This guide walks through the process of developing production-ready CUDA kernels, starting from scratch and progressing to scaling for real-world workloads. It covers essential concepts, optimization techniques, and best practices for high-performance GPU programming.
Getting Started
Before writing kernels, understand your GPU's architecture. Key metrics include the number of streaming multiprocessors (SMs), memory bandwidth, and shared memory size. Use cudaGetDeviceProperties() to query these.
Your First Kernel
A simple vector addition kernel:
global__ void vecAdd(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];
}
Launch it with appropriate grid and block dimensions.
Memory Management
- Global memory: High latency, optimize with coalesced access.
- Shared memory: Fast, on-chip; use for data reuse.
- Registers: Fastest, but limited.
Minimize global memory transactions by aligning data and using vectorized loads.
Optimization Techniques
- Memory coalescing: Ensure adjacent threads access adjacent memory.
- Occupancy: Maximize threads per SM to hide latency. Balance register and shared memory usage.
- Shared memory tiling: For matrix multiplication, load tiles into shared memory.
- Reducing divergence: Avoid warp-divergent branches.
Profiling and Debugging
Use NVIDIA Nsight Compute and Nsight Systems to profile. Key metrics: occupancy, memory bandwidth utilization, and instruction throughput.
Scaling to Multi-GPU
For multiple GPUs, use CUDA streams for concurrency and peer-to-peer access for direct transfers. Distribute data across GPUs and synchronize carefully.
Production Considerations
- Error handling: Check for CUDA errors after each API call.
- Fault tolerance: For long-running jobs, implement checkpointing.
- Portability: Target multiple architectures with
-archflags.
Case Study: Optimizing a Reduction Kernel
Start with a naive reduction and apply optimizations: warp-level primitives, unrolling, and shared memory. Measure speedup.
Conclusion
Building production-ready CUDA kernels requires understanding hardware, iterative optimization, and rigorous testing. Use profiling tools and follow best practices to achieve peak performance.