====== Nvidia GPU based toolkit ====== * Nvidia developer program: https://developer.nvidia.com/ * its AI models: https://catalog.ngc.nvidia.com/models * RIVA is their language process framework * Maxine is video-audio translator and recomposer to video chat * Merlin is recommed system, like shop, video recommed * it also has computer vision related: Image segmentation models * ai related: nvidia AI accelerated program * Digital Twins, like real world twins, it is a exact duplicate model for testing when the main physical copy is not accessible. * in addition, digital twins also updates with the main physical copy, anythings happen to main one, the digital twin one update to the same. * nivida omniverse is the tool to simulate the real world, to build digital twins, * its tutorials: https://docs.omniverse.nvidia.com/plat_omniverse/common/video-list.html * it bridge realtime collaboration between different users and different graphic softwares. * omniverse audio2face: to generate face animation from audio * nvidia OVX server provide hardware support to build larget scale digital twins * omniverse system: digital twins+ robotics; design +content creation; integration; rendering; sensors; asset lib; * AI: drive, ISAAC (for move+manipulate stuff), metropolis (auto infrastructure), holoscan (robotic medical) * replicator: generate + train synthetic data for train+test AI model * omnigraph, behavior, animation: run data center scale 3d application * avatar (wip): build digital humans * nvidia open source Material Definition Language (MDL): https://developer.nvidia.com/rendering-technologies/mdl-sdk * https://developer.nvidia.com/rendering-technologies/mdl-sdk * tut: https://www.nvidia.com/en-us/on-demand/session/gtcspring22-se2310/?playlistId=playList-5168fd54-82d2-4179-a612-491b68322489 * tut: https://www.nvidia.com/en-us/on-demand/session/gtcspring22-s41207/?playlistId=playList-5168fd54-82d2-4179-a612-491b68322489 ====== Nvidia MDL ====== * to define physically based material * store specification for material exchange * render-algorithm agnostic * designed for high performance on GPU ====== Nvidia Cuda programming ====== * video info: How CUDA Programming Works * https://www.nvidia.com/en-us/on-demand/session/gtcspring22-s41487/?playlistId=playList-87118008-d10b-42f9-8c57-a50bbf006662 * the cube programming is designed based on how the GPU hardware works, and GPU also designed how GPU normally programms for best performance * Cuda programming is: * each thread has its thread ID, that its id determine which block of data it works on, and all threads finish the data together at the same data. * optimize how code use memory can be important to fit more thing in the fixed size memory by better arrangement and swapping thing in memory blocks. ====== CV-Cuda ====== * CV cuda: computer vision with cuda. ====== Nivdia RTX stack ====== * 1st Gen: VkRay, DXR, DLSS1 * 2nd Gen: * real-time denoise: spatial denoise * caustics * RTXDI: raytrace direct illumination, casting shadows from all lights, emissive surface * RTXGI: real-time multiple bounce indirect lighting * Reflex * DLSS2: deep learning super resolution, AI generat pixel * 3rd Gen: * Displaced micro-meshes * 2D SGM optical flow, shader execution reordering, real-time path tracing, opacity micro-maps, * DLSS3: deep learning super resolution, AI frame generator ====== Nvidia GPU architecture ====== | core ^ turing ^ ampere ^ ada | | shader | 16 | 40 | 90 | | RT | 49 | 78 | 200 | | tensor | 130 | 320 | 1400 | | OFA | | 126 | 300 | ====== Nvidia for AI ====== * large language model: enable single model to do various different task with one single model, context aware output. like text related, image related. * NeMo LLM service, Prompt learning framework, to promp learn with pre-trained LLM for specific task. * recommed system: like in shopping, social network