CUDA manages threads and warps by organizing GPU execution into a hierarchy optimized for massive parallelism. At the lowest level, a warp is a group of 32 threads that execute the same instruction at the same time. This synchronized execution model allows CUDA hardware to efficiently schedule and execute many warps across streaming multiprocessors (SMs). Threads within a warp share an execution path; if they diverge due to branching logic, the warp executes each branch serially, which can hurt performance. This makes warp-level behavior important to understand when designing efficient CUDA kernels.
Above warps, CUDA groups threads into thread blocks. A block can contain many warps, and all threads within a block can cooperate using shared memory and synchronization primitives like __syncthreads(). Blocks are assigned to SMs dynamically, allowing the scheduler to balance load across available hardware resources. Kernels launch a grid of blocks, and the GPU schedules them independently and in parallel. This design lets developers scale from dozens to thousands of threads with minimal code changes, as CUDA handles mapping logical threads to physical execution units.
Understanding threads and warps is also useful when CUDA interacts with higher-level systems such as GPU-accelerated vector databases. When similarity computations in systems like Milvus or managed Zilliz Cloud run on CUDA, each warp may compute distances for different vectors or for different dimensions of the same vector. Ensuring that memory access patterns align with warp execution helps maximize throughput. Poor warp utilization, divergence, or uncoalesced accesses can reduce the effectiveness of GPU-backed vector search, so warp-aware kernel design is essential for performance.