The script [`bench_gpu_1bn.py`](bench_gpu_1bn.py) runs multi-gpu searches on the two 1-billion vector datasets we considered. It is more complex than the previous scripts, because it supports many search options and decomposes the dataset build process in Python to exploit the best possible CPU/GPU parallelism and GPU distribution.
Even on multiple GPUs, building the 1B datasets can last several hours. It is often a good idea to validate that everything is working fine on smaller datasets like SIFT1M, SIFT2M, etc.
The search results on SIFT1B in the "GPU paper" can be obtained with
0/10000 (0.005 s) probe=256: 0.736 s 1-R@1: 0.4933 1-R@10: 0.8912
```
Here we are a bit tight on memory so we disable precomputed tables (`-noptables`) and restrict the amount of temporary memory. The `-altadd` option avoids GPU memory overflows during add.
### knn-graph on Deep1B
The same script generates the KNN-graph on Deep1B.
### knn-graph on Deep1B
The same script generates the KNN-graph on Deep1B. Note that the inverted file from above will not be re-used because the training sets are different. For the knngraph, the script will first do a pass over the whole dataset to compute the ground-truth knn for a subset of 10k nodes, for evaluation.
