releases/tag/v2.19) (07.09.2024) [changes](https://github.com/ProjectPhysX/FluidX3D/compare/v2.18...v2.19) (camera splines)\n - the camera can now fly along a smooth path through a list of provided keyframe camera placements, [using Catmull-Rom splines](https://github.com/ProjectPhysX/FluidX3D/blob/master/DOCUMENTATION.md#video-rendering)\n - more accurate remaining runtime estimation that includes time spent on rendering\n - enabled FP16S memory compression by default\n - printed camera placement using key \u003ckbd\u003eG\u003c/kbd\u003e is now formatted for easier copy/paste\n - added benchmark chart in Readme using mermaid gantt chart\n - placed memory allocation info during simulation startup at better location\n - fixed threading conflict between `INTERACTIVE_GRAPHICS` and `lbm.graphics.write_frame();`\n - fixed maximum buffer allocation size limit for AMD GPUs and in Intel CPU Runtime for OpenCL\n - fixed wrong `Re\u003cRe_max` info printout for 2D simulations\n - minor fix in `bandwidth_bytes_per_cell_device()`\n- [v3.0](https://github.com/ProjectPhysX/FluidX3D/releases/tag/v3.0) (16.11.2024) [changes](https://github.com/ProjectPhysX/FluidX3D/compare/v2.19...v3.0) (larger CPU/iGPU simulations)\n - reduced memory footprint on CPUs and iGPU from 72 to 55 Bytes/cell (fused OpenCL host+device buffers for `rho`/`u`/`flags`), allowing 31% higher resolution in the same RAM capacity\n - faster hardware-supported and faster fallback emulation atomic floating-point addition for `PARTICLES` extension\n - hardened `calculate_f_eq()` against bad user input for `D2Q9`\n - fixed velocity voxelization for overlapping geometry with different velocity\n - fixed Remaining Time printout during paused simulation\n - fixed CPU/GPU memory printout for CPU/iGPU simulations\n- [v3.1](https://github.com/ProjectPhysX/FluidX3D/releases/tag/v3.1) (08.02.2025) [changes](https://github.com/ProjectPhysX/FluidX3D/compare/v3.0...v3.1) (more bug fixes)\n - faster `enqueueReadBuffer()` on modern CPUs with 64-Byte-aligned `host_buffer`\n - hardened ray intersection functions against planar ray edge case\n - updated OpenCL headers\n - better OpenCL device specs detection using vendor ID and Nvidia compute capability\n - better VRAM capacity reporting correction for Intel dGPUs\n - improved styling of performance mermaid gantt chart in Readme\n - added multi-GPU performance mermaid gantt chart in Readme\n - updated driver install guides\n - fixed voxelization being broken on some GPUs\n - added workaround for compiler bug in Intel CPU Runtime for OpenCL that causes Q-criterion isosurface rendering corruption\n - fixed TFlops estimate for Intel Battlemage GPUs\n - fixed wrong device name reporting for AMD GPUs\n- [v3.2](https://github.com/ProjectPhysX/FluidX3D/releases/tag/v3.2) (09.03.2025) [changes](https://github.com/ProjectPhysX/FluidX3D/compare/v3.1...v3.2) (fast force/torque summation)\n - implemented GPU-accelerated force/torque summation (~20x faster than CPU-multithreaded implementation before)\n - simplified calculating object force/torque in setups\n - improved coloring in `VIS_FIELD`/`ray_grid_traverse_sum()`\n - updated OpenCL-Wrapper now compiles OpenCL C code with `-cl-std=CL3.0` if available\n - fixed compiling on macOS with new OpenCL headers\n\n\u003c/details\u003e\n\n\n\n## How to get started?\n\nRead the [FluidX3D Documentation](DOCUMENTATION.md)!\n\n\n\n## Compute Features - Getting the Memory Problem under Control\n\n- \u003cdetails\u003e\u003csummary\u003e\u003ca name=\"cfd-model\"\u003e\u003c/a\u003eCFD model: lattice Boltzmann method (LBM)\u003c/summary\u003e\n\n - streaming (part 2/2)\u003cp align=\"center\"\u003e\u003ci\u003ef\u003c/i\u003e\u003csub\u003e0\u003c/sub\u003e\u003csup\u003etemp\u003c/sup\u003e(\u003ci\u003ex\u003c/i\u003e,\u003ci\u003et\u003c/i\u003e) = \u003ci\u003ef\u003c/i\u003e\u003csub\u003e0\u003c/sub\u003e(\u003ci\u003ex\u003c/i\u003e, \u003ci\u003et\u003c/i\u003e)\u003cbr\u003e\u003ci\u003ef\u003csub\u003ei\u003c/sub\u003e\u003c/i\u003e\u003csup\u003etemp\u003c/sup\u003e(\u003ci\u003ex\u003c/i\u003e,\u003ci\u003et\u003c/i\u003e) = \u003ci\u003ef\u003c/i\u003e\u003csub\u003e(\u003ci\u003et\u003c/i\u003e%2 ? \u003ci\u003ei\u003c/i\u003e : (\u003ci\u003ei\u003c/i\u003e%2 ? \u003ci\u003ei\u003c/i\u003e+1 : \u003ci\u003ei\u003c/i\u003e-1))\u003c/sub\u003e(\u003ci\u003ei\u003c/i\u003e%2 ? \u003ci\u003ex\u003c/i\u003e : \u003ci\u003ex\u003c/i\u003e-\u003ci\u003ee\u003csub\u003ei\u003c/sub\u003e\u003c/i\u003e, \u003ci\u003et\u003c/i\u003e) \u0026nbsp; for \u0026nbsp; \u003ci\u003ei\u003c/i\u003e \u0026isin; [1, \u003ci\u003eq\u003c/i\u003e-1]\u003c/p\u003e\n - collision\u003cp align=\"center\"\u003e\u003ci\u003e\u0026rho;\u003c/i\u003e(\u003ci\u003ex\u003c/i\u003e,\u003ci\u003et\u003c/i\u003e) = (\u0026Sigma;\u003csub\u003e\u003ci\u003ei\u003c/i\u003e\u003c/sub\u003e \u003ci\u003ef\u003csub\u003ei\u003c/sub\u003e\u003c/i\u003e\u003csup\u003etemp\u003c/sup\u003e(\u003ci\u003ex\u003c/i\u003e,\u003ci\u003et\u003c/i\u003e)) + 1\u003cbr\u003e\u003cbr\u003e\u003ci\u003eu\u003c/i\u003e(\u003ci\u003ex\u003c/i\u003e,\u003ci\u003et\u003c/i\u003e) = \u003csup\u003e1\u003c/sup\u003e\u0026#8725;\u003csub\u003e\u003ci\u003e\u0026rho;\u003c/i\u003e(\u003ci\u003ex\u003c/i\u003e,\u003ci\u003et\u003c/i\u003e)\u003c/sub\u003e \u0026Sigma;\u003csub\u003e\u003ci\u003ei\u003c/i\u003e\u003c/sub\u003e \u003ci\u003ec\u003csub\u003ei\u003c/sub\u003e\u003c/i\u003e \u003ci\u003ef\u003csub\u003ei\u003c/sub\u003e\u003c/i\u003e\u003csup\u003etemp\u003c/sup\u003e(\u003ci\u003ex\u003c/i\u003e,\u003ci\u003et\u003c/i\u003e)\u003cbr\u003e\u003cbr\u003e\u003ci\u003ef\u003csub\u003ei\u003c/sub\u003e\u003c/i\u003e\u003csup\u003eeq-shifted\u003c/sup\u003e(\u003ci\u003ex\u003c/i\u003e,\u003ci\u003et\u003c/i\u003e) = \u003ci\u003ew\u003csub\u003ei\u003c/sub\u003e\u003c/i\u003e \u003ci\u003e\u0026rho;\u003c/i\u003e · (\u003csup\u003e(\u003ci\u003eu\u003c/i\u003e\u003csub\u003e°\u003c/sub\u003e\u003ci\u003ec\u003csub\u003ei\u003c/sub\u003e\u003c/i\u003e)\u003csup\u003e2\u003c/sup\u003e\u003c/sup\u003e\u0026#8725;\u003csub\u003e(2\u003ci\u003ec\u003c/i\u003e\u003csup\u003e4\u003c/sup\u003e)\u003c/sub\u003e - \u003csup\u003e(\u003ci\u003eu\u003c/i\u003e\u003csub\u003e°\u003c/sub\u003e\u003ci\u003eu\u003c/i\u003e)\u003c/sup\u003e\u0026#8725;\u003csub\u003e(2c\u003csup\u003e2\u003c/sup\u003e)\u003c/sub\u003e + \u003csup\u003e(\u003ci\u003eu\u003c/i\u003e\u003csub\u003e°\u003c/sub\u003e\u003ci\u003ec\u003csub\u003ei\u003c/sub\u003e\u003c/i\u003e)\u003c/sup\u003e\u0026#8725;\u003csub\u003e\u003ci\u003ec\u003c/i\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/sub\u003e) + \u003ci\u003ew\u003csub\u003ei\u003c/sub\u003e\u003c/i\u003e (\u003ci\u003e\u0026rho;\u003c/i\u003e-1)\u003cbr\u003e\u003cbr\u003e\u003ci\u003ef\u003csub\u003ei\u003c/sub\u003e\u003c/i\u003e\u003csup\u003etemp\u003c/sup\u003e(\u003ci\u003ex\u003c/i\u003e, \u003ci\u003et\u003c/i\u003e+\u0026Delta;\u003ci\u003et\u003c/i\u003e) = \u003ci\u003ef\u003csub\u003ei\u003c/sub\u003e\u003c/i\u003e\u003csup\u003etemp\u003c/sup\u003e(\u003ci\u003ex\u003c/i\u003e,\u003ci\u003et\u003c/i\u003e) + \u003ci\u003e\u0026Omega;\u003csub\u003ei\u003c/sub\u003e\u003c/i\u003e(\u003ci\u003ef\u003csub\u003ei\u003c/sub\u003e\u003c/i\u003e\u003csup\u003etemp\u003c/sup\u003e(\u003ci\u003ex\u003c/i\u003e,\u003ci\u003et\u003c/i\u003e), \u003ci\u003ef\u003csub\u003ei\u003c/sub\u003e\u003c/i\u003e\u003csup\u003eeq-shifted\u003c/sup\u003e(\u003ci\u003ex\u003c/i\u003e,\u003ci\u003et\u003c/i\u003e), \u003ci\u003e\u0026tau;\u003c/i\u003e)\u003c/p\u003e\n - streaming (part 1/2)\u003cp align=\"center\"\u003e\u003ci\u003ef\u003c/i\u003e\u003csub\u003e0\u003c/sub\u003e(\u003ci\u003ex\u003c/i\u003e, \u003ci\u003et\u003c/i\u003e+\u0026Delta;\u003ci\u003et\u003c/i\u003e) = \u003ci\u003ef\u003c/i\u003e\u003csub\u003e0\u003c/sub\u003e\u003csup\u003etemp\u003c/sup\u003e(\u003ci\u003ex\u003c/i\u003e, \u003ci\u003et\u003c/i\u003e+\u0026Delta;\u003ci\u003et\u003c/i\u003e)\u003cbr\u003e\u003ci\u003ef\u003c/i\u003e\u003csub\u003e(\u003ci\u003et\u003c/i\u003e%2 ? (\u003ci\u003ei\u003c/i\u003e%2 ? \u003ci\u003ei\u003c/i\u003e+1 : \u003ci\u003ei\u003c/i\u003e-1) : \u003ci\u003ei\u003c/i\u003e)\u003c/sub\u003e(\u003ci\u003ei\u003c/i\u003e%2 ? \u003ci\u003ex\u003c/i\u003e+\u003ci\u003ee\u003csub\u003ei\u003c/sub\u003e\u003c/i\u003e : \u003ci\u003ex\u003c/i\u003e, \u003ci\u003et\u003c/i\u003e+\u0026Delta;\u003ci\u003et\u003c/i\u003e) = \u003ci\u003ef\u003csub\u003ei\u003c/sub\u003e\u003c/i\u003e\u003csup\u003etemp\u003c/sup\u003e(\u003ci\u003ex\u003c/i\u003e, \u003ci\u003et\u003c/i\u003e+\u0026Delta;\u003ci\u003et\u003c/i\u003e) \u0026nbsp; for \u0026nbsp; \u003ci\u003ei\u003c/i\u003e \u0026isin; [1, \u003ci\u003eq\u003c/i\u003e-1]\u003c/p\u003e\n - \u003cdetails\u003e\u003csummary\u003evariables and \u003ca href=\"https://doi.org/10.15495/EPub_UBT_00005400\"\u003enotation\u003c/a\u003e\u003c/summary\u003e\n\n | variable | SI units | defining equation | description |\n | :------------------: | :---------------------------------: | :-------------------------------------------------: | :------------------------------------------------------------------------------ |\n | | | | |\n | \u003ci\u003ex\u003c/i\u003e | m | \u003ci\u003ex\u003c/i\u003e = (x,y,z)\u003csup\u003eT\u003c/sup\u003e | 3D position in Cartesian coordinates |\n | \u003ci\u003et\u003c/i\u003e | s | - | time |\n | \u003ci\u003e\u0026rho;\u003c/i\u003e | \u003csup\u003ekg\u003c/sup\u003e\u0026#8725;\u003csub\u003em³\u003c/sub\u003e | \u003ci\u003e\u0026rho;\u003c/i\u003e = (\u0026Sigma;\u003csub\u003e\u003ci\u003ei\u003c/i\u003e\u003c/sub\u003e \u003ci\u003ef\u003csub\u003ei\u003c/sub\u003e\u003c/i\u003e)+1 | mass density of fluid |\n | \u003ci\u003ep\u003c/i\u003e | \u003csup\u003ekg\u003c/sup\u003e\u0026#8725;\u003csub\u003em\u0026nbsp;s²\u003c/sub\u003e | \u003ci\u003ep\u003c/i\u003e = \u003ci\u003ec\u003c/i\u003e² \u003ci\u003e\u0026rho;\u003c/i\u003e | pressure of fluid |\n | \u003ci\u003eu\u003c/i\u003e | \u003csup\u003em\u003c/sup\u003e\u0026#8725;\u003csub\u003es\u003c/sub\u003e | \u003ci\u003eu\u003c/i\u003e = \u003csup\u003e1\u003c/sup\u003e\u0026#8725;\u003csub\u003e\u003ci\u003e\u0026rho;\u003c/i\u003e\u003c/sub\u003e \u0026Sigma;\u003csub\u003e\u003ci\u003ei\u003c/i\u003e\u003c/sub\u003e \u003ci\u003ec\u003csub\u003ei\u003c/sub\u003e\u003c/i\u003e \u003ci\u003ef\u003csub\u003ei\u003c/sub\u003e\u003c/i\u003e | velocity of fluid |\n | \u003ci\u003e\u0026nu;\u003c/i\u003e | \u003csup\u003em²\u003c/sup\u003e\u0026#8725;\u003csub\u003es\u003c/sub\u003e | \u003ci\u003e\u0026nu;\u003c/i\u003e = \u003csup\u003e\u003ci\u003e\u0026mu;\u003c/i\u003e\u003c/sup\u003e\u0026#8725;\u003csub\u003e\u003ci\u003e\u0026rho;\u003c/i\u003e\u003c/sub\u003e | kinematic shear viscosity of fluid |\n | \u003ci\u003e\u0026mu;\u003c/i\u003e | \u003csup\u003ekg\u003c/sup\u003e\u0026#8725;\u003csub\u003em\u0026nbsp;s\u003c/sub\u003e | \u003ci\u003e\u0026mu;\u003c/i\u003e = \u003ci\u003e\u0026rho;\u003c/i\u003e \u003ci\u003e\u0026nu;\u003c/i\u003e | dynamic viscosity of fluid |\n | | | | |\n | \u003ci\u003ef\u003csub\u003ei\u003c/sub\u003e\u003c/i\u003e | \u003csup\u003ekg\u003c/sup\u003e\u0026#8725;\u003csub\u003em³\u003c/sub\u003e | - | shifted density distribution functions (DDFs) |\n | \u0026Delta;\u003ci\u003ex\u003c/i\u003e | m | \u0026Delta;\u003ci\u003ex\u003c/i\u003e = 1 | lattice constant (in LBM units) |\n | \u0026Delta;\u003ci\u003et\u003c/i\u003e | s | \u0026Delta;\u003ci\u003et\u003c/i\u003e = 1 | simulation time step (in LBM units) |\n | \u003ci\u003ec\u003c/i\u003e | \u003csup\u003em\u003c/sup\u003e\u0026#8725;\u003csub\u003es\u003c/sub\u003e | \u003ci\u003ec\u003c/i\u003e = \u003csup\u003e1\u003c/sup\u003e\u0026#8725;\u003csub\u003e\u0026radic;3\u003c/sub\u003e \u003csup\u003e\u0026Delta;\u003ci\u003ex\u003c/i\u003e\u003c/sup\u003e\u0026#8725;\u003csub\u003e\u0026Delta;\u003ci\u003et\u003c/i\u003e\u003c/sub\u003e | lattice speed of sound (in LBM units) |\n | \u003ci\u003ei\u003c/i\u003e | 1 | 0 \u0026le; \u003ci\u003ei\u003c/i\u003e \u003c \u003ci\u003eq\u003c/i\u003e | LBM streaming direction index |\n | \u003ci\u003eq\u003c/i\u003e | 1 | \u003ci\u003eq\u003c/i\u003e \u0026isin; {\u0026nbsp;9,15,19,27\u0026nbsp;} | number of LBM streaming directions |\n | \u003ci\u003ee\u003csub\u003ei\u003c/sub\u003e\u003c/i\u003e | m | D2Q9 / D3Q15/19/27 | LBM streaming directions |\n | \u003ci\u003ec\u003csub\u003ei\u003c/sub\u003e\u003c/i\u003e | \u003csup\u003em\u003c/sup\u003e\u0026#8725;\u003csub\u003es\u003c/sub\u003e | \u003ci\u003ec\u003csub\u003ei\u003c/sub\u003e\u003c/i\u003e = \u003csup\u003e\u003ci\u003ee\u003csub\u003ei\u003c/sub\u003e\u003c/i\u003e\u003c/sup\u003e\u0026#8725;\u003csub\u003e\u0026Delta;\u003ci\u003et\u003c/i\u003e\u003c/sub\u003e | LBM streaming velocities |\n | \u003ci\u003ew\u003csub\u003ei\u003c/sub\u003e\u003c/i\u003e | 1 | \u0026Sigma;\u003csub\u003e\u003ci\u003ei\u003c/i\u003e\u003c/sub\u003e \u003ci\u003ew\u003csub\u003ei\u003c/sub\u003e\u003c/i\u003e = 1 | LBM velocity set weights |\n | \u003ci\u003e\u0026Omega;\u003csub\u003ei\u003c/sub\u003e\u003c/i\u003e | \u003csup\u003ekg\u003c/sup\u003e\u0026#8725;\u003csub\u003em³\u003c/sub\u003e | SRT or TRT | LBM collision operator |\n | \u003ci\u003e\u0026tau;\u003c/i\u003e | s | \u003ci\u003e\u0026tau;\u003c/i\u003e = \u003csup\u003e\u003ci\u003e\u0026nu;\u003c/i\u003e\u003c/sup\u003e\u0026#8725;\u003csub\u003e\u003ci\u003ec\u003c/i\u003e²\u003c/sub\u003e + \u003csup\u003e\u0026Delta;\u003ci\u003et\u003c/i\u003e\u003c/sup\u003e\u0026#8725;\u003csub\u003e2\u003c/sub\u003e | LBM relaxation time |\n\n \u003c/details\u003e\n - velocity sets: D2Q9, D3Q15, D3Q19 (default), D3Q27\n - collision operators: single-relaxation-time (SRT/BGK) (default), two-relaxation-time (TRT)\n - [DDF-shifting](https://www.researchgate.net/publication/362275548_Accuracy_and_performance_of_the_lattice_Boltzmann_method_with_64-bit_32-bit_and_customized_16-bit_number_formats) and other algebraic optimization to minimize round-off error\n\n \u003c/details\u003e\n\n\u003c!-- markdown equations don't render properly in mobile browser\n - streaming (part 2/2):\n$$j=0\\\\ \\textrm{for}\\\\ i=0$$\n$$j=t\\\\%2\\\\ ?\\\\ i\\\\ :\\\\ (i\\\\%2\\\\ ?\\\\ i+1\\\\ :\\\\ i-1)\\\\ \\textrm{for}\\\\ i\\in[1,q-1]$$\n$$f_i^\\textrm{temp}(\\vec{x},t)=f_j(i\\\\%2\\\\ ?\\\\ \\vec{x}\\\\ :\\\\ \\vec{x}-\\vec{e}_i,\\\\ t)$$\n - collision:\n$$\\rho(\\vec{x},t)=\\left(\\sum_i f_i^\\textrm{temp}(\\vec{x},t)\\right)+1$$\n$$\\vec{u}(\\vec{x},t)=\\frac{1}{\\rho(\\vec{x},t)}\\sum_i\\vec{c}_i f_i^\\textrm{temp}(\\vec{x},t)$$\n$$f_i^\\textrm{eq-shifted}(\\vec{x},t)=w_i \\rho \\cdot\\left(\\frac{(\\vec{u} _{^{^\\circ}}\\vec{c}_i)^2}{2 c^4}-\\frac{\\vec{u} _{^{^\\circ}}\\vec{u}}{2 c^2}+\\frac{\\vec{u} _{^{^\\circ}}\\vec{c}_i}{c^2}\\right)+w_i (\\rho-1)$$\n$$f_i^\\textrm{temp}(\\vec{x},\\\\ t+\\Delta t)=f_i^\\textrm{temp}(\\vec{x},t)+\\Omega_i(f_i^\\textrm{temp}(\\vec{x},t),\\\\ f_i^\\textrm{eq-shifted}(\\vec{x},t),\\\\ \\tau)$$\n - streaming (part 1/2):\n$$j=0\\\\ \\textrm{for}\\\\ i=0$$\n$$j=t\\\\%2\\\\ ?\\\\ (i\\\\%2\\\\ ?\\\\ i+1\\\\ :\\\\ i-1)\\\\ :\\\\ i\\\\ \\textrm{for}\\\\ i\\in[1,q-1]$$\n$$f_j(i\\\\%2\\\\ ?\\\\ \\vec{x}+\\vec{e}_i\\\\ :\\\\ \\vec{x},\\\\ t+\\Delta t)=f_i^\\textrm{temp}(\\vec{x},\\\\ t+\\Delta t)$$\n --\u003e\n\n- \u003cdetails\u003e\u003csummary\u003e\u003ca name=\"vram-footprint\"\u003e\u003c/a\u003eoptimized to minimize VRAM footprint to 1/6 of other LBM codes\u003c/summary\u003e\n\n - traditional LBM (D3Q19) with FP64 requires ~344 Bytes/cell\u003cbr\u003e\n - 🟧🟧🟧🟧🟧🟧🟧🟧🟦🟦🟦🟦🟦🟦🟦🟦🟦🟦🟦🟦🟦🟦🟦🟦🟦🟦🟦🟦🟦🟦🟦🟦🟨🟨🟨🟨🟨🟨🟨🟨🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥🟥\u003cbr\u003e(density 🟧, velocity 🟦, flags 🟨, 2 copies of DDFs 🟩/🟥; each square = 1 Byte)\n - allows for 3 Million cells per 1 GB VRAM\n - FluidX3D (D3Q19) requires only 55 Bytes/cell with [Esoteric-Pull](https://doi.org/10.3390/computation10060092)+[FP16](https://www.researchgate.net/publication/362275548_Accuracy_and_performance_of_the_lattice_Boltzmann_method_with_64-bit_32-bit_and_customized_16-bit_number_formats)\u003cbr\u003e\n - 🟧🟧🟧🟧🟦🟦🟦🟦🟦🟦🟦🟦🟦🟦🟦🟦🟨🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩\u003cbr\u003e(density 🟧, velocity 🟦, flags 🟨, DDFs 🟩; each square = 1 Byte)\n - allows for 19 Million cells per 1 GB VRAM\n - in-place streaming with [Esoteric-Pull](https://doi.org/10.3390/computation10060092): eliminates redundant copy of density distribution functions (DDFs) in memory; almost cuts memory demand in half and slightly increases performance due to implicit bounce-back boundaries; offers optimal memory access patterns for single-cell in-place streaming\n - [decoupled arithmetic precision (FP32) and memory precision (FP32 or FP16S or FP16C)](https://www.researchgate.net/publication/362275548_Accuracy_and_performance_of_the_lattice_Boltzmann_method_with_64-bit_32-bit_and_customized_16-bit_number_formats): all arithmetic is done in FP32 for compatibility on all hardware, but DDFs in memory can be compressed to FP16S or FP16C: almost cuts memory demand in half again and almost doubles performance, without impacting overall accuracy for most setups\n - \u003cdetails\u003e\u003csummary\u003eonly 8 flag bits per lattice point (can be used independently / at the same time)\u003c/summary\u003e\n\n - `TYPE_S` (stationary or moving) solid boundaries\n - `TYPE_E` equilibrium boundaries (inflow/outflow)\n - `TYPE_T` temperature boundaries\n - `TYPE_F` free surface (fluid)\n - `TYPE_I` free surface (interface)\n - `TYPE_G` free surface (gas)\n - `TYPE_X` remaining for custom use or further extensions\n - `TYPE_Y` remaining for custom use or further extensions\n\n \u003c/details\u003e\n - large cost saving: comparison of maximum single-GPU grid resolution for D3Q19 LBM\n\n | GPU\u0026nbsp;VRAM\u0026nbsp;capacity | 1\u0026nbsp;GB | 2\u0026nbsp;GB | 3\u0026nbsp;GB | 4\u0026nbsp;GB | 6\u0026nbsp;GB | 8\u0026nbsp;GB | 10\u0026nbsp;GB | 11\u0026nbsp;GB | 12\u0026nbsp;GB | 16\u0026nbsp;GB | 20\u0026nbsp;GB | 24\u0026nbsp;GB | 32\u0026nbsp;GB | 40\u0026nbsp;GB | 48\u0026nbsp;GB | 64\u0026nbsp;GB | 80\u0026nbsp;GB | 94\u0026nbsp;GB | 128\u0026nbsp;GB | 192\u0026nbsp;GB | 256\u0026nbsp;GB |\n | :------------------------------- | --------: | --------: | --------: | --------: | --------: | --------: | ---------: | ---------: | ---------: | ---------: | ---------: | ---------: | ---------: | ---------: | ---------: | ---------: | ---------: | ---------: | ----------: | ----------: | ----------: |\n | approximate\u0026nbsp;GPU\u0026nbsp;price | $25\u003cbr\u003eGT\u0026nbsp;210 | $25\u003cbr\u003eGTX\u0026nbsp;950 | $12\u003cbr\u003eGTX\u0026nbsp;1060 | $50\u003cbr\u003eGT\u0026nbsp;730 | $35\u003cbr\u003eGTX\u0026nbsp;1060 | $70\u003cbr\u003eRX\u0026nbsp;470 | $500\u003cbr\u003eRTX\u0026nbsp;3080 | $240\u003cbr\u003eGTX\u0026nbsp;1080\u0026nbsp;Ti | $75\u003cbr\u003eTesla\u0026nbsp;M40 | $75\u003cbr\u003eInstinct\u0026nbsp;MI25 | $900\u003cbr\u003eRX\u0026nbsp;7900\u0026nbsp;XT | $205\u003cbr\u003eTesla\u0026nbsp;P40 | $600\u003cbr\u003eInstinct\u0026nbsp;MI60 | $5500\u003cbr\u003eA100 | $2400\u003cbr\u003eRTX\u0026nbsp;8000 | $10k\u003cbr\u003eInstinct\u0026nbsp;MI210 | $11k\u003cbr\u003eA100 | \u003e$40k\u003cbr\u003eH100\u0026nbsp;NVL | ?\u003cbr\u003eGPU\u0026nbsp;Max\u0026nbsp;1550 | ~$10k\u003cbr\u003eMI300X | - |\n | traditional\u0026nbsp;LBM\u0026nbsp;(FP64) | 144³ | 182³ | 208³ | 230³ | 262³ | 288³ | 312³ | 322³ | 330³ | 364³ | 392³ | 418³ | 460³ | 494³ | 526³ | 578³ | 624³ | 658³ | 730³ | 836³ | 920³ |\n | FluidX3D\u0026nbsp;(FP32/FP32) | 224³ | 282³ | 322³ | 354³ | 406³ | 448³ | 482³ | 498³ | 512³ | 564³ | 608³ | 646³ | 710³ | 766³ | 814³ | 896³ | 966³ | 1018³ | 1130³ | 1292³ | 1422³ |\n | FluidX3D\u0026nbsp;(FP32/FP16) | 266³ | 336³ | 384³ | 424³ | 484³ | 534³ | 574³ | 594³ | 610³ | 672³ | 724³ | 770³ | 848³ | 912³ | 970³ | 1068³ | 1150³ | 1214³ | 1346³ | 1540³ | 1624³ |\n\n \u003c/details\u003e\n- \u003cdetails\u003e\u003csummary\u003e\u003ca name=\"multi-gpu\"\u003e\u003c/a\u003ecross-vendor multi-GPU support on a single computer/server\u003c/summary\u003e\n\n - domain decomposition allows pooling VRAM from multiple GPUs for much larger grid resolution\n - GPUs don't have to be identical, not even from the same vendor - \u003ca href=\"https://youtu.be/_8Ed8ET9gBU\"\u003eany combination of AMD+Intel+Nvidia GPUs will work\u003c/a\u003e - but similar VRAM capacity/bandwidth is recommended\n - domain communication architecture (simplified)\n ```diff\n ++ .-----------------------------------------------------------------. ++\n ++ | GPU 0 | ++\n ++ | LBM Domain 0 | ++\n ++ '-----------------------------------------------------------------' ++\n ++ | selective /|\\ ++\n ++ \\|/ in-VRAM copy | ++\n ++ .-------------------------------------------------------. ++\n ++ | GPU 0 - Transfer Buffer 0 | ++\n ++ '-------------------------------------------------------' ++\n !! | PCIe /|\\ !!\n !! \\|/ copy | !!\n @@ .-------------------------. .-------------------------. @@\n @@ | CPU - Transfer Buffer 0 | | CPU - Transfer Buffer 1 | @@\n @@ '-------------------------'\\ /'-------------------------' @@\n @@ pointer X swap @@\n @@ .-------------------------./ \\.-------------------------. @@\n @@ | CPU - Transfer Buffer 1 | | CPU - Transfer Buffer 0 | @@\n @@ '-------------------------' '-------------------------' @@\n !! /|\\ PCIe | !!\n !! | copy \\|/ !!\n ++ .-------------------------------------------------------. ++\n ++ | GPU 1 - Transfer Buffer 1 | ++\n ++ '-------------------------------------------------------' ++\n ++ /|\\ selective | ++\n ++ | in-VRAM copy \\|/ ++\n ++ .-----------------------------------------------------------------. ++\n ++ | GPU 1 | ++\n ++ | LBM Domain 1 | ++\n ++ '-----------------------------------------------------------------' ++\n ## | ##\n ## domain synchronization barrier ##\n ## | ##\n || -------------------------------------------------------------\u003e time ||\n ```\n - domain communication architecture (detailed)\n ```diff\n ++ .-----------------------------------------------------------------. ++\n ++ | GPU 0 | ++\n ++ | LBM Domain 0 | ++\n ++ '-----------------------------------------------------------------' ++\n ++ | selective in- /|\\ | selective in- /|\\ | selective in- /|\\ ++\n ++ \\|/ VRAM copy (X) | \\|/ VRAM copy (Y) | \\|/ VRAM copy (Z) | ++\n ++ .---------------------.---------------------.---------------------. ++\n ++ | GPU 0 - TB 0X+ | GPU 0 - TB 0Y+ | GPU 0 - TB 0Z+ | ++\n ++ | GPU 0 - TB 0X- | GPU 0 - TB 0Y- | GPU 0 - TB 0Z- | ++\n ++ '---------------------'---------------------'---------------------' ++\n !! | PCIe /|\\ | PCIe /|\\ | PCIe /|\\ !!\n !! \\|/ copy | \\|/ copy | \\|/ copy | !!\n @@ .---------. .---------.---------. .---------.---------. .---------. @@\n @@ | CPU 0X+ | | CPU 1X- | CPU 0Y+ | | CPU 3Y- | CPU 0Z+ | | CPU 5Z- | @@\n @@ | CPU 0X- | | CPU 2X+ | CPU 0Y- | | CPU 4Y+ | CPU 0Z- | | CPU 6Z+ | @@\n @@ '---------\\ /---------'---------\\ /---------'---------\\ /---------' @@\n @@ pointer X swap (X) pointer X swap (Y) pointer X swap (Z) @@\n @@ .---------/ \\---------.---------/ \\---------.---------/ \\---------. @@\n @@ | CPU 1X- | | CPU 0X+ | CPU 3Y- | | CPU 0Y+ | CPU 5Z- | | CPU 0Z+ | @@\n @@ | CPU 2X+ | | CPU 0X- | CPU 4Y+ | | CPU 0Y- | CPU 6Z+ | | CPU 0Z- | @@\n @@ '---------' '---------'---------' '---------'---------' '---------' @@\n !! /|\\ PCIe | /|\\ PCIe | /|\\ PCIe | !!\n !! | copy \\|/ | copy \\|/ | copy \\|/ !!\n ++ .--------------------..---------------------..--------------------. ++\n ++ | GPU 1 - TB 1X- || GPU 3 - TB 3Y- || GPU 5 - TB 5Z- | ++\n ++ :====================::=====================::====================: ++\n ++ | GPU 2 - TB 2X+ || GPU 4 - TB 4Y+ || GPU 6 - TB 6Z+ | ++\n ++ '--------------------''---------------------''--------------------' ++\n ++ /|\\ selective in- | /|\\ selective in- | /|\\ selective in- | ++\n ++ | VRAM copy (X) \\|/ | VRAM copy (Y) \\|/ | VRAM copy (Z) \\|/ ++\n ++ .--------------------..---------------------..--------------------. ++\n ++ | GPU 1 || GPU 3 || GPU 5 | ++\n ++ | LBM Domain 1 || LBM Domain 3 || LBM Domain 5 | ++\n ++ :====================::=====================::====================: ++\n ++ | GPU 2 || GPU 4 || GPU 6 | ++\n ++ | LBM Domain 2 || LBM Domain 4 || LBM Domain 6 | ++\n ++ '--------------------''---------------------''--------------------' ++\n ## | | | ##\n ## | domain synchronization barriers | ##\n ## | | | ##\n || -------------------------------------------------------------\u003e time ||\n ```\n\n \u003c/details\u003e\n- \u003cdetails\u003e\u003csummary\u003e\u003ca name=\"performance\"\u003e\u003c/a\u003epeak performance on GPUs (datacenter/gaming/professional/laptop)\u003c/summary\u003e\n\n - [single-GPU/CPU benchmarks](#single-gpucpu-benchmarks)\n - [multi-GPU benchmarks](#multi-gpu-benchmarks)\n\n \u003c/details\u003e\n- \u003cdetails\u003e\u003csummary\u003e\u003ca name=\"extensions\"\u003e\u003c/a\u003epowerful model extensions\u003c/summary\u003e\n\n - [boundary types](https://doi.org/10.15495/EPub_UBT_00005400)\n - stationary mid-grid bounce-back boundaries (stationary solid boundaries)\n - moving mid-grid bounce-back boundaries (moving solid boundaries)\n - equilibrium boundaries (non-reflective inflow/outflow)\n - temperature boundaries (fixed temperature)\n - global force per volume (Guo forcing), can be modified on-the-fly\n - local force per volume (force field)\n - optional computation of forces from the fluid on solid boundaries\n - state-of-the-art [free surface LBM](https://doi.org/10.3390/computation10060092) (FSLBM) implementation:\n - [volume-of-fluid model](https://doi.org/10.15495/EPub_UBT_00005400)\n - [fully analytic PLIC](https://doi.org/10.3390/computation10020021) for efficient curvature calculation\n - improved mass conservation\n - ultra efficient implementation with only [4 kernels](https://doi.org/10.3390/computation10060092) additionally to `stream_collide()` kernel\n - thermal LBM to simulate thermal convection\n - D3Q7 subgrid for thermal DDFs\n - in-place streaming with [Esoteric-Pull](https://doi.org/10.3390/computation10060092) for thermal DDFs\n - optional [FP16S or FP16C compression](https://www.researchgate.net/publication/362275548_Accuracy_and_performance_of_the_lattice_Boltzmann_method_with_64-bit_32-bit_and_customized_16-bit_number_formats) for thermal DDFs with [DDF-shifting](https://www.researchgate.net/publication/362275548_Accuracy_and_performance_of_the_lattice_Boltzmann_method_with_64-bit_32-bit_and_customized_16-bit_number_formats)\n - Smagorinsky-Lilly subgrid turbulence LES model to keep simulations with very large Reynolds number stable\n \u003cp align=\"center\"\u003e\u003ci\u003e\u0026Pi;\u003csub\u003e\u0026alpha;\u0026beta;\u003c/sub\u003e\u003c/i\u003e = \u0026Sigma;\u003csub\u003e\u003ci\u003ei\u003c/i\u003e\u003c/sub\u003e \u003ci\u003ee\u003csub\u003ei\u0026alpha;\u003c/sub\u003e\u003c/i\u003e \u003ci\u003ee\u003csub\u003ei\u0026beta;\u003c/sub\u003e\u003c/i\u003e (\u003ci\u003ef\u003csub\u003ei\u003c/sub\u003e\u003c/i\u003e - \u003ci\u003ef\u003csub\u003ei\u003c/sub\u003e\u003c/i\u003e\u003csup\u003eeq-shifted\u003c/sup\u003e)\u003cbr\u003e\u003cbr\u003eQ = \u0026Sigma;\u003csub\u003e\u003ci\u003e\u0026alpha;\u0026beta;\u003c/i\u003e\u003c/sub\u003e \u003ci\u003e\u0026Pi;\u003csub\u003e\u0026alpha;\u0026beta;\u003c/sub\u003e\u003c/i\u003e\u003csup\u003e2\u003c/sup\u003e\u003cbr\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;______________________\u003cbr\u003e\u0026tau; = \u0026frac12; (\u0026tau;\u003csub\u003e0\u003c/sub\u003e + \u0026radic; \u0026tau;\u003csub\u003e0\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e + \u003csup\u003e(16\u0026radic;2)\u003c/sup\u003e\u0026#8725;\u003csub\u003e(\u003ci\u003e3\u0026pi;\u003c/i\u003e\u003csup\u003e2\u003c/sup\u003e)\u003c/sub\u003e \u003csup\u003e\u0026radic;Q\u003c/sup\u003e\u0026#8725;\u003csub\u003e\u003ci\u003e\u0026rho;\u003c/i\u003e\u003c/sub\u003e )\u003c/p\u003e\n - particles with immersed-boundary method (either passive or 2-way-coupled, single-GPU only)\n\n \u003c/details\u003e\n\n\n\n## Solving the Visualization Problem\n\n- FluidX3D can do simulations so large that storing the volumetric data for later rendering becomes unmanageable (like 120GB for a single frame, hundreds of TeraByte for a video)\n- instead, FluidX3D allows [rendering raw simulation data directly in VRAM](https://www.researchgate.net/publication/360501260_Combined_scientific_CFD_simulation_and_interactive_raytracing_with_OpenCL), so no large volumetric files have to be exported to the hard disk (see my [technical talk](https://youtu.be/pD8JWAZ2f8o))\n- the rendering is so fast that it works interactively in real time for both rasterization and raytracing\n- rasterization and raytracing are done in OpenCL and work on all GPUs, even the ones without RTX/DXR raytracing cores or without any rendering hardware at all (like A100, MI200, ...)\n- if no monitor is available (like on a remote Linux server), there is an [ASCII rendering mode](https://youtu.be/pD8JWAZ2f8o\u0026t=1456) to interactively visualize the simulation in the terminal (even in WSL and/or through SSH)\n- rendering is fully multi-GPU-parallelized via seamless domain decomposition rasterization\n- with interactive graphics mode disabled, image resolution can be as large as VRAM allows for (4K/8K/16K and above)\n- (interacitive) visualization modes:\n - flag wireframe / solid surface (and force vectors on solid cells or surface pressure if the extension is used)\n - velocity field (with slice mode)\n - streamlines (with slice mode)\n - velocity-colored Q-criterion isosurface\n - rasterized free surface with [marching-cubes](http://paulbourke.net/geometry/polygonise/)\n - [raytraced free surface](https://www.researchgate.net/publication/360501260_Combined_scientific_CFD_simulation_and_interactive_raytracing_with_OpenCL) with fast ray-grid traversal and marching-cubes, either 1-4 rays/pixel or 1-10 rays/pixel\n\n\n\n## Solving the Compatibility Problem\n\n- FluidX3D is written in OpenCL 1.2, so it runs on all hardware from all vendors (Nvidia, AMD, Intel, ...):\n - world's fastest datacenter GPUs: MI300X, H100 (NVL), A100, MI200, MI100, V100(S), GPU Max 1100, ...\n - gaming GPUs (desktop/laptop): Nvidia GeForce, AMD Radeon, Intel Arc\n - professional/workstation GPUs: Nvidia Quadro, AMD Radeon Pro / FirePro, Intel Arc Pro\n - integrated GPUs\n - CPUs (requires [installation of Intel CPU Runtime for OpenCL](DOCUMENTATION.md#0-install-gpu-drivers-and-opencl-runtime))\n - Intel Xeon Phi (requires [installation of Intel CPU Runtime for OpenCL](DOCUMENTATION.md#0-install-gpu-drivers-and-opencl-runtime))\n - smartphone ARM GPUs\n- native cross-vendor multi-GPU implementation\n - uses PCIe communication, so no SLI/Crossfire/NVLink/InfinityFabric required\n - single-node parallelization, so no MPI installation required\n - [GPUs don't even have to be from the same vendor](https://youtu.be/_8Ed8ET9gBU), but similar memory capacity and bandwidth are recommended\n- works on [Windows](DOCUMENTATION.md#windows) and [Linux](DOCUMENTATION.md#linux--macos--android) with C++17, with limited support also for [macOS](DOCUMENTATION.md#linux--macos--android) and [Android](DOCUMENTATION.md#linux--macos--android)\n- supports [importing and voxelizing triangle meshes](DOCUMENTATION.md#loading-stl-files) from binary `.stl` files, with fast GPU voxelization\n- supports [exporting volumetric data](DOCUMENTATION.md#data-export) as binary `.vtk` files\n- supports [exporting triangle meshes](DOCUMENTATION.md#data-export) as binary `.vtk` files\n- supports [exporting rendered images](DOCUMENTATION.md#video-rendering) as `.png`/`.qoi`/`.bmp` files; encoding runs in parallel on the CPU while the simulation on GPU can continue without delay\n\n\n\n## Single-GPU/CPU Benchmarks\n\nHere are [performance benchmarks](https://doi.org/10.3390/computation10060092) on various hardware in MLUPs/s, or how many million lattice cells are updated per second. The settings used for the benchmark are D3Q19 SRT with no extensions enabled (only LBM with implicit mid-grid bounce-back boundaries) and the setup consists of an empty cubic box with sufficient size (typically 256³). Without extensions, a single lattice cell requires:\n- a memory capacity of 93 (FP32/FP32) or 55 (FP32/FP16) Bytes\n- a memory bandwidth of 153 (FP32/FP32) or 77 (FP32/FP16) Bytes per time step\n- 363 (FP32/FP32) or 406 (FP32/FP16S) or 1275 (FP32/FP16C) FLOPs per time step (FP32+INT32 operations counted combined)\n\nIn consequence, the arithmetic intensity of this implementation is 2.37 (FP32/FP32) or 5.27 (FP32/FP16S) or 16.56 (FP32/FP16C) FLOPs/Byte. So performance is only limited by memory bandwidth. The table in the left 3 columns shows the hardware specs as found in the data sheets (theoretical peak FP32 compute performance, memory capacity, theoretical peak memory bandwidth). The right 3 columns show the measured FluidX3D performance for FP32/FP32, FP32/FP16S, FP32/FP16C floating-point precision settings, with the ([roofline model](https://en.wikipedia.org/wiki/Roofline_model) efficiency) in round brackets, indicating how much % of theoretical peak memory bandwidth are being used.\n\nIf your GPU/CPU is not on the list yet, you can report your benchmarks [here](https://github.com/ProjectPhysX/FluidX3D/issues/8).\n\n```mermaid\ngantt\n\ntitle FluidX3D Performance [MLUPs/s] - FP32 arithmetic, (fastest of FP32/FP16S/FP16C) memory storage\ndateFormat X\naxisFormat %s\n%%{\n\tinit: {\n\t\t\"gantt\": {\n\t\t\t'titleTopMargin': 42,\n\t\t\t'topPadding': 70,\n\t\t\t'leftPadding': 260,\n\t\t\t'rightPadding': 5,\n\t\t\t'sectionFontSize': 20,\n\t\t\t'fontSize': 20,\n\t\t\t'barHeight': 20,\n\t\t\t'barGap': 3,\n\t\t\t'numberSectionStyles': 2\n\t\t},\n\t\t'theme': 'forest',\n\t\t'themeVariables': {\n\t\t\t'sectionBkgColor': '#99999999',\n\t\t\t'altSectionBkgColor': '#00000000',\n\t\t\t'titleColor': '#AFAFAF',\n\t\t\t'textColor': '#AFAFAF',\n\t\t\t'taskTextColor': 'black',\n\t\t\t'taskBorderColor': '#487E3A'\n\t\t}\n\t}\n}%%\n\nsection MI300X\n\t41327 :crit, 0, 41327\nsection MI250 (1 GCD)\n\t9030 :crit, 0, 9030\nsection MI210\n\t9547 :crit, 0, 9547\nsection MI100\n\t8542 :crit, 0, 8542\nsection MI60\n\t5111 :crit, 0, 5111\nsection MI50 32GB\n\t8477 :crit, 0, 8477\nsection Radeon VII\n\t7778 :crit, 0, 7778\nsection GPU Max 1100\n\t6303 :done, 0, 6303\nsection GH200 94GB GPU\n\t34689 : 0, 34689\nsection H100 NVL\n\t32922 : 0, 32922\nsection H100 SXM5 80GB HBM3\n\t29561 : 0, 29561\nsection H100 PCIe 80GB HBM2e\n\t20624 : 0, 20624\nsection A100 SXM4 80GB\n\t18448 : 0, 18448\nsection A100 PCIe 80GB\n\t17896 : 0, 17896\nsection PG506-242/243\n\t15654 : 0, 15654\nsection A100 SXM4 40GB\n\t16013 : 0, 16013\nsection A100 PCIe 40GB\n\t16035 : 0, 16035\nsection CMP 170HX\n\t12392 : 0, 12392\nsection A30\n\t9721 : 0, 9721\nsection V100 SXM2 32GB\n\t8947 : 0, 8947\nsection V100 PCIe 16GB\n\t10325 : 0, 10325\nsection GV100\n\t6641 : 0, 6641\nsection Titan V\n\t7253 : 0, 7253\nsection P100 PCIe 16GB\n\t5950 : 0, 5950\nsection P100 PCIe 12GB\n\t4141 : 0, 4141\nsection GTX TITAN\n\t2500 : 0, 2500\nsection K40m\n\t1868 : 0, 1868\nsection K80 (1 GPU)\n\t1642 : 0, 1642\nsection K20c\n\t1507 : 0, 1507\n\nsection RX 9070 XT\n\t6688 :crit, 0, 6688\nsection RX 9070\n\t6019 :crit, 0, 6019\nsection RX 7900 XTX\n\t7716 :crit, 0, 7716\nsection PRO W7900\n\t5939 :crit, 0, 5939\nsection RX 7900 XT\n\t5986 :crit, 0, 5986\nsection RX 7800 XT\n\t3105 :crit, 0, 3105\nsection PRO W7800\n\t4426 :crit, 0, 4426\nsection RX 7900 GRE\n\t4570 :crit, 0, 4570\nsection PRO W7700\n\t2943 :crit, 0, 2943\nsection RX 7600\n\t2561 :crit, 0, 2561\nsection PRO W7600\n\t2287 :crit, 0, 2287\nsection PRO W7500\n\t1682 :crit, 0, 1682\nsection RX 6900 XT\n\t4227 :crit, 0, 4227\nsection RX 6800 XT\n\t4241 :crit, 0, 4241\nsection PRO W6800\n\t3361 :crit, 0, 3361\nsection RX 6700 XT\n\t2908 :crit, 0, 2908\nsection RX 6800M\n\t3213 :crit, 0, 3213\nsection RX 6700M\n\t2429 :crit, 0, 2429\nsection RX 6600\n\t1839 :crit, 0, 1839\nsection RX 6500 XT\n\t1030 :crit, 0, 1030\nsection RX 5700 XT\n\t3253 :crit, 0, 3253\nsection RX 5700\n\t3167 :crit, 0, 3167\nsection RX 5600 XT\n\t2214 :crit, 0, 2214\nsection RX Vega 64\n\t3227 :crit, 0, 3227\nsection RX 590\n\t1688 :crit, 0, 1688\nsection RX 580 4GB\n\t1848 :crit, 0, 1848\nsection RX 580 2048SP 8GB\n\t1622 :crit, 0, 1622\nsection R9 390X\n\t2217 :crit, 0, 2217\nsection HD 7850\n\t635 :crit, 0, 635\nsection Arc B580 LE\n\t4979 :done, 0, 4979\nsection Arc A770 LE\n\t4568 :done, 0, 4568\nsection Arc A750 LE\n\t4314 :done, 0, 4314\nsection Arc A580\n\t3889 :done, 0, 3889\nsection Arc Pro A40\n\t985 :done, 0, 985\nsection Arc A380\n\t1115 :done, 0, 1115\nsection RTX 5090\n\t19141 : 0, 19141\nsection RTX 5080\n\t10304 : 0, 10304\nsection RTX 5070\n\t7238 : 0, 7238\nsection RTX 4090\n\t11496 : 0, 11496\nsection RTX 6000 Ada\n\t10293 : 0, 10293\nsection L40S\n\t7637 : 0, 7637\nsection L40\n\t7945 : 0, 7945\nsection RTX 4080 Super\n\t8218 : 0, 8218\nsection RTX 4080\n\t7933 : 0, 7933\nsection RTX 4070 Ti Super\n\t7295 : 0, 7295\nsection RTX 4090M\n\t6901 : 0, 6901\nsection RTX 4070 Super\n\t5554 : 0, 5554\nsection RTX 4070\n\t5016 : 0, 5016\nsection RTX 4080M\n\t5114 : 0, 5114\nsection RTX 4000 Ada\n\t4221 : 0, 4221\nsection RTX 4060\n\t3124 : 0, 3124\nsection RTX 4070M\n\t3092 : 0, 3092\nsection RTX 2000 Ada\n\t2526 : 0, 2526\nsection RTX 3090 Ti\n\t10956 : 0, 10956\nsection RTX 3090\n\t10732 : 0, 10732\nsection RTX 3080 Ti\n\t9832 : 0, 9832\nsection RTX 3080 12GB\n\t9657 : 0, 9657\nsection RTX A6000\n\t8814 : 0, 8814\nsection RTX 3080 10GB\n\t8118 : 0, 8118\nsection RTX 3070 Ti\n\t6807 : 0, 6807\nsection RTX 3080M Ti\n\t5908 : 0, 5908\nsection RTX 3070\n\t5096 : 0, 5096\nsection RTX 3060 Ti\n\t5129 : 0, 5129\nsection RTX A4000\n\t4945 : 0, 4945\nsection RTX A5000M\n\t4461 : 0, 4461\nsection RTX 3060\n\t4070 : 0, 4070\nsection RTX 3060M\n\t4012 : 0, 4012\nsection A2\n\t2051 : 0, 2051\nsection RTX 3050M Ti\n\t2341 : 0, 2341\nsection RTX 3050M\n\t2339 : 0, 2339\nsection Titan RTX\n\t7554 : 0, 7554\nsection RTX 6000\n\t6879 : 0, 6879\nsection RTX 8000 Passive\n\t5607 : 0, 5607\nsection RTX 2080 Ti\n\t6853 : 0, 6853\nsection RTX 2080 Super\n\t5284 : 0, 5284\nsection RTX 5000\n\t4773 : 0, 4773\nsection RTX 2080\n\t4977 : 0, 4977\nsection RTX 2070 Super\n\t4893 : 0, 4893\nsection RTX 2070\n\t5017 : 0, 5017\nsection RTX 2060 Super\n\t5035 : 0, 5035\nsection RTX 4000\n\t4584 : 0, 4584\nsection RTX 2060 KO\n\t3376 : 0, 3376\nsection RTX 2060\n\t3604 : 0, 3604\nsection GTX 1660 Super\n\t3551 : 0, 3551\nsection T4\n\t2887 : 0, 2887\nsection GTX 1660 Ti\n\t3041 : 0, 3041\nsection GTX 1660\n\t1992 : 0, 1992\nsection GTX 1650M 896C\n\t1858 : 0, 1858\nsection GTX 1650M 1024C\n\t1400 : 0, 1400\nsection T500\n\t665 : 0, 665\nsection Titan Xp\n\t5495 : 0, 5495\nsection GTX 1080 Ti\n\t4877 : 0, 4877\nsection GTX 1080\n\t3182 : 0, 3182\nsection GTX 1060 6GB\n\t1925 : 0, 1925\nsection GTX 1060M\n\t1882 : 0, 1882\nsection GTX 1050M Ti\n\t1224 : 0, 1224\nsection P1000\n\t839 : 0, 839\nsection GTX 980 Ti\n\t2703 : 0, 2703\nsection GTX 980\n\t1965 : 0, 1965\nsection GTX 970\n\t1721 : 0, 1721\nsection M4000\n\t1519 : 0, 1519\nsection M60 (1 GPU)\n\t1571 : 0, 1571\nsection GTX 960M\n\t872 : 0, 872\nsection GTX 770\n\t1215 : 0, 1215\nsection GTX 680 4GB\n\t1274 : 0, 1274\nsection K2000\n\t444 : 0, 444\nsection GT 630 (OEM)\n\t185 : 0, 185\nsection NVS 290\n\t9 : 0, 9\nsection Arise 1020\n\t6 :active, 0, 6\n\nsection M2 Ultra (76-CU, 192GB)\n\t8769 :active, 0, 8769\nsection M2 Max (38-CU, 32GB)\n\t4641 :active, 0, 4641\nsection M1 Ultra (64-CU, 128GB)\n\t8418 :active, 0, 8418\nsection M1 Max (24-CU, 32GB)\n\t4496 :active, 0, 4496\nsection M1 Pro (16-CU, 16GB)\n\t2329 :active, 0, 2329\nsection M1 (8-CU, 16GB)\n\t759 :active, 0, 759\nsection Radeon 8060S (Max+ 395)\n\t2563 :crit, 0, 2563\nsection Radeon 780M (Z1 Extreme)\n\t860 :crit, 0, 860\nsection Radeon Graphics (7800X3D)\n\t498 :crit, 0, 498\nsection Vega 8 (4750G)\n\t511 :crit, 0, 511\nsection Vega 8 (3500U)\n\t288 :crit, 0, 288\nsection Arc 140V GPU (16GB)\n\t1282 :done, 0, 1282\nsection Arc Graphics (Ultra 9 185H)\n\t724 :done, 0, 724\nsection Iris Xe Graphics (i7-1265U)\n\t621 :done, 0, 621\nsection UHD Xe 32EUs\n\t245 :done, 0, 245\nsection UHD 770\n\t475 :done, 0, 475\nsection UHD 630\n\t301 :done, 0, 301\nsection UHD P630\n\t288 :done, 0, 288\nsection HD 5500\n\t192 :done, 0, 192\nsection HD 4600\n\t115 :done, 0, 115\nsection Orange Pi 5 Mali-G610 MP4\n\t232 :active, 0, 232\nsection Samsung Mali-G72 MP18\n\t230 :active, 0, 230\n\nsection 2x EPYC 9754\n\t5179 :crit, 0, 5179\nsection 2x EPYC 9654\n\t1814 :crit, 0, 1814\nsection 2x EPYC 9554\n\t2552 :crit, 0, 2552\nsection 1x EPYC 9124\n\t772 :crit, 0, 772\nsection 2x EPYC 7713\n\t1418 :crit, 0, 1418\nsection 2x EPYC 7352\n\t739 :crit, 0, 739\nsection 2x EPYC 7313\n\t498 :crit, 0, 498\nsection 2x EPYC 7302\n\t784 :crit, 0, 784\nsection 2x 6980P\n\t7875 :done, 0, 7875\nsection 2x 6979P\n\t8135 :done, 0, 8135\nsection 2x Platinum 8592+\n\t3135 :done, 0, 3135\nsection 2x Gold 6548N\n\t1811 :done, 0, 1811\nsection 2x CPU Max 9480\n\t2037 :done, 0, 2037\nsection 2x Platinum 8480+\n\t2162 :done, 0, 2162\nsection 2x Platinum 8470\n\t2068 :done, 0, 2068\nsection 2x Gold 6438Y+\n\t1945 :done, 0, 1945\nsection 2x Platinum 8380\n\t1410 :done, 0, 1410\nsection 2x Platinum 8358\n\t1285 :done, 0, 1285\nsection 2x Platinum 8256\n\t396 :done, 0, 396\nsection 2x Platinum 8153\n\t691 :done, 0, 691\nsection 2x Gold 6248R\n\t755 :done, 0, 755\nsection 2x Gold 6128\n\t254 :done, 0, 254\nsection Phi 7210\n\t415 :done, 0, 415\nsection 4x E5-4620 v4\n\t460 :done, 0, 460\nsection 2x E5-2630 v4\n\t264 :done, 0, 264\nsection 2x E5-2623 v4\n\t125 :done, 0, 125\nsection 2x E5-2680 v3\n\t304 :done, 0, 304\nsection GH200 Neoverse-V2\n\t1323 : 0, 1323\nsection TR PRO 7995WX\n\t1715 :crit, 0, 1715\nsection TR 3970X\n\t463 :crit, 0, 463\nsection TR 1950X\n\t273 :crit, 0, 273\nsection Ryzen 7900X3D\n\t521 :crit, 0, 521\nsection Ryzen 7800X3D\n\t363 :crit, 0, 363\nsection Ryzen 5700X3D\n\t229 :crit, 0, 229\nsection FX-6100\n\t22 :crit, 0, 22\nsection Athlon X2 QL-65\n\t3 :crit, 0, 3\nsection Ultra 7 258V\n\t287 :done, 0, 287\nsection Ultra 9 185H\n\t317 :done, 0, 317\nsection i9-14900K\n\t490 :done, 0, 490\nsection i7-13700K\n\t504 :done, 0, 504\nsection i7-1265U\n\t128 :done, 0, 128\nsection i9-11900KB\n\t208 :done, 0, 208\nsection i9-10980XE\n\t286 :done, 0, 286\nsection E-2288G\n\t198 :done, 0, 198\nsection i7-9700\n\t103 :done, 0, 103\nsection i5-9600\n\t147 :done, 0, 147\nsection i7-8700K\n\t152 :done, 0, 152\nsection E-2176G\n\t201 :done, 0, 201\nsection i7-7700HQ\n\t108 :done, 0, 108\nsection E3-1240 v5\n\t141 :done, 0, 141\nsection i5-5300U\n\t37 :done, 0, 37\nsection i7-4770\n\t104 :done, 0, 104\nsection i7-4720HQ\n\t80 :done, 0, 80\nsection N2807\n\t7 :done, 0, 7\n```\n\n\u003cdetails\u003e\u003csummary\u003eSingle-GPU/CPU Benchmark Table\u003c/summary\u003e\n\nColors: 🔴 AMD, 🔵 Intel, 🟢 Nvidia, ⚪ Apple, 🟡 ARM, 🟤 Glenfly\n\n| Device | FP32\u003cbr\u003e[TFlops/s] | Mem\u003cbr\u003e[GB] | BW\u003cbr\u003e[GB/s] | FP32/FP32\u003cbr\u003e[MLUPs/s] | FP32/FP16S\u003cbr\u003e[MLUPs/s] | FP32/FP16C\u003cbr\u003e[MLUPs/s] |\n| :----------------------------------------------- | -----------------: | ----------: | -----------: | ---------------------: | ----------------------: | ----------------------: |\n| | | | | | | |\n| 🔴\u0026nbsp;Instinct\u0026nbsp;MI300X | 163.40 | 192 | 5300 | 22867\u0026nbsp;(66%) | 41327\u0026nbsp;(60%) | 31670\u0026nbsp;(46%) |\n| 🔴\u0026nbsp;Instinct\u0026nbsp;MI250\u0026nbsp;(1\u0026nbsp;GCD) | 45.26 | 64 | 1638 | 5638 (53%) | 9030 (42%) | 8506 (40%) |\n| 🔴\u0026nbsp;Instinct\u0026nbsp;MI210 | 45.26 | 64 | 1638 | 6517 (61%) | 9547 (45%) | 8829 (41%) |\n| 🔴\u0026nbsp;Instinct\u0026nbsp;MI100 | 46.14 | 32 | 1228 | 5093 (63%) | 8133 (51%) | 8542 (54%) |\n| 🔴\u0026nbsp;Instinct\u0026nbsp;MI60 | 14.75 | 32 | 1024 | 3570 (53%) | 5047 (38%) | 5111 (38%) |\n| 🔴\u0026nbsp;Instinct\u0026nbsp;MI50\u0026nbsp;32GB | 13.25 | 32 | 1024 | 4446 (66%) | 8477 (64%) | 4406 (33%) |\n| 🔴\u0026nbsp;Radeon\u0026nbsp;VII | 13.83 | 16 | 1024 | 4898 (73%) | 7778 (58%) | 5256 (40%) |\n| 🔵\u0026nbsp;Data\u0026nbsp;Center\u0026nbsp;GPU\u0026nbsp;Max\u0026nbsp;1100 | 22.22 | 48 | 1229 | 3769 (47%) | 6303 (39%) | 3520 (22%) |\n| 🟢\u0026nbsp;GH200\u0026nbsp;94GB\u0026nbsp;GPU | 66.91 | 94 | 4000 | 20595\u0026nbsp;(79%) | 34689\u0026nbsp;(67%) | 19407\u0026nbsp;(37%) |\n| 🟢\u0026nbsp;H100\u0026nbsp;NVL | 60.32 | 94 | 3938 | 20303\u0026nbsp;(79%) | 32922\u0026nbsp;(64%) | 18424\u0026nbsp;(36%) |\n| 🟢\u0026nbsp;H100\u0026nbsp;SXM5\u0026nbsp;80GB\u0026nbsp;HBM3 | 66.91 | 80 | 3350 | 17602\u0026nbsp;(80%) | 29561\u0026nbsp;(68%) | 20227\u0026nbsp;(46%) |\n| 🟢\u0026nbsp;H100\u0026nbsp;PCIe\u0026nbsp;80GB\u0026nbsp;HBM2e | 51.01 | 80 | 2000 | 11128\u0026nbsp;(85%) | 20624\u0026nbsp;(79%) | 13862\u0026nbsp;(53%) |\n| 🟢\u0026nbsp;A100\u0026nbsp;SXM4\u0026nbsp;80GB | 19.49 | 80 | 2039 | 10228\u0026nbsp;(77%) | 18448\u0026nbsp;(70%) | 11197\u0026nbsp;(42%) |\n| 🟢\u0026nbsp;A100\u0026nbsp;PCIe\u0026nbsp;80GB | 19.49 | 80 | 1935 | 9657 (76%) | 17896\u0026nbsp;(71%) | 10817\u0026nbsp;(43%) |\n| 🟢\u0026nbsp;PG506-243\u0026nbsp;/\u0026nbsp;PG506-242 | 22.14 | 64 | 1638 | 8195 (77%) | 15654\u0026nbsp;(74%) | 12271\u0026nbsp;(58%) |\n| 🟢\u0026nbsp;A100\u0026nbsp;SXM4\u0026nbsp;40GB | 19.49 | 40 | 1555 | 8522 (84%) | 16013\u0026nbsp;(79%) | 11251\u0026nbsp;(56%) |\n| 🟢\u0026nbsp;A100\u0026nbsp;PCIe\u0026nbsp;40GB | 19.49 | 40 | 1555 | 8526 (84%) | 16035\u0026nbsp;(79%) | 11088\u0026nbsp;(55%) |\n| 🟢\u0026nbsp;CMP\u0026nbsp;170HX | 6.32 | 8 | 1493 | 7684 (79%) | 12392\u0026nbsp;(64%) | 6859 (35%) |\n| 🟢\u0026nbsp;A30 | 10.32 | 24 | 933 | 5004 (82%) | 9721 (80%) | 5726 (47%) |\n| 🟢\u0026nbsp;Tesla\u0026nbsp;V100\u0026nbsp;SXM2\u0026nbsp;32GB | 15.67 | 32 | 900 | 4471 (76%) | 8947 (77%) | 7217 (62%) |\n| 🟢\u0026nbsp;Tesla\u0026nbsp;V100\u0026nbsp;PCIe\u0026nbsp;16GB | 14.13 | 16 | 900 | 5128 (87%) | 10325\u0026nbsp;(88%) | 7683 (66%) |\n| 🟢\u0026nbsp;Quadro\u0026nbsp;GV100 | 16.66 | 32 | 870 | 3442 (61%) | 6641 (59%) | 5863 (52%) |\n| 🟢\u0026nbsp;Titan\u0026nbsp;V | 14.90 | 12 | 653 | 3601 (84%) | 7253 (86%) | 6957 (82%) |\n| 🟢\u0026nbsp;Tesla\u0026nbsp;P100\u0026nbsp;16GB | 9.52 | 16 | 732 | 3295 (69%) | 5950 (63%) | 4176 (44%) |\n| 🟢\u0026nbsp;Tesla\u0026nbsp;P100\u0026nbsp;12GB | 9.52 | 12 | 549 | 2427 (68%) | 4141 (58%) | 3999 (56%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;GTX\u0026nbsp;TITAN | 4.71 | 6 | 288 | 1460 (77%) | 2500 (67%) | 1113 (30%) |\n| 🟢\u0026nbsp;Tesla\u0026nbsp;K40m | 4.29 | 12 | 288 | 1131 (60%) | 1868 (50%) | 912 (24%) |\n| 🟢\u0026nbsp;Tesla\u0026nbsp;K80\u0026nbsp;(1\u0026nbsp;GPU) | 4.11 | 12 | 240 | 916 (58%) | 1642 (53%) | 943 (30%) |\n| 🟢\u0026nbsp;Tesla\u0026nbsp;K20c | 3.52 | 5 | 208 | 861 (63%) | 1507 (56%) | 720 (27%) |\n| | | | | | | |\n| 🔴\u0026nbsp;Radeon\u0026nbsp;RX\u0026nbsp;9070\u0026nbsp;XT | 48.66 | 16 | 640 | 3089 (74%) | 6688 (80%) | 6090 (73%) |\n| 🔴\u0026nbsp;Radeon\u0026nbsp;RX\u0026nbsp;9070 | 36.13 | 16 | 640 | 3007 (72%) | 5746 (69%) | 6019 (72%) |\n| 🔴\u0026nbsp;Radeon\u0026nbsp;RX\u0026nbsp;7900\u0026nbsp;XTX | 61.44 | 24 | 960 | 3665 (58%) | 7644 (61%) | 7716 (62%) |\n| 🔴\u0026nbsp;Radeon\u0026nbsp;PRO\u0026nbsp;W7900 | 61.30 | 48 | 864 | 3107 (55%) | 5939 (53%) | 5780 (52%) |\n| 🔴\u0026nbsp;Radeon\u0026nbsp;RX\u0026nbsp;7900\u0026nbsp;XT | 51.61 | 20 | 800 | 3013 (58%) | 5856 (56%) | 5986 (58%) |\n| 🔴\u0026nbsp;Radeon\u0026nbsp;RX\u0026nbsp;7800\u0026nbsp;XT | 37.32 | 16 | 624 | 1704 (42%) | 3105 (38%) | 3061 (38%) |\n| 🔴\u0026nbsp;Radeon\u0026nbsp;PRO\u0026nbsp;W7800 | 45.20 | 32 | 576 | 1872 (50%) | 4426 (59%) | 4145 (55%) |\n| 🔴\u0026nbsp;Radeon\u0026nbsp;RX\u0026nbsp;7900\u0026nbsp;GRE | 42.03 | 16 | 576 | 1996 (53%) | 4570 (61%) | 4463 (60%) |\n| 🔴\u0026nbsp;Radeon\u0026nbsp;PRO\u0026nbsp;W7700 | 28.30 | 16 | 576 | 1547 (41%) | 2943 (39%) | 2899 (39%) |\n| 🔴\u0026nbsp;Radeon\u0026nbsp;RX\u0026nbsp;7600 | 21.75 | 8 | 288 | 1250 (66%) | 2561 (68%) | 2512 (67%) |\n| 🔴\u0026nbsp;Radeon\u0026nbsp;PRO\u0026nbsp;W7600 | 20.00 | 8 | 288 | 1179 (63%) | 2263 (61%) | 2287 (61%) |\n| 🔴\u0026nbsp;Radeon\u0026nbsp;PRO\u0026nbsp;W7500 | 12.20 | 8 | 172 | 856 (76%) | 1630 (73%) | 1682 (75%) |\n| 🔴\u0026nbsp;Radeon\u0026nbsp;RX\u0026nbsp;6900\u0026nbsp;XT | 23.04 | 16 | 512 | 1968 (59%) | 4227 (64%) | 4207 (63%) |\n| 🔴\u0026nbsp;Radeon\u0026nbsp;RX\u0026nbsp;6800\u0026nbsp;XT | 20.74 | 16 | 512 | 2008 (60%) | 4241 (64%) | 4224 (64%) |\n| 🔴\u0026nbsp;Radeon\u0026nbsp;PRO\u0026nbsp;W6800 | 17.83 | 32 | 512 | 1620 (48%) | 3361 (51%) | 3180 (48%) |\n| 🔴\u0026nbsp;Radeon\u0026nbsp;RX\u0026nbsp;6700\u0026nbsp;XT | 13.21 | 12 | 384 | 1408 (56%) | 2883 (58%) | 2908 (58%) |\n| 🔴\u0026nbsp;Radeon\u0026nbsp;RX\u0026nbsp;6800M | 11.78 | 12 | 384 | 1439 (57%) | 3190 (64%) | 3213 (64%) |\n| 🔴\u0026nbsp;Radeon\u0026nbsp;RX\u0026nbsp;6700M | 10.60 | 10 | 320 | 1194 (57%) | 2388 (57%) | 2429 (58%) |\n| 🔴\u0026nbsp;Radeon\u0026nbsp;RX\u0026nbsp;6600 | 8.93 | 8 | 224 | 963 (66%) | 1817 (62%) | 1839 (63%) |\n| 🔴\u0026nbsp;Radeon\u0026nbsp;RX\u0026nbsp;6500\u0026nbsp;XT | 5.77 | 4 | 144 | 459 (49%) | 1011 (54%) | 1030 (55%) |\n| 🔴\u0026nbsp;Radeon\u0026nbsp;RX\u0026nbsp;5700\u0026nbsp;XT | 9.75 | 8 | 448 | 1368 (47%) | 3253 (56%) | 3049 (52%) |\n| 🔴\u0026nbsp;Radeon\u0026nbsp;RX\u0026nbsp;5700 | 7.72 | 8 | 448 | 1521 (52%) | 3167 (54%) | 2758 (47%) |\n| 🔴\u0026nbsp;Radeon\u0026nbsp;RX\u0026nbsp;5600\u0026nbsp;XT | 6.73 | 6 | 288 | 1136 (60%) | 2214 (59%) | 2148 (57%) |\n| 🔴\u0026nbsp;Radeon\u0026nbsp;RX\u0026nbsp;Vega\u0026nbsp;64 | 13.35 | 8 | 484 | 1875 (59%) | 2878 (46%) | 3227 (51%) |\n| 🔴\u0026nbsp;Radeon\u0026nbsp;RX\u0026nbsp;590 | 5.53 | 8 | 256 | 1257 (75%) | 1573 (47%) | 1688 (51%) |\n| 🔴\u0026nbsp;Radeon\u0026nbsp;RX\u0026nbsp;580\u0026nbsp;4GB | 6.50 | 4 | 256 | 946 (57%) | 1848 (56%) | 1577 (47%) |\n| 🔴\u0026nbsp;Radeon\u0026nbsp;RX\u0026nbsp;580\u0026nbsp;2048SP\u0026nbsp;8GB | 4.94 | 8 | 224 | 868 (59%) | 1622 (56%) | 1240 (43%) |\n| 🔴\u0026nbsp;Radeon\u0026nbsp;R9\u0026nbsp;390X | 5.91 | 8 | 384 | 1733 (69%) | 2217 (44%) | 1722 (35%) |\n| 🔴\u0026nbsp;Radeon\u0026nbsp;HD\u0026nbsp;7850 | 1.84 | 2 | 154 | 112 (11%) | 120 ( 6%) | 635 (32%) |\n| 🔵\u0026nbsp;Arc\u0026nbsp;B580\u0026nbsp;LE | 14.59 | 12 | 456 | 2598 (87%) | 4443 (75%) | 4979 (84%) |\n| 🔵\u0026nbsp;Arc\u0026nbsp;A770\u0026nbsp;LE | 19.66 | 16 | 560 | 2663 (73%) | 4568 (63%) | 4519 (62%) |\n| 🔵\u0026nbsp;Arc\u0026nbsp;A750\u0026nbsp;LE | 17.20 | 8 | 512 | 2555 (76%) | 4314 (65%) | 4047 (61%) |\n| 🔵\u0026nbsp;Arc\u0026nbsp;A580 | 12.29 | 8 | 512 | 2534 (76%) | 3889 (58%) | 3488 (52%) |\n| 🔵\u0026nbsp;Arc\u0026nbsp;Pro\u0026nbsp;A40 | 5.02 | 6 | 192 | 594 (47%) | 985 (40%) | 927 (37%) |\n| 🔵\u0026nbsp;Arc\u0026nbsp;A380 | 4.20 | 6 | 186 | 622 (51%) | 1097 (45%) | 1115 (46%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;RTX\u0026nbsp;5090 | 104.88 | 32 | 1792 | 9522 (81%) | 18459 (79%) | 19141 (82%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;RTX\u0026nbsp;5080 | 56.34 | 16 | 960 | 5174 (82%) | 10252 (82%) | 10304 (83%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;RTX\u0026nbsp;5070 | 30.84 | 12 | 672 | 3658 (83%) | 7238 (83%) | 7107 (81%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;RTX\u0026nbsp;4090 | 82.58 | 24 | 1008 | 5624 (85%) | 11091 (85%) | 11496 (88%) |\n| 🟢\u0026nbsp;RTX\u0026nbsp;6000\u0026nbsp;Ada | 91.10 | 48 | 960 | 4997 (80%) | 10249 (82%) | 10293 (83%) |\n| 🟢\u0026nbsp;L40S | 91.61 | 48 | 864 | 3788 (67%) | 7637 (68%) | 7617 (68%) |\n| 🟢\u0026nbsp;L40 | 90.52 | 48 | 864 | 3870 (69%) | 7778 (69%) | 7945 (71%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;RTX\u0026nbsp;4080\u0026nbsp;Super | 52.22 | 16 | 736 | 4089 (85%) | 7660 (80%) | 8218 (86%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;RTX\u0026nbsp;4080 | 55.45 | 16 | 717 | 3914 (84%) | 7626 (82%) | 7933 (85%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;RTX\u0026nbsp;4070\u0026nbsp;Ti\u0026nbsp;Super | 44.10 | 16 | 672 | 3694 (84%) | 6435 (74%) | 7295 (84%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;RTX\u0026nbsp;4090M | 28.31 | 16 | 576 | 3367 (89%) | 6545 (87%) | 6901 (92%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;RTX\u0026nbsp;4070\u0026nbsp;Super | 35.55 | 12 | 504 | 2751 (83%) | 5149 (79%) | 5554 (85%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;RTX\u0026nbsp;4070 | 29.15 | 12 | 504 | 2646 (80%) | 4548 (69%) | 5016 (77%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;RTX\u0026nbsp;4080M | 33.85 | 12 | 432 | 2577 (91%) | 5086 (91%) | 5114 (91%) |\n| 🟢\u0026nbsp;RTX\u0026nbsp;4000\u0026nbsp;Ada | 26.73 | 20 | 360 | 2130 (91%) | 3964 (85%) | 4221 (90%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;RTX\u0026nbsp;4060 | 15.11 | 8 | 272 | 1614 (91%) | 3052 (86%) | 3124 (88%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;RTX\u0026nbsp;4070M | 18.25 | 8 | 256 | 1553 (93%) | 2945 (89%) | 3092 (93%) |\n| 🟢\u0026nbsp;RTX\u0026nbsp;2000\u0026nbsp;Ada | 12.00 | 16 | 224 | 1351 (92%) | 2452 (84%) | 2526 (87%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;RTX\u0026nbsp;3090\u0026nbsp;Ti | 40.00 | 24 | 1008 | 5717 (87%) | 10956 (84%) | 10400 (79%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;RTX\u0026nbsp;3090 | 39.05 | 24 | 936 | 5418 (89%) | 10732 (88%) | 10215 (84%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;RTX\u0026nbsp;3080\u0026nbsp;Ti | 37.17 | 12 | 912 | 5202 (87%) | 9832 (87%) | 9347 (79%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;RTX\u0026nbsp;3080\u0026nbsp;12GB | 32.26 | 12 | 912 | 5071 (85%) | 9657 (81%) | 8615 (73%) |\n| 🟢\u0026nbsp;RTX\u0026nbsp;A6000 | 40.00 | 48 | 768 | 4421 (88%) | 8814 (88%) | 8533 (86%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;RTX\u0026nbsp;3080\u0026nbsp;10GB | 29.77 | 10 | 760 | 4230 (85%) | 8118 (82%) | 7714 (78%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;RTX\u0026nbsp;3070\u0026nbsp;Ti | 21.75 | 8 | 608 | 3490 (88%) | 6807 (86%) | 5926 (75%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;RTX\u0026nbsp;3080M\u0026nbsp;Ti | 23.61 | 16 | 512 | 2985 (89%) | 5908 (89%) | 5780 (87%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;RTX\u0026nbsp;3070 | 20.31 | 8 | 448 | 2578 (88%) | 5096 (88%) | 5060 (87%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;RTX\u0026nbsp;3060\u0026nbsp;Ti | 16.49 | 8 | 448 | 2644 (90%) | 5129 (88%) | 4718 (81%) |\n| 🟢\u0026nbsp;RTX\u0026nbsp;A4000 | 19.17 | 16 | 448 | 2500 (85%) | 4945 (85%) | 4664 (80%) |\n| 🟢\u0026nbsp;RTX\u0026nbsp;A5000M | 16.59 | 16 | 448 | 2228 (76%) | 4461 (77%) | 3662 (63%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;RTX\u0026nbsp;3060 | 13.17 | 12 | 360 | 2108 (90%) | 4070 (87%) | 3566 (76%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;RTX\u0026nbsp;3060M | 10.94 | 6 | 336 | 2019 (92%) | 4012 (92%) | 3572 (82%) |\n| 🟢\u0026nbsp;A2 | 4.53 | 15 | 200 | 1031 (79%) | 2051 (79%) | 1199 (46%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;RTX\u0026nbsp;3050M\u0026nbsp;Ti | 7.60 | 4 | 192 | 1181 (94%) | 2341 (94%) | 2253 (90%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;RTX\u0026nbsp;3050M | 7.13 | 4 | 192 | 1180 (94%) | 2339 (94%) | 2016 (81%) |\n| 🟢\u0026nbsp;Titan\u0026nbsp;RTX | 16.31 | 24 | 672 | 3471 (79%) | 7456 (85%) | 7554 (87%) |\n| 🟢\u0026nbsp;Quadro\u0026nbsp;RTX\u0026nbsp;6000 | 16.31 | 24 | 672 | 3307 (75%) | 6836 (78%) | 6879 (79%) |\n| 🟢\u0026nbsp;Quadro\u0026nbsp;RTX\u0026nbsp;8000\u0026nbsp;Passive | 14.93 | 48 | 624 | 2591 (64%) | 5408 (67%) | 5607 (69%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;RTX\u0026nbsp;2080\u0026nbsp;Ti | 13.45 | 11 | 616 | 3194 (79%) | 6700 (84%) | 6853 (86%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;RTX\u0026nbsp;2080\u0026nbsp;Super | 11.34 | 8 | 496 | 2434 (75%) | 5284 (82%) | 5087 (79%) |\n| 🟢\u0026nbsp;Quadro\u0026nbsp;RTX\u0026nbsp;5000 | 11.15 | 16 | 448 | 2341 (80%) | 4766 (82%) | 4773 (82%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;RTX\u0026nbsp;2080 | 10.07 | 8 | 448 | 2318 (79%) | 4977 (86%) | 4963 (85%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;RTX\u0026nbsp;2070\u0026nbsp;Super | 9.22 | 8 | 448 | 2255 (77%) | 4866 (84%) | 4893 (84%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;RTX\u0026nbsp;2070 | 7.47 | 8 | 448 | 2444 (83%) | 4387 (75%) | 5017 (86%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;RTX\u0026nbsp;2060\u0026nbsp;Super | 7.18 | 8 | 448 | 2503 (85%) | 5035 (87%) | 4463 (77%) |\n| 🟢\u0026nbsp;Quadro\u0026nbsp;RTX\u0026nbsp;4000 | 7.12 | 8 | 416 | 2284 (84%) | 4584 (85%) | 4062 (75%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;RTX\u0026nbsp;2060\u0026nbsp;KO | 6.74 | 6 | 336 | 1643 (75%) | 3376 (77%) | 3266 (75%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;RTX\u0026nbsp;2060 | 6.74 | 6 | 336 | 1681 (77%) | 3604 (83%) | 3571 (82%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;GTX\u0026nbsp;1660\u0026nbsp;Super | 5.03 | 6 | 336 | 1696 (77%) | 3551 (81%) | 3040 (70%) |\n| 🟢\u0026nbsp;Tesla\u0026nbsp;T4 | 8.14 | 15 | 300 | 1356 (69%) | 2869 (74%) | 2887 (74%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;GTX\u0026nbsp;1660\u0026nbsp;Ti | 5.48 | 6 | 288 | 1467 (78%) | 3041 (81%) | 3019 (81%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;GTX\u0026nbsp;1660 | 5.07 | 6 | 192 | 1016 (81%) | 1924 (77%) | 1992 (80%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;GTX\u0026nbsp;1650M\u0026nbsp;896C | 2.72 | 4 | 192 | 963 (77%) | 1836 (74%) | 1858 (75%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;GTX\u0026nbsp;1650M\u0026nbsp;1024C | 3.20 | 4 | 128 | 706 (84%) | 1214 (73%) | 1400 (84%) |\n| 🟢\u0026nbsp;T500 | 3.04 | 4 | 80 | 339 (65%) | 578 (56%) | 665 (64%) |\n| 🟢\u0026nbsp;Titan\u0026nbsp;Xp | 12.15 | 12 | 548 | 2919 (82%) | 5495 (77%) | 5375 (76%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;GTX\u0026nbsp;1080\u0026nbsp;Ti | 12.06 | 11 | 484 | 2631 (83%) | 4837 (77%) | 4877 (78%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;GTX\u0026nbsp;1080 | 9.78 | 8 | 320 | 1623 (78%) | 3100 (75%) | 3182 (77%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;GTX\u0026nbsp;1060\u0026nbsp;6GB | 4.57 | 6 | 192 | 997 (79%) | 1925 (77%) | 1785 (72%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;GTX\u0026nbsp;1060M | 4.44 | 6 | 192 | 983 (78%) | 1882 (75%) | 1803 (72%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;GTX\u0026nbsp;1050M Ti | 2.49 | 4 | 112 | 631 (86%) | 1224 (84%) | 1115 (77%) |\n| 🟢\u0026nbsp;Quadro\u0026nbsp;P1000 | 1.89 | 4 | 82 | 426 (79%) | 839 (79%) | 778 (73%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;GTX\u0026nbsp;980\u0026nbsp;Ti | 6.05 | 6 | 336 | 1509 (69%) | 2703 (62%) | 2381 (55%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;GTX\u0026nbsp;980 | 4.98 | 4 | 224 | 1018 (70%) | 1965 (68%) | 1872 (64%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;GTX\u0026nbsp;970 | 4.17 | 4 | 224 | 980 (67%) | 1721 (59%) | 1623 (56%) |\n| 🟢\u0026nbsp;Quadro\u0026nbsp;M4000 | 2.57 | 8 | 192 | 899 (72%) | 1519 (61%) | 1050 (42%) |\n| 🟢\u0026nbsp;Tesla\u0026nbsp;M60\u0026nbsp;(1\u0026nbsp;GPU) | 4.82 | 8 | 160 | 853 (82%) | 1571 (76%) | 1557 (75%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;GTX\u0026nbsp;960M | 1.51 | 4 | 80 | 442 (84%) | 872 (84%) | 627 (60%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;GTX\u0026nbsp;770 | 3.33 | 2 | 224 | 800 (55%) | 1215 (42%) | 876 (30%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;GTX\u0026nbsp;680\u0026nbsp;4GB | 3.33 | 4 | 192 | 783 (62%) | 1274 (51%) | 814 (33%) |\n| 🟢\u0026nbsp;Quadro\u0026nbsp;K2000 | 0.73 | 2 | 64 | 312 (75%) | 444 (53%) | 171 (21%) |\n| 🟢\u0026nbsp;GeForce\u0026nbsp;GT\u0026nbsp;630\u0026nbsp;(OEM) | 0.46 | 2 | 29 | 151 (81%) | 185 (50%) | 78 (21%) |\n| 🟢\u0026nbsp;Quadro\u0026nbsp;NVS\u0026nbsp;290 | 0.03 | 1/4 | 6 | 9 (22%) | 4 ( 5%) | 4 ( 5%) |\n| 🟤\u0026nbsp;Arise\u0026nbsp;1020 | 1.50 | 2 | 19 | 6 ( 5%) | 6 ( 2%) | 6 ( 2%) |\n| | | | | | | |\n| ⚪\u0026nbsp;M2\u0026nbsp;Ultra\u0026nbsp;GPU\u0026nbsp;76CU\u0026nbsp;192GB | 19.46 | 147 | 800 | 4629 (89%) | 8769 (84%) | 7972 (77%) |\n| ⚪\u0026nbsp;M2\u0026nbsp;Max\u0026nbsp;GPU\u0026nbsp;38CU\u0026nbsp;32GB | 9.73 | 22 | 400 | 2405 (92%) | 4641 (89%) | 2444 (47%) |\n| ⚪\u0026nbsp;M1\u0026nbsp;Ultra\u0026nbsp;GPU\u0026nbsp;64CU\u0026nbsp;128GB | 16.38 | 98 | 800 | 4519 (86%) | 8418 (81%) | 6915 (67%) |\n| ⚪\u0026nbsp;M1\u0026nbsp;Max\u0026nbsp;GPU\u0026nbsp;24CU\u0026nbsp;32GB | 6.14 | 22 | 400 | 2369 (91%) | 4496 (87%) | 2777 (53%) |\n| ⚪\u0026nbsp;M1\u0026nbsp;Pro\u0026nbsp;GPU\u0026nbsp;16CU\u0026nbsp;16GB | 4.10 | 11 | 200 | 1204 (92%) | 2329 (90%) | 1855 (71%) |\n| ⚪\u0026nbsp;M1\u0026nbsp;GPU\u0026nbsp;8CU\u0026nbsp;16GB | 2.05 | 11 | 68 | 384 (86%) | 758 (85%) | 759 (86%) |\n| 🔴\u0026nbsp;Radeon\u0026nbsp;8060S\u0026nbsp;Graphics\u0026nbsp;(Max+\u0026nbsp;395)) | 29.70 | 15 | 256 | 1231 (74%) | 2541 (76%) | 2563 (77%) |\n| 🔴\u0026nbsp;Radeon\u0026nbsp;780M\u0026nbsp;(Z1\u0026nbsp;Extreme) | 8.29 | 8 | 102 | 443 (66%) | 860 (65%) | 820 (62%) |\n| 🔴\u0026nbsp;Radeon\u0026nbsp;Graphics\u0026nbsp;(7800X3D) | 0.56 | 12 | 102 | 338 (51%) | 498 (37%) | 283 (21%) |\n| 🔴\u0026nbsp;Radeon\u0026nbsp;Vega\u0026nbsp;8\u0026nbsp;(4750G) | 2.15 | 27 | 57 | 263 (71%) | 511 (70%) | 501 (68%) |\n| 🔴\u0026nbsp;Radeon\u0026nbsp;Vega\u0026nbsp;8\u0026nbsp;(3500U) | 1.23 | 7 | 38 | 157 (63%) | 282 (57%) | 288 (58%) |\n| 🔵\u0026nbsp;Arc\u0026nbsp;140V\u0026nbsp;GPU\u0026nbsp;(16GB) | 3.99 | 16 | 137 | 636 (71%) | 1282 (72%) | 773 (44%) |\n| 🔵\u0026nbsp;Arc\u0026nbsp;Graphics\u0026nbsp;(Ultra\u0026nbsp;9\u0026nbsp;185H) | 4.81 | 14 | 90 | 271 (46%) | 710 (61%) | 724 (62%) |\n| 🔵\u0026nbsp;Iris\u0026nbsp;Xe\u0026nbsp;Graphics\u0026nbsp;(i7-1265U) | 1.92 | 13 | 77 | 342 (68%) | 621 (62%) | 574 (58%) |\n| 🔵\u0026nbsp;UHD\u0026nbsp;Graphics\u0026nbsp;Xe\u0026nbsp;32EUs | 0.74 | 25 | 51 | 128 (38%) | 245 (37%) | 216 (32%) |\n| 🔵\u0026nbsp;UHD\u0026nbsp;Graphics\u0026nbsp;770 | 0.82 | 30 | 90 | 342 (58%) | 475 (41%) | 278 (24%) |\n| 🔵\u0026nbsp;UHD\u0026nbsp;Graphics\u0026nbsp;630 | 0.46 | 7 | 51 | 151 (45%) | 301 (45%) | 187 (28%) |\n| 🔵\u0026nbsp;UHD\u0026nbsp;Graphics\u0026nbsp;P630 | 0.46 | 51 | 42 | 177 (65%) | 288 (53%) | 137 (25%) |\n| 🔵\u0026nbsp;HD\u0026nbsp;Graphics\u0026nbsp;5500 | 0.35 | 3 | 26 | 75 (45%) | 192 (58%) | 108 (32%) |\n| 🔵\u0026nbsp;HD\u0026nbsp;Graphics\u0026nbsp;4600 | 0.38 | 2 | 26 | 105 (63%) | 115 (35%) | 34 (10%) |\n| 🟡\u0026nbsp;Mali-G610\u0026nbsp;MP4 (Orange\u0026nbsp;Pi\u0026nbsp;5) | 0.06 | 16 | 34 | 130 (58%) | 232 (52%) | 93 (21%) |\n| 🟡\u0026nbsp;Mali-G72\u0026nbsp;MP18 (Samsung\u0026nbsp;S9+) | 0.24 | 4 | 29 | 110 (59%) | 230 (62%) | 21 ( 6%) |\n| | | | | | | |\n| 🔴\u0026nbsp;2x\u0026nbsp;EPYC\u0026nbsp;9754 | 50.79 | 3072 | 922 | 3276 (54%) | 5077 (42%) | 5179 (43%) |\n| 🔴\u0026nbsp;2x\u0026nbsp;EPYC\u0026nbsp;9654 | 43.62 | 1536 | 922 | 1381 (23%) | 1814 (15%) | 1801 (15%) |\n| 🔴\u0026nbsp;2x\u0026nbsp;EPYC\u0026nbsp;9554 | 30.72 | 384 | 922 | 2552 (42%) | 2127 (18%) | 2144 (18%) |\n| 🔴\u0026nbsp;1x\u0026nbsp;EPYC\u0026nbsp;9124 | 3.69 | 128 | 307 | 772 (38%) | 579 (15%) | 586 (15%) |\n| 🔴\u0026nbsp;2x\u0026nbsp;EPYC\u0026nbsp;7713 | 8.19 | 512 | 410 | 1298 (48%) | 492 ( 9%) | 1418 (27%) |\n| 🔴\u0026nbsp;2x\u0026nbsp;EPYC\u0026nbsp;7352 | 3.53 | 512 | 410 | 739 (28%) | 106 ( 2%) | 412 ( 8%) |\n| 🔴\u0026nbsp;2x\u0026nbsp;EPYC\u0026nbsp;7313 | 3.07 | 128 | 410 | 498 (19%) | 367 ( 7%) | 418 ( 8%) |\n| 🔴\u0026nbsp;2x\u0026nbsp;EPYC\u0026nbsp;7302 | 3.07 | 128 | 410 | 784 (29%) | 336 ( 6%) | 411 ( 8%) |\n| 🔵\u0026nbsp;2x\u0026nbsp;Xeon\u0026nbsp;6980P | 98.30 | 6144 | 1690 | 7875 (71%) | 5112 (23%) | 5610 (26%) |\n| 🔵\u0026nbsp;2x\u0026nbsp;Xeon\u0026nbsp;6979P | 92.16 | 3072 | 1690 | 8135 (74%) | 4175 (19%) | 4622 (21%) |\n| 🔵\u0026nbsp;2x\u0026nbsp;Xeon\u0026nbsp;Platinum\u0026nbsp;8592+ | 31.13 | 1024 | 717 | 3135 (67%) | 2359 (25%) | 2466 (26%) |\n| 🔵\u0026nbsp;2x\u0026nbsp;Xeon\u0026nbsp;Gold\u0026nbsp;6548N | 22.94 | 2048 | 666 | 1811 (42%) | 1388 (16%) | 1425 (16%) |\n| 🔵\u0026nbsp;2x\u0026nbsp;Xeon\u0026nbsp;CPU\u0026nbsp;Max\u0026nbsp;9480 | 27.24 | 256 | 614 | 2037 (51%) | 1520 (19%) | 1464 (18%) |\n| 🔵\u0026nbsp;2x\u0026nbsp;Xeon\u0026nbsp;Platinum\u0026nbsp;8480+ | 28.67 | 512 | 614 | 2162 (54%) | 1845 (23%) | 1884 (24%) |\n| 🔵\u0026nbsp;2x\u0026nbsp;Xeon\u0026nbsp;Platinum\u0026nbsp;8470 | 25.29 | 2048 | 614 | 1865 (46%) | 1909 (24%) | 2068 (26%) |\n| 🔵\u0026nbsp;2x\u0026nbsp;Xeon\u0026nbsp;Gold\u0026nbsp;6438Y+ | 16.38 | 1024 | 614 | 1945 (48%) | 1219 (15%) | 1257 (16%) |\n| 🔵\u0026nbsp;2x\u0026nbsp;Xeon\u0026nbsp;Platinum\u0026nbsp;8380 | 23.55 | 2048 | 410 | 1410 (53%) | 1159 (22%) | 1298 (24%) |\n| 🔵\u0026nbsp;2x\u0026nbsp;Xeon\u0026nbsp;Platinum\u0026nbsp;8358 | 21.30 | 256 | 410 | 1285 (48%) | 1007 (19%) | 1120 (21%) |\n| 🔵\u0026nbsp;2x\u0026nbsp;Xeon\u0026nbsp;Platinum\u0026nbsp;8256 | 3.89 | 1536 | 282 | 396 (22%) | 158 ( 4%) | 175 ( 5%) |\n| 🔵\u0026nbsp;2x\u0026nbsp;Xeon\u0026nbsp;Platinum\u0026nbsp;8153 | 8.19 | 384 | 256 | 691 (41%) | 290 ( 9%) | 328 (10%) |\n| 🔵\u0026nbsp;2x\u0026nbsp;Xeon\u0026nbsp;Gold\u0026nbsp;6248R | 18.43 | 384 | 282 | 755 (41%) | 566 (15%) | 694 (19%) |\n| 🔵\u0026nbsp;2x\u0026nbsp;Xeon\u0026nbsp;Gold\u0026nbsp;6128 | 5.22 | 192 | 256 | 254 (15%) | 185 ( 6%) | 193 ( 6%) |\n| 🔵\u0026nbsp;Xeon\u0026nbsp;Phi\u0026nbsp;7210 | 5.32 | 192 | 102 | 415 (62%) | 193 (15%) | 223 (17%) |\n| 🔵\u0026nbsp;4x\u0026nbsp;Xeon\u0026nbsp;E5-4620\u0026nbsp;v4 | 2.69 | 512 | 273 | 460 (26%) | 275 ( 8%) | 239 ( 7%) |\n| 🔵\u0026nbsp;2x\u0026nbsp;Xeon\u0026nbsp;E5-2630\u0026nbsp;v4 | 1.41 | 64 | 137 | 264 (30%) | 146 ( 8%) | 129 ( 7%) |\n| 🔵\u0026nbsp;2x\u0026nbsp;Xeon\u0026nbsp;E5-2623\u0026nbsp;v4 | 0.67 | 64 | 137 | 125 (14%) | 66 ( 4%) | 59 ( 3%) |\n| 🔵\u0026nbsp;2x\u0026nbsp;Xeon\u0026nbsp;E5-2680\u0026nbsp;v3 | 1.92 | 128 | 137 | 304 (34%) | 234 (13%) | 291 (16%) |\n| 🟢\u0026nbsp;GH200\u0026nbsp;Neoverse-V2\u0026nbsp;CPU | 7.88 | 480 | 384 | 1323 (53%) | 853 (17%) | 683 (14%) |\n| 🔴\u0026nbsp;Threadripper\u0026nbsp;PRO\u0026nbsp;7995WX | 15.36 | 256 | 333 | 1134 (52%) | 1697 (39%) | 1715 (40%) |\n| 🔴\u0026nbsp;Threadripper\u0026nbsp;3970X | 3.79 | 128 | 102 | 376 (56%) | 103 ( 8%) | 463 (35%) |\n| 🔴\u0026nbsp;Threadripper\u0026nbsp;1950X | 0.87 | 128 | 85 | 273 (49%) | 43 ( 4%) | 151 (14%) |\n| 🔴\u0026nbsp;Ryzen\u0026nbsp;9\u0026nbsp;7900X3D | 1.69 | 128 | 83 | 278 (51%) | 521 (48%) | 462 (43%) |\n| 🔴\u0026nbsp;Ryzen\u0026nbsp;7\u0026nbsp;7800X3D | 1.08 | 32 | 102 | 296 (44%) | 361 (27%) | 363 (27%) |\n| 🔴\u0026nbsp;Ryzen\u0026nbsp;7\u0026nbsp;5700X3D | 0.87 | 32 | 51 | 229 (68%) | 135 (20%) | 173 (26%) |\n| 🔴\u0026nbsp;FX-6100 | 0.16 | 16 | 26 | 11 ( 7%) | 11 ( 3%) | 22 ( 7%) |\n| 🔴\u0026nbsp;Athlon\u0026nbsp;X2\u0026nbsp;QL-65 | 0.03 | 4 | 11 | 3 ( 4%) | 2 ( 2%) | 3 ( 2%) |\n| 🔵\u0026nbsp;Core\u0026nbsp;Ultra\u0026nbsp;7\u0026nbsp;258V | 0.56 | 32 | 137 | 287 (32%) | 123 ( 7%) | 167 ( 9%) |\n| 🔵\u0026nbsp;Core\u0026nbsp;Ultra\u0026nbsp;9\u0026nbsp;185H | 1.79 | 16 | 90 | 317 (54%) | 267 (23%) | 288 (25%) |\n| 🔵\u0026nbsp;Core\u0026nbsp;i9-14900K | 3.74 | 32 | 96 | 443 (71%) | 453 (36%) | 490 (39%) |\n| 🔵\u0026nbsp;Core\u0026nbsp;i7-13700K | 2.51 | 64 | 90 | 504 (86%) | 398 (34%) | 424 (36%) |\n| 🔵\u0026nbsp;Core\u0026nbsp;i7-1265U | 1.23 | 32 | 77 | 128 (26%) | 62 ( 6%) | 58 ( 6%) |\n| 🔵\u0026nbsp;Core\u0026nbsp;i9-11900KB | 0.84 | 32 | 51 | 109 (33%) | 195 (29%) | 208 (31%) |\n| 🔵\u0026nbsp;Core\u0026nbsp;i9-10980XE | 3.23 | 128 | 94 | 286 (47%) | 251 (21%) | 223 (18%) |\n| 🔵\u0026nbsp;Xeon\u0026nbsp;E-2288G | 0.95 | 32 | 43 | 196 (70%) | 182 (33%) | 198 (36%) |\n| 🔵\u0026nbsp;Core\u0026nbsp;i7-9700 | 0.77 | 64 | 43 | 103 (37%) | 62 (11%) | 95 (17%) |\n| 🔵\u0026nbsp;Core\u0026nbsp;i5-9600 | 0.60 | 16 | 43 | 146 (52%) | 127 (23%) | 147 (27%) |\n| 🔵\u0026nbsp;Core\u0026nbsp;i7-8700K | 0.71 | 16 | 51 | 152 (45%) | 134 (20%) | 116 (17%) |\n| 🔵\u0026nbsp;Xeon\u0026nbsp;E-2176G | 0.71 | 64 | 42 | 201 (74%) | 136 (25%) | 148 (27%) |\n| 🔵\u0026nbsp;Core\u0026nbsp;i7-7700HQ | 0.36 | 12 | 38 | 81 (32%) | 82 (16%) | 108 (22%) |\n| 🔵\u0026nbsp;Xeon\u0026nbsp;E3-1240\u0026nbsp;v5 | 0.50 | 32 | 34 | 141 (63%) | 75 (17%) | 88 (20%) |\n| 🔵\u0026nbsp;Core\u0026nbsp;i7-4770 | 0.44 | 16 | 26 | 104 (62%) | 69 (21%) | 59 (18%) |\n| 🔵\u0026nbsp;Core\u0026nbsp;i7-4720HQ | 0.33 | 16 | 26 | 80 (48%) | 23 ( 7%) | 60 (18%) |\n| 🔵\u0026nbsp;Celeron\u0026nbsp;N2807 | 0.01 | 4 | 11 | 7 (10%) | 3 ( 2%) | 3 ( 2%) |\n\n\u003c/details\u003e\n\n\n\n## Multi-GPU Benchmarks\n\nMulti-GPU benchmarks are done at the largest possible grid resolution with cubic domains, and either 2x1x1, 2x2x1 or 2x2x2 of these domains together. The (percentages in round brackets) are single-GPU [roofline model](https://en.wikipedia.org/wiki/Roofline_model) efficiency, and the (multiplicators in round brackets) are scaling factors relative to benchmarked single-GPU performance.\n\n```mermaid\ngantt\n\ntitle FluidX3D Performance [MLUPs/s] - FP32 arithmetic, (fastest of FP32/FP16S/FP16C) memory storage\ndateFormat X\naxisFormat %s\n%%{\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fprojectphysx%2Ffluidx3d","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fprojectphysx%2Ffluidx3d","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fprojectphysx%2Ffluidx3d/lists"}