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NVIDIA CUDA parallel computing platform — use when writing .cu kernels, using cuBLAS/cuDNN/cuFFT/cuSPARSE/cuRAND/cuSolver, Thrust, or Cooperative Groups for GPU-accelerated computing
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
GreyCat C API and GCL Standard Library reference. Use for: (1) Native C development with gc_machine_t context, tensors, objects, memory management, crypto, I/O; (2) GCL Standard Library modules - std::core (Date/Time/Tuple/geospatial types), std::runtime (Scheduler/Task/Logger/Identity/Security/System/License/OpenAPI/MCP), std::io (CSV/JSON/XML/HTTP/Email/FileWalker/S3), std::util (Queue/Stack/SlidingWindow/TimeWindow/Gaussian/Histogram/Quantizers/Random/Uuid/Crypto); (3) Plugin development patterns - lifecycle hooks, type configuration, nativegen, module-level and type-level function linking, global state, thread safety, conditional logging. Keywords: GreyCat, GCL, native functions, tensors, task automation, scheduler, plugin development.
Build, run, and edit GreyCat projects. GreyCat is a statically-typed language plus runtime for graph-persistent, time-series-aware applications. Use when reading or writing `.gcl` source, when the user mentions GreyCat / project.gcl / nodeTime / nodeList / nodeIndex / nodeGeo / @expose / @library, or when the task involves running `greycat <command>`, deploying a project, or reasoning about gcdata/, lib/, files/, webroot/.
Complete llama.cpp C/C++ API reference covering model loading, inference, text generation, embeddings, chat, tokenization, sampling, batching, KV cache, LoRA adapters, and state management. Triggers on: llama.cpp questions, LLM inference code, GGUF models, local AI/ML inference, C/C++ LLM integration, "how do I use llama.cpp", API function lookups, implementation questions, troubleshooting llama.cpp issues, and any llama-cpp or ggerganov/llama.cpp mentions.
Complete vLLM v0.19.0 Python API reference for high-throughput LLM inference: offline batch generation, chat, embeddings, classification, structured outputs, LoRA adapters, multimodal inputs, and OpenAI-compatible server. Triggers on: vLLM questions, Python LLM serving, GPU inference, "how do I use vllm", batch inference, vllm serve, OpenAI-compatible API, structured JSON output, LoRA serving.
C tensor computation library for ML inference and training. Use when working with ggml graphs, GGUF model files, backend scheduling, quantization, or implementing low-level ML ops in C/C++.
Run and manage local LLMs via Ollama REST API — text generation, chat completions, embeddings, tool calling, structured output, and model management. Use when code imports ollama, references localhost:11434, or user asks about local LLM inference.
SOC 職業分類に基づく
| name | cuda |
| description | NVIDIA CUDA parallel computing platform — use when writing .cu kernels, using cuBLAS/cuDNN/cuFFT/cuSPARSE/cuRAND/cuSolver, Thrust, or Cooperative Groups for GPU-accelerated computing |
CUDA is NVIDIA's parallel computing platform and programming model for GPU-accelerated applications. It provides direct access to the GPU's virtual instruction set and parallel compute elements for executing kernels in C, C++, and Fortran.
cuda-samples version: v13.3 (CUDA Toolkit 13.3) CUDALibrarySamples: main (July 2026) Language: C/C++ (.cu files) Licenses: BSD-3-Clause (cuda-samples), Apache-2.0 (CUDALibrarySamples)
v13.3 layout note: cuda-samples renamed its top-level
Samples/directory tocpp/(Python samples now live underpython/). C++ samples are grouped undercpp/0_Introduction,cpp/4_CUDA_Libraries,cpp/6_Performance, plus newcpp/9_CUDA_Tile(CUDA Tile C++) and CCCL 3.3 feature samples (cubDeviceFind,cubDeviceSegmentedScan,cubDeviceTransform,libcuxxRandom,libcuxxMdspan) undercpp/4_CUDA_Libraries/.
// Minimal kernel + launch
__global__ void addVectors(float *a, float *b, float *c, int n) {
int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i < n) c[i] = a[i] + b[i];
}
int main() {
int n = 1 << 20;
float *d_a, *d_b, *d_c;
cudaMalloc(&d_a, n * sizeof(float));
cudaMalloc(&d_b, n * sizeof(float));
cudaMalloc(&d_c, n * sizeof(float));
int threads = 256;
int blocks = (n + threads - 1) / threads;
addVectors<<<blocks, threads>>>(d_a, d_b, d_c, n);
cudaDeviceSynchronize();
cudaFree(d_a); cudaFree(d_b); cudaFree(d_c);
return 0;
}
__global__ function executed on GPU by many parallel threads<<<gridDim, blockDim>>> configures parallelismcudaMalloc and freed with cudaFreecudaMallocManaged): Automatically migrates data between CPU and GPU; check support with device_prop.managedMemory| Domain | File | Description |
|---|---|---|
| CUDA Runtime | api-runtime.md | Device mgmt, memory, streams, events, kernel launch |
| cuBLAS | api-cublas.md | Dense linear algebra: GEMM, GEMV, TRSM, grouped batched ops |
| cuFFT | api-cufft.md | 1D/2D/3D FFT and batched transforms |
| cuSPARSE | api-cusparse.md | Sparse matrix ops: SpMM, SpMV, format conversions |
| cuRAND | api-curand.md | Random number generation on GPU |
| cuSolver | api-cusolver.md | Dense/sparse solvers: QR, LU, eigenvalue, SVD |
| Thrust | api-thrust.md | STL-like GPU algorithms: sort, reduce, transform, scan |
| Cooperative Groups | api-cooperative-groups.md | Flexible thread synchronization beyond blocks |
| Workflows | workflows.md | Complete working examples |
See references/workflows.md for complete examples.
Quick reference:
CUDA_CHECK(err) macro patterncudaDeviceSynchronize() or stream synchronize before reading results on hostcudaOccupancyMaxPotentialBlockSize to tune block dimensions; uses uint32_t for array indexing to avoid overflowcudaStreamNonBlocking when creating non-default streams to avoid implicit synchronization with the null streamdevice_prop.managedMemory before using cudaMallocManaged; use cudaMemPrefetchAsync to avoid page faultscuModuleUnload before cuCtxDestroy when using the CUDA Driver API--gpu-architecture=sm_90a for Hopper (H100), sm_80 for Ampere (A100), sm_70 for Volta (V100)cuda::std::tuple, cuda::std::make_tuple, and cuda::std::get (the thrust::tuple variants are replaced)