CUDA threads and blocks work together by organizing GPU computation into a hierarchy that maps well to the hardware’s parallel architecture. A thread is the smallest execution unit, responsible for performing a single instance of a computation, such as computing one element of a matrix or one distance calculation. Threads are grouped into blocks, which share fast on-chip memory called shared memory. This allows threads within the same block to cooperate by sharing intermediate results, synchronizing execution, and improving memory access efficiency.
Each block contains a developer-specified number of threads, and the GPU schedules these blocks across its streaming multiprocessors (SMs). While threads within a block can communicate, blocks themselves are independent and may run in any order, meaning workloads must be block-independent. This structure is powerful because it lets developers scale their kernels simply by adjusting grid and block dimensions. For example, a kernel computing distances between millions of vectors might launch thousands of blocks, each containing hundreds of threads, enabling massive parallelism.
This thread–block model also complements GPU-accelerated vector search operations. When running similarity calculations inside systems like Milvus or Zilliz Cloud, CUDA kernels may assign one thread per vector dimension or one block per vector batch, depending on the algorithm. Shared memory can be used to cache partial results during inner-product or L2 distance calculations, reducing expensive global memory reads. By structuring computations around threads and blocks, CUDA enables efficient parallel execution that significantly boosts performance in large-scale numerical workloads, including vector search, indexing, and machine learning pipelines.