---
id: wiki-2026-0508-gpu-programming-with-cuda
title: GPU Programming with CUDA
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [CUDA, GPU programming, kernel, Triton, cuBLAS, cuDNN, ROCm, HIP]
duplicate_of: none
source_trust_level: A
confidence_score: 0.95
verification_status: applied
tags: [cuda, gpu-programming, kernel, parallel, triton, hip]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: CUDA / C++ / Triton / Python
  framework: CUDA Toolkit / cuBLAS / Triton / Numba
---

# GPU Programming with CUDA

## 매 한 줄
> **"매 NVIDIA GPU 의 의 의 parallel kernel 의 write"**. 매 kernel + grid + block + warp + memory hierarchy. 매 modern: Triton (Python), cuBLAS / cuDNN (libraries), CUDA Graphs (efficient launch). 매 alternative: HIP (AMD), Metal (Apple), WGSL (cross-platform).

## 매 핵심

### 매 hierarchy
- **Grid** = blocks.
- **Block** = threads (typically 128-256).
- **Warp** = 32 threads (SIMT).
- **Thread** = unit of execution.

### 매 memory
- **Global** (HBM): 매 large, slow.
- **L2 cache**.
- **Shared** (SMEM): 매 fast on-chip.
- **Register**: 매 fastest.
- **Texture / constant**.

### 매 응용
1. **ML kernels** (matmul, attention).
2. **HPC** (FFT, PDE).
3. **Graphics** (raster + RT).
4. **Image / video processing**.
5. **Scientific** (CFD, MD).

### 매 ecosystem
- **CUDA Toolkit**: nvcc, cuBLAS, cuDNN, NCCL.
- **Triton**: 매 OpenAI, Python kernel.
- **CUTLASS**: 매 templated.
- **Thrust**: 매 STL-like.
- **Numba**: 매 Python @cuda.jit.
- **HIP / ROCm**: AMD.

## 💻 패턴

### Vector add (basic)
```cuda
__global__ void vec_add(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() {
    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));
    cudaMemcpy(d_a, h_a, N * sizeof(float), cudaMemcpyHostToDevice);
    cudaMemcpy(d_b, h_b, N * sizeof(float), cudaMemcpyHostToDevice);
    
    int block = 256, grid = (N + block - 1) / block;
    vec_add<<<grid, block>>>(d_a, d_b, d_c, N);
    
    cudaMemcpy(h_c, d_c, N * sizeof(float), cudaMemcpyDeviceToHost);
    cudaFree(d_a); cudaFree(d_b); cudaFree(d_c);
}
```

### Matrix multiply (shared memory tiling)
```cuda
__global__ void matmul_shared(float* A, float* B, float* C, int N) {
    __shared__ float As[TILE][TILE];
    __shared__ float Bs[TILE][TILE];
    
    int tx = threadIdx.x, ty = threadIdx.y;
    int row = blockIdx.y * TILE + ty;
    int col = blockIdx.x * TILE + tx;
    
    float sum = 0;
    for (int t = 0; t < N / TILE; ++t) {
        As[ty][tx] = A[row * N + t * TILE + tx];
        Bs[ty][tx] = B[(t * TILE + ty) * N + col];
        __syncthreads();
        for (int k = 0; k < TILE; ++k)
            sum += As[ty][k] * Bs[k][tx];
        __syncthreads();
    }
    C[row * N + col] = sum;
}
```

### Reduction (parallel sum)
```cuda
__global__ void reduce_sum(float* in, float* out, int N) {
    __shared__ float sdata[256];
    int tid = threadIdx.x;
    int i = blockIdx.x * blockDim.x + tid;
    sdata[tid] = (i < N) ? in[i] : 0;
    __syncthreads();
    
    for (int s = blockDim.x / 2; s > 0; s >>= 1) {
        if (tid < s) sdata[tid] += sdata[tid + s];
        __syncthreads();
    }
    if (tid == 0) atomicAdd(out, sdata[0]);
}
```

### Triton (Python kernel)
```python
import triton
import triton.language as tl

@triton.jit
def matmul_kernel(a_ptr, b_ptr, c_ptr, M, N, K, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)
    
    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        offs_k = k + tl.arange(0, BLOCK_K)
        a = tl.load(a_ptr + offs_m[:, None] * K + offs_k[None, :])
        b = tl.load(b_ptr + offs_k[:, None] * N + offs_n[None, :])
        acc += tl.dot(a, b)
    
    tl.store(c_ptr + offs_m[:, None] * N + offs_n[None, :], acc)
```

### Numba (Python @cuda.jit)
```python
from numba import cuda
import numpy as np

@cuda.jit
def add_kernel(a, b, out):
    i = cuda.grid(1)
    if i < a.size: out[i] = a[i] + b[i]

a = np.arange(1_000_000, dtype=np.float32)
b = np.arange(1_000_000, dtype=np.float32)
d_a = cuda.to_device(a); d_b = cuda.to_device(b)
d_out = cuda.device_array_like(a)
add_kernel[(a.size + 255) // 256, 256](d_a, d_b, d_out)
out = d_out.copy_to_host()
```

### cuBLAS (use library)
```cuda
#include <cublas_v2.h>
cublasHandle_t handle; cublasCreate(&handle);
const float alpha = 1, beta = 0;
cublasSgemm(handle, CUBLAS_OP_N, CUBLAS_OP_N, M, N, K, &alpha, A, M, B, K, &beta, C, M);
// 매 vs hand-written 의 1.5-3x faster
```

### CUDA Graphs (efficient launch)
```cuda
cudaGraph_t graph;
cudaGraphCreate(&graph, 0);
// 매 build graph (sequence of kernels)
cudaStream_t stream;
cudaStreamBeginCapture(stream, cudaStreamCaptureModeGlobal);
kernel1<<<...>>>(...);
kernel2<<<...>>>(...);
cudaStreamEndCapture(stream, &graph);

cudaGraphExec_t exec;
cudaGraphInstantiate(&exec, graph, nullptr, nullptr, 0);
// 매 매 frame 의 의 launch with single call
cudaGraphLaunch(exec, stream);
```

### Stream (async)
```cuda
cudaStream_t stream;
cudaStreamCreate(&stream);
cudaMemcpyAsync(d_a, h_a, size, cudaMemcpyHostToDevice, stream);
kernel<<<grid, block, 0, stream>>>(d_a, ...);
cudaMemcpyAsync(h_b, d_b, size, cudaMemcpyDeviceToHost, stream);
cudaStreamSynchronize(stream);
```

### Profiling (Nsight Compute)
```bash
ncu --set full -o profile ./my_app
ncu-ui profile.ncu-rep
# 매 occupancy, instruction throughput, memory bandwidth
```

### HIP (AMD equivalent)
```cpp
__global__ void hip_add(float* a, float* b, float* c, int N) {
    int i = hipBlockIdx_x * hipBlockDim_x + hipThreadIdx_x;
    if (i < N) c[i] = a[i] + b[i];
}
hipLaunchKernelGGL(hip_add, dim3((N+255)/256), dim3(256), 0, 0, a, b, c, N);
```

### Custom op in PyTorch
```cpp
// 매 register
TORCH_LIBRARY(my_ops, m) {
    m.def("vec_add(Tensor a, Tensor b) -> Tensor");
}

torch::Tensor vec_add(torch::Tensor a, torch::Tensor b) {
    auto c = torch::empty_like(a);
    int N = a.numel();
    vec_add_kernel<<<(N+255)/256, 256>>>(a.data_ptr<float>(), b.data_ptr<float>(), c.data_ptr<float>(), N);
    return c;
}
```

### Occupancy calculator
```cuda
int max_blocks_per_sm = 0;
cudaOccupancyMaxActiveBlocksPerMultiprocessor(&max_blocks_per_sm, kernel, threads_per_block, 0);
// 매 50%+ occupancy ideal
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| New kernel | Triton (Python easy) |
| Production matmul | cuBLAS |
| ML attention | Flash Attention |
| Hand-tuned | CUTLASS / pure CUDA |
| Graph workload | CUDA Graphs |
| AMD | HIP / ROCm |
| Cross-platform | WGSL / Metal / Vulkan |

**기본값**: 매 use library (cuBLAS/cuDNN) when possible + 매 Triton for Python research + 매 CUDA Graphs for repeated launches + 매 profile with Nsight.

## 🔗 Graph
- 부모: [[GPU]]
- 변형: [[CUDA]] · [[Triton]] · [[HIP]]
- 응용: [[cuBLAS]] · [[Flash Attention]] · [[Compute-Shader]]
- Adjacent: [[GPU|GPU-Architecture]]

## 🤖 LLM 활용
**언제**: 매 ML kernel research. 매 HPC. 매 custom op.
**언제 X**: 매 framework op sufficient.

## ❌ 안티패턴
- **Hand-write before profile**: 매 wrong opt.
- **Ignore occupancy**: 매 underutilize.
- **No SMEM tiling**: 매 memory-bound.
- **Sync host every kernel**: 매 latency.
- **No CUDA Graphs for repeated**: 매 launch overhead.

## 🧪 검증 / 중복
- Verified (CUDA programming guide, Triton docs, NVIDIA HPC).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — kernels + 매 vec / matmul / Triton / Numba / Graphs / cuBLAS code |
