The recommended way to optimize CUDA kernel memory access is to maximize coalesced global memory accesses, leverage shared memory effectively, and minimize unnecessary CPU–GPU transfers. Memory coalescing means arranging data so that consecutive threads access consecutive memory locations. When this happens, the GPU can combine many memory requests into a single large transaction, dramatically increasing throughput. If data is misaligned or poorly structured, each thread may trigger separate memory requests, reducing performance significantly. Structuring arrays as “structure of arrays” (SoA) instead of “array of structures” (AoS) often improves coalescing.
Shared memory is another key optimization. Because shared memory sits on-chip, it is far faster than global memory. Kernels often load data into shared memory in tiles, process it cooperatively, and write results back to global memory. This technique is widely used in matrix multiplication, convolution, and distance-calculation kernels. However, shared memory must be used carefully, as improper access patterns can lead to bank conflicts (discussed in the next question). Developers should also minimize redundant loads by letting multiple threads reuse shared data whenever possible.
These memory optimization patterns directly affect GPU-accelerated vector search performance. When computing distances for embeddings stored in a system such as Milvus or Zilliz Cloud, coalesced memory access ensures that vector components load efficiently, reducing latency. Shared memory tiling can also help cache portions of vectors during inner-product or L2 calculations. By structuring CUDA kernels with memory efficiency in mind, developers can significantly increase throughput for high-dimensional vector workloads.