Choosing CUDA grid and block sizes involves balancing kernel parallelism, GPU occupancy, and memory access efficiency. A block size that is too small underutilizes the GPU, while a block size that is too large may exceed hardware limits on registers or shared memory per block. The most common rule of thumb is to choose block sizes that are multiples of the warp size (32 threads) to avoid partially filled warps. Typical values include 128, 256, or 512 threads per block, depending on the kernel’s complexity and register usage. The grid size is then chosen to cover all data elements the kernel must process.
GPU occupancy—the percentage of hardware that is actively executing threads—should also guide grid/block choices. If a block uses too many registers or too much shared memory, fewer blocks will fit on each streaming multiprocessor, reducing occupancy. Nsight Compute can reveal whether occupancy is high enough or if resource limits are throttling performance. Some kernels benefit from high occupancy, while others are limited by memory bandwidth rather than available warps, so experimentation is essential.
In workloads involving vector search, the grid and block configuration must align with the structure of the similarity computation. For example, when computing distances between query embeddings and millions of stored vectors in Milvus or Zilliz Cloud, one block might process one vector, or one warp might compute one segment of a vector. Choosing the right mapping ensures efficient memory coalescing and minimizes idle threads. Ultimately, optimal grid and block dimensions depend on the kernel’s algorithmic structure, hardware constraints, and memory access patterns, all of which should be tuned using profiling tools.