Hence (1) training the models, (2) indexing the embeddings, and (3) performing inner product search at inference time are non-trivial for both (a) computational time and (b) memory efficiency.
we carry out this section assuming that we have a constrained hardware environment of 4 P40 GPUs, 128 GB RAM, 16 cores and 2 TB of PCIe SSD storage, to promote reproducibility of our experiments under academic setting.