indeed the network effect, so nvidia comes out with cagra search, then i with a nvidia card decide to use cuvs for that then think hmm i can make even faster by fusing some of my existing cuda embedding model with cuvs so i will rewrite my own cuda for that (me and claude made lee101/gobed a cuda enabled search engine github.com/lee101/gobed ) i dont have a amd gpu, not many other deep learning researchers do and so i dont care about amd support. its only really for me and my cuda machines. This is replaying like many thousands of times with various open source researchers building ontop of other cuda libraries on their cuda machines eg all these: AI Dump of fun cuda words: cuBLASGPU-accelerated Basic Linear Algebra Subprograms (matrix/vector ops).cuSPARSESparse matrix operations (CSR/COO formats).cuSOLVERDense and sparse linear solvers (LU, QR, Cholesky, eigenvalues).cuFFTFast Fourier Transforms on GPU.cuRANDRandom number generation on GPU.cuTENSORHigh-performance tensor algebra (Einstein summation, contractions).cuDNNDeep Neural Network primitives (convolutions, RNNs, activations, etc).cuBLASLt"Lightweight" version of cuBLAS with advanced heuristics and mixed precision.cuSPARSELtSparse matrix acceleration for deep learning inference. ⚙️ Systems & Runtime Libraries LibraryPurposeCUDA RuntimeHigh-level host/device management layer (kernel launches, memory copies).CUDA Driver APILower-level control of GPU contexts and execution.NCCL (NVIDIA Collective Communication Library)Multi-GPU / multi-node collective ops (all-reduce, broadcast, etc).NVTX (NVIDIA Tools Extension)Instrumentation markers for profiling with Nsight tools.NVML (NVIDIA Management Library)GPU monitoring, thermals, utilization, power management.NVRTCRuntime compilation of CUDA kernels (JIT).NPP (NVIDIA Performance Primitives)Image, video, and signal processing primitives.ThrustC++ STL-like parallel algorithms library (map/reduce/sort/etc). 🧩 Domain-Specific CUDA SDKs DomainLibraries / SDKsAI / MLcuDNN, cuTENSOR, TensorRT, cuSPARSELt, cuBLASLtData AnalyticsRAPIDS (cuDF, cuML, cuGraph, cuSpatial), NVTabular, DALIComputer VisionNPP, VPI (Vision Programming Interface), CV-CUDA3D / Simulation / PhysicsCUDA PhysX, Flex, Omniverse Kit SDKVideo / ImagingNVENC, NVDEC, NPP, DeepStream SDKRendering / GraphicsOptiX (ray tracing), IndeX (volume visualization), RTXGINetworking / HPCNCCL, Magnum IO, GPUDirect RDMA, UCX, NVSHMEMAutonomous MachinesJetPack SDK, DriveWorks SDK, Isaac SDK 🧬 Bioinformatics / Scientific (your “BioCUDA” mention) Library / ProjectDescriptionnvBIONVIDIA bioinformatics library for DNA/RNA sequence alignment, assembly, etc.cuQuantumGPU-accelerated quantum simulation (state vector + tensor network).cuDF / cuML (RAPIDS)Can be applied in bioinformatics for data science workflows.Clara ParabricksGPU-accelerated genomics pipeline (variant calling, alignment).BioCUDA (community term)Often refers to custom CUDA kernels for genomics / molecular dynamics.AMBER / GROMACS / NAMD GPU buildsMolecular dynamics engines using CUDA backend. 🧮 High-Level Ecosystems EcosystemComponentsRAPIDScuDF (DataFrame), cuML (ML), cuGraph, cuSpatial, cuCIM (imaging).TensorRTInference optimization and deployment engine (on top of cuDNN + cuBLAS).DeepStreamVideo analytics framework for AI + IoT.OmniverseRTX-based simulation + rendering platform using CUDA + OptiX + MDL.Magnum IOSuite for multi-GPU and multi-node data transfer (NCCL, NVSHMEM, UCX, GPUDirect). 🔧 Developer / Tooling Stack ToolDescriptionNsight SystemsSystem-wide performance analysis.Nsight ComputeKernel-level profiler.Nsight GraphicsGraphics debugging/profiling.CUDA-GDBCUDA debugger.CUDA-MEMCHECKMemory error detection tool. 💡 Other Specialized Libraries LibraryAreaCUTLASSTemplate library for building custom GEMMs (used by PyTorch).cuDLADeep Learning Accelerator runtime (for Jetson).NVInfer / TensorRTNeural network inference optimization.CV-CUDAOpen-source CV primitives for cloud inference.cuQuantum / cuStateVec / cuTensorNetQuantum simulation primitives.cuPHY5G baseband physical layer acceleration.