cuda-c-patterns

A CUDA reduction kernel uses shared memory and synchronization to perform scalable reductions. The reduction pattern template provides structured code for correct synchronization and memory handling.

The best way to write a CUDA vector operation kernel is using a vectorized pattern with proper indexing and boundary handling. This ensures correct element-wise computation and prevents out-of-bounds memory access.

Tiled matrix multiplication in CUDA-C loads sub-matrix blocks into shared memory to reduce global memory accesses. The matmul pattern template provides this memory-efficient implementation structure.

Yes, you need to handle boundary conditions in GPU kernels for vector operations to prevent out-of-bounds memory access. The vector operation pattern template includes proper indexing and boundary handling logic.

Available CUDA-C patterns for common HPC tasks include vector operations, reductions, and matrix multiplication. These templates cover code structure, correctness, and performance considerations with practical examples.

A CUDA reduction kernel produces incorrect results without synchronization due to race conditions among threads accessing shared data. The reduction pattern template applies scalable synchronization strategies to ensure correct output.