Multi-GPU CAGRA#
Multi-GPU CAGRA extends the graph-based CAGRA algorithm to work across multiple GPUs, providing improved scalability and performance for large-scale vector search. It supports both replicated and sharded distribution modes.
Note
IMPORTANT: Multi-GPU CAGRA requires all data (datasets, queries, output arrays) to be in host memory (CPU).
If using CuPy/device arrays, transfer to host with array.get() or cp.asnumpy(array) before use.