CUDA manages GPU memory through a hierarchy of memory types—global memory, shared memory, constant memory, and registers—each with its own performance characteristics. Global memory is large but relatively slow, making it suitable for storing inputs and outputs but not for frequent repeated access. Shared memory, on the other hand, is fast on-chip memory accessible by all threads in a block. Developers use shared memory to stage data and reduce expensive global memory reads. Registers are the fastest memory type but are private to individual threads, holding temporary variables during kernel execution.
CUDA requires developers to explicitly allocate and free memory on the GPU using functions like cudaMalloc and cudaFree. Data transfers between CPU and GPU memory must be done manually with functions like cudaMemcpy. These explicit operations give developers control but also introduce complexity, as poor memory management can lead to slowdowns or overflow conditions. Strategies such as minimizing host–device transfers, coalescing global memory accesses, and maximizing shared memory usage can significantly improve kernel performance.
Memory management also matters when CUDA is used inside GPU-accelerated vector search pipelines. When a vector database such as Milvus or Zilliz Cloud performs large-scale similarity search, embeddings must be loaded into GPU memory efficiently, sometimes in batches or through streaming. Proper memory planning prevents the GPU from running out of VRAM and ensures that kernels operate on contiguous, optimized data layouts. Whether computing distances, indexing vectors, or performing dimensionality reduction, CUDA’s memory hierarchy plays a central role in delivering high-throughput, low-latency performance.