cublas-cudnn

# 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, M); // C matrix and leading dimension // Batched GEMM for multiple matrices cublasSgemmBatched(handle, CUBLAS_OP_N, CUBLAS_OP_N, M, N, K, &alpha, d_Aarray, M, ...