Babysitter 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.

install

source · Clone the upstream repo

git clone https://github.com/a5c-ai/babysitter

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/a5c-ai/babysitter "$T" && mkdir -p ~/.claude/skills && cp -r "$T/library/specializations/gpu-programming/skills/cublas-cudnn" ~/.claude/skills/a5c-ai-babysitter-cublas-cudnn && rm -rf "$T"

manifest: library/specializations/gpu-programming/skills/cublas-cudnn/SKILL.md

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:

#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,
    d_Barray, K,
    &beta,
    d_Carray, M,
    batchCount);

// Strided batched GEMM (contiguous memory)
cublasSgemmStridedBatched(handle,
    CUBLAS_OP_N, CUBLAS_OP_N,
    M, N, K,
    &alpha,
    d_A, M, strideA,
    d_B, K, strideB,
    &beta,
    d_C, M, strideC,
    batchCount);

2. Tensor Core Operations

Enable tensor cores for maximum performance:

// Enable tensor cores (requires Volta+)
cublasSetMathMode(handle, CUBLAS_TENSOR_OP_MATH);

// For Ampere+, use TF32
cublasSetMathMode(handle, CUBLAS_TF32_TENSOR_OP_MATH);

// Half precision GEMM with tensor cores
cublasGemmEx(handle,
    CUBLAS_OP_N, CUBLAS_OP_N,
    M, N, K,
    &alpha,
    d_A, CUDA_R_16F, M,      // FP16 input
    d_B, CUDA_R_16F, K,      // FP16 input
    &beta,
    d_C, CUDA_R_16F, M,      // FP16 output
    CUDA_R_16F,              // Compute type
    CUBLAS_GEMM_DEFAULT_TENSOR_OP);

// Mixed precision: FP16 inputs, FP32 accumulate
cublasGemmEx(handle,
    CUBLAS_OP_N, CUBLAS_OP_N,
    M, N, K,
    &alpha,
    d_A, CUDA_R_16F, M,
    d_B, CUDA_R_16F, K,
    &beta,
    d_C, CUDA_R_32F, M,      // FP32 output
    CUDA_R_32F,              // FP32 compute
    CUBLAS_GEMM_DEFAULT_TENSOR_OP);

3. cuDNN Convolution

Deep learning convolutions:

#include <cudnn.h>

cudnnHandle_t cudnn;
cudnnCreate(&cudnn);

// Create tensor descriptors
cudnnTensorDescriptor_t inputDesc, outputDesc;
cudnnCreateTensorDescriptor(&inputDesc);
cudnnCreateTensorDescriptor(&outputDesc);

cudnnSetTensor4dDescriptor(inputDesc, CUDNN_TENSOR_NCHW,
    CUDNN_DATA_FLOAT, N, C, H, W);

// Create filter descriptor
cudnnFilterDescriptor_t filterDesc;
cudnnCreateFilterDescriptor(&filterDesc);
cudnnSetFilter4dDescriptor(filterDesc, CUDNN_DATA_FLOAT,
    CUDNN_TENSOR_NCHW, K, C, R, S);

// Create convolution descriptor
cudnnConvolutionDescriptor_t convDesc;
cudnnCreateConvolutionDescriptor(&convDesc);
cudnnSetConvolution2dDescriptor(convDesc,
    pad_h, pad_w,      // Padding
    stride_h, stride_w, // Stride
    1, 1,               // Dilation
    CUDNN_CROSS_CORRELATION,
    CUDNN_DATA_FLOAT);

// Enable tensor cores
cudnnSetConvolutionMathType(convDesc, CUDNN_TENSOR_OP_MATH);

// Find best algorithm
cudnnConvolutionFwdAlgoPerf_t perfResults[8];
int returnedAlgoCount;
cudnnFindConvolutionForwardAlgorithm(cudnn,
    inputDesc, filterDesc, convDesc, outputDesc,
    8, &returnedAlgoCount, perfResults);

cudnnConvolutionFwdAlgo_t algo = perfResults[0].algo;

// Get workspace size
size_t workspaceSize;
cudnnGetConvolutionForwardWorkspaceSize(cudnn,
    inputDesc, filterDesc, convDesc, outputDesc,
    algo, &workspaceSize);

void* workspace;
cudaMalloc(&workspace, workspaceSize);

// Execute convolution
float alpha = 1.0f, beta = 0.0f;
cudnnConvolutionForward(cudnn,
    &alpha,
    inputDesc, d_input,
    filterDesc, d_filter,
    convDesc, algo, workspace, workspaceSize,
    &beta,
    outputDesc, d_output);

4. cuDNN Batch Normalization

cudnnTensorDescriptor_t bnScaleBiasMeanVarDesc;
cudnnCreateTensorDescriptor(&bnScaleBiasMeanVarDesc);
cudnnDeriveBNTensorDescriptor(bnScaleBiasMeanVarDesc, inputDesc,
    CUDNN_BATCHNORM_SPATIAL);

// Forward training
cudnnBatchNormalizationForwardTraining(cudnn,
    CUDNN_BATCHNORM_SPATIAL,
    &alpha, &beta,
    inputDesc, d_input,
    outputDesc, d_output,
    bnScaleBiasMeanVarDesc,
    d_scale, d_bias,
    0.1,  // Exponential average factor
    d_runningMean, d_runningVariance,
    1e-5, // Epsilon
    d_savedMean, d_savedInvVariance);

// Forward inference
cudnnBatchNormalizationForwardInference(cudnn,
    CUDNN_BATCHNORM_SPATIAL,
    &alpha, &beta,
    inputDesc, d_input,
    outputDesc, d_output,
    bnScaleBiasMeanVarDesc,
    d_scale, d_bias,
    d_runningMean, d_runningVariance,
    1e-5);

5. Algorithm Selection and Benchmarking

// Benchmark all algorithms
cudnnConvolutionFwdAlgoPerf_t perfResults[CUDNN_CONVOLUTION_FWD_ALGO_COUNT];
int returnedCount;

cudnnFindConvolutionForwardAlgorithmEx(cudnn,
    inputDesc, d_input,
    filterDesc, d_filter,
    convDesc,
    outputDesc, d_output,
    CUDNN_CONVOLUTION_FWD_ALGO_COUNT,
    &returnedCount,
    perfResults,
    workspace, workspaceSize);

// Print benchmark results
for (int i = 0; i < returnedCount; i++) {
    printf("Algorithm %d: %.3f ms, workspace: %zu bytes\n",
        perfResults[i].algo,
        perfResults[i].time,
        perfResults[i].memory);
}

// Select algorithm by heuristics
cudnnConvolutionFwdAlgo_t algo;
cudnnGetConvolutionForwardAlgorithm_v7(cudnn,
    inputDesc, filterDesc, convDesc, outputDesc,
    8, &returnedCount, perfResults);

6. Workspace Memory Management

// Query workspace for all operations
size_t convWorkspace, bnWorkspace, poolWorkspace;

cudnnGetConvolutionForwardWorkspaceSize(cudnn, ...);
cudnnGetBatchNormalizationForwardTrainingExWorkspaceSize(cudnn, ...);

// Allocate maximum needed
size_t maxWorkspace = max(convWorkspace, max(bnWorkspace, poolWorkspace));
void* workspace;
cudaMalloc(&workspace, maxWorkspace);

// Reuse workspace across operations

7. Mixed Precision Support

// FP16 convolution
cudnnSetTensor4dDescriptor(inputDesc, CUDNN_TENSOR_NHWC,
    CUDNN_DATA_HALF, N, C, H, W);
cudnnSetFilter4dDescriptor(filterDesc, CUDNN_DATA_HALF,
    CUDNN_TENSOR_NHWC, K, C, R, S);

// INT8 convolution for inference
cudnnSetTensor4dDescriptor(inputDesc, CUDNN_TENSOR_NHWC,
    CUDNN_DATA_INT8, N, C, H, W);
cudnnSetConvolution2dDescriptor(convDesc,
    pad_h, pad_w, stride_h, stride_w, 1, 1,
    CUDNN_CROSS_CORRELATION,
    CUDNN_DATA_INT32);  // INT32 accumulation

8. cuSPARSE Integration

#include <cusparse.h>

cusparseHandle_t sparse;
cusparseCreate(&sparse);

// Create sparse matrix in CSR format
cusparseSpMatDescr_t matA;
cusparseCreateCsr(&matA, M, N, nnz,
    d_rowPtr, d_colIdx, d_values,
    CUSPARSE_INDEX_32I, CUSPARSE_INDEX_32I,
    CUSPARSE_INDEX_BASE_ZERO, CUDA_R_32F);

// Create dense vectors
cusparseDnVecDescr_t vecX, vecY;
cusparseCreateDnVec(&vecX, N, d_x, CUDA_R_32F);
cusparseCreateDnVec(&vecY, M, d_y, CUDA_R_32F);

// SpMV: y = alpha * A * x + beta * y
size_t bufferSize;
cusparseSpMV_bufferSize(sparse, CUSPARSE_OPERATION_NON_TRANSPOSE,
    &alpha, matA, vecX, &beta, vecY, CUDA_R_32F,
    CUSPARSE_SPMV_ALG_DEFAULT, &bufferSize);

void* buffer;
cudaMalloc(&buffer, bufferSize);

cusparseSpMV(sparse, CUSPARSE_OPERATION_NON_TRANSPOSE,
    &alpha, matA, vecX, &beta, vecY, CUDA_R_32F,
    CUSPARSE_SPMV_ALG_DEFAULT, buffer);

Process Integration

This skill integrates with the following processes:

```
tensor-core-programming.js
```
- Tensor core workflows
```
ml-inference-optimization.js
```
- ML inference
```
custom-cuda-operator-development.js
```
- Custom operators

Output Format

{
  "operation": "gemm-benchmark",
  "library": "cuBLAS",
  "configuration": {
    "M": 4096, "N": 4096, "K": 4096,
    "datatype": "FP16",
    "math_mode": "TENSOR_OP_MATH"
  },
  "performance": {
    "time_ms": 0.85,
    "tflops": 16.2,
    "efficiency_pct": 81.0
  },
  "recommendations": [
    "Use CUBLAS_GEMM_DEFAULT_TENSOR_OP for tensor core path",
    "Ensure dimensions are multiples of 8 for optimal tensor core usage"
  ]
}

Dependencies

CUDA Toolkit 11.0+
cuBLAS
cuDNN 8.0+
cuSPARSE (optional)

Constraints

Tensor cores require specific data types and alignments
Algorithm selection should be cached per configuration
Workspace memory must be allocated before execution
Mixed precision may require loss scaling