cublas-cudnn
Expert integration with NVIDIA GPU-accelerated math libraries. Configure cuBLAS tensor core operations, generate cuBLAS GEMM calls, integrate cuDNN layers, handle algorithm selection, and support mixed-precision operations.
npx skills add majiayu000/claude-skill-registry --skill cublas-cudnn --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# cublas-cudnn You are **cublas-cudnn** - a specialized skill for NVIDIA GPU-accelerated math library integration. This skill provides expert capabilities for using cuBLAS, cuDNN, and related libraries. ## Overview This skill enables AI-powered GPU library operations including: - Configure cuBLAS tensor core operations - Generate cuBLAS GEMM calls with optimal parameters - Integrate cuDNN convolution and normalization layers - Handle cuBLAS/cuDNN algorithm selection - Configure workspace memory requirements - Benchmark library operations vs custom kernels - Support mixed-precision operations (FP16, TF32, INT8) - Integrate with cuSPARSE for sparse operations ## Prerequisites - CUDA Toolkit 11.0+ - cuBLAS library - cuDNN 8.0+ - cuSPARSE (optional) ## Capabilities ### 1. cuBLAS GEMM Operations Matrix multiplication with cuBLAS: ```c #include <cublas_v2.h> // Initialize cuBLAS cublasHandle_t handle; cublasCreate(&handle); // Standard SGEMM: C = alpha * A * B + beta * C float alpha = 1.0f, beta = 0.0f; cublasSgemm(handle, CUBLAS_OP_N, CUBLAS_OP_N, // No transpose M, N, K, // Dimensions &alpha, d_A, M, // A matrix and leading dimension d_B, K, // B matrix and leading dimension &beta, d_C
- Overview
- Prerequisites
- Capabilities
- 1. cuBLAS GEMM Operations
- 2. Tensor Core Operations
- 3. cuDNN Convolution
- 4. cuDNN Batch Normalization
- 5. Algorithm Selection and Benchmarking
- 6. Workspace Memory Management
- 7. Mixed Precision Support
- 8. cuSPARSE Integration
- Process Integration
- Output Format
- Dependencies
What does the cublas-cudnn skill do?
Expert integration with NVIDIA GPU-accelerated math libraries. Configure cuBLAS tensor core operations, generate cuBLAS GEMM calls, integrate cuDNN layers, handle algorithm selection, and support mixed-precision operations.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill cublas-cudnn --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.