CUDA handles shared memory bank conflicts by dividing shared memory into multiple banks that can each serve one read or write per cycle. When multiple threads in a warp access different addresses that fall within the same bank, conflicts occur, causing serialized access and reducing performance. The hardware resolves these conflicts by executing each conflicting access one at a time. While this ensures correctness, it increases latency and can dramatically reduce throughput for kernels heavily dependent on shared memory.
Developers can avoid bank conflicts by aligning or padding shared memory arrays so that concurrent thread accesses map to different banks. For example, if each thread loads an element from a 2D shared-memory tile, adding a small padding value (such as +1 to the second dimension) can prevent multiple threads from hitting the same bank. Access patterns that use strided indexing or irregular layouts are more prone to conflicts, so understanding how threads map to banks is essential. Tools like Nsight Compute can reveal whether bank conflicts are harming performance.
These principles are especially important when shared memory is used to accelerate vector workloads. If a kernel performing similarity search uses shared memory to store partial vector chunks, avoiding bank conflicts ensures that each warp can process embeddings without unnecessary serialization. This affects systems like Milvus or Zilliz Cloud, where throughput matters for large-scale similarity search. Optimizing shared memory access patterns helps achieve consistent, low-latency vector computation, which is essential when handling millions of embeddings.