CUDA enables parallel processing on NVIDIA GPUs by organizing computation into thousands of lightweight threads that execute simultaneously on the GPU’s streaming multiprocessors. Each GPU contains many cores optimized for performing simple arithmetic operations in parallel. CUDA exposes these cores through a hierarchical execution model: threads are grouped into blocks, and blocks are grouped into a grid. When a kernel is launched, CUDA schedules blocks across the GPU hardware automatically, allowing developers to define large-scale parallel tasks without managing individual core assignments.
Threads within a block can share data through fast on-chip shared memory, which is one of the major strengths of CUDA’s architecture. Shared memory allows threads to cooperate efficiently, reducing expensive global memory accesses and increasing throughput. This makes CUDA particularly effective for workloads such as image filtering, matrix multiplication, and vector distance computations—each of which can be decomposed into many small independent tasks. The hardware handles scheduling at warp granularity (groups of 32 threads), ensuring well-defined execution behavior for highly parallel kernels.
CUDA’s parallel execution model plays a natural role in accelerating vector operations used by systems like Milvus and Zilliz Cloud. Vector search often involves computing similarity metrics across millions of embeddings, which maps perfectly to CUDA’s thread-based structure. Each thread can compute the distance for one vector or even one dimension of a vector, while blocks handle batches of vectors at once. This high-throughput parallelism allows GPU-backed vector search systems to return results far faster than CPU-only methods.